1. 智慧城市数智化公共治理发展概述
1.1 核心范畴与融合逻辑
智慧城市的核心理念在于运用先进技术提升城市运行效率、优化公共服务、改善居民生活品质1。在这一语境下，数据技术、人工智能（AI）与公共治理的融合构成了数智化治理的基石。
数据技术在智慧城市中扮演着感知和联结的角色。通过物联网（IoT）设备、传感器网络、移动通信以及各类城市信息系统，海量多源数据被实时采集、传输和存储，涵盖交通、环境、能源、公共安全、政务服务等多个领域23。这些数据是构建智慧城市运行态势感知和智能分析的基础。
人工智能（AI）是驱动智慧城市数智化治理的“大脑”。它利用机器学习、深度学习、自然语言处理等技术，对海量城市数据进行分析、挖掘和预测，从而实现智能决策和自动化管理1。AI技术能够识别复杂模式、预测未来趋势，为城市交通优化、公共安全预警、资源调度、甚至疫情应对等提供智能支持145。AI的应用不仅提升了传统公共服务的智能化水平，也催生了全新的服务模式。
公共治理在智慧城市数智化转型中，其核心范畴从传统的行政管理延伸至强调效率、透明、协同和以人为本的服务型治理6。数智化公共治理是指政府及其他公共部门运用数据技术和人工智能，以更高效、公正和响应迅速的方式，管理城市事务、提供公共服务并促进社会福祉的实践78。其目标在于通过技术赋能，提升政府的决策科学性、服务精准性、管理精细化和风险防控能力。
三者协同赋能城市公共服务和治理效率提升的底层逻辑在于形成一个闭环反馈系统：
- 数据驱动感知与洞察： 数据技术持续汇集城市运行的各项信息，形成全面的数字画像。
- AI智能分析与决策： AI对这些数据进行深度分析，从中提取有价值的知识，识别问题、预测趋势，并生成优化方案或辅助决策建议。
- 治理实践优化与反馈： 公共治理部门依据AI的分析结果和决策支持，调整政策、优化服务流程、实施精准管理。这些实践的效果又会通过新的数据被系统感知和收集，形成新的数据输入，驱动新一轮的AI分析和治理优化，从而实现持续迭代和进步。
这种融合逻辑使得智慧城市能够从被动响应转向主动预防，从粗放管理转向精细治理，最终实现资源利用效率的最大化、公共服务质量的显著提升以及城市可持续发展目标的达成1910。
1.2 发展演进与当前阶段
全球范围内数智技术融入城市公共治理的发展历程是一个动态且不断加速的过程。早期智慧城市的理念萌芽于20世纪90年代末和21世纪初，最初侧重于信息通信技术（ICT）在城市基础设施中的应用，例如电子政务和数字化服务。这一阶段的特点是技术驱动，主要目标是提升行政效率和信息共享。
进入21世纪第二个十年，随着物联网（IoT）、大数据和云计算技术的成熟，智慧城市的内涵被极大地丰富。城市开始通过部署大量传感器和连接设备来收集实时数据，并利用大数据分析来优化交通、能源和环境管理等。例如，通过交通传感器数据进行交通流分析和信号灯优化，以缓解拥堵。这标志着智慧城市从“数字化”向“智能化”的初步转变。在这一时期，许多研究将智慧城市定义为利用数字技术、通信技术和数据分析来创建高效、有效的服务环境，以改善城市生活质量并促进可持续发展11。
自2020年以来，全球智慧城市治理进入了一个以人工智能（AI）深度融合为特征的新阶段。 AIoT（人工智能物联网）的兴起，使得海量城市数据能够被更智能地感知、处理和分析，为环境治理、城市规划和应急响应等提供了前所未有的能力12。城市数字孪生（City Digital Twin）技术也在此阶段快速发展，它通过构建城市物理实体的虚拟副本，实现对城市运行状态的实时监测、模拟和预测，为城市规划和治理决策提供强大的支持131415。这一阶段的重点是实现数据驱动的决策、预测性治理以及为市民提供个性化和可访问的服务11。
我国智慧城市治理在2020年以来展现出显著的技术渗透水平、明确的政策导向和独特的整体发展阶段。
从技术渗透水平来看，我国在智慧城市基础设施建设方面投入巨大，5G网络、物联网设备以及城市级大数据平台已广泛部署。AI技术在城市治理中的应用日益深入，例如在交通领域，AI驱动的信号灯控制系统能够根据实时车流量动态调整配时；在城市安全领域，智能视频分析系统用于快速识别异常事件并预警。数字孪生城市作为城市信息模型（CIM）的高级应用，正成为我国智慧城市建设的重要方向，旨在通过多维数据整合实现城市的全要素数字化和智能化管理15。
在政策导向方面，中国政府对智慧城市建设给予了高度重视和政策支持，强调以人为本、绿色发展和数字经济赋能。与美国城市侧重参与式治理和基础设施建设不同，中国智慧城市建设呈现出政府主导的层级式管理模式，地方政府在培育智能科技产业、环境保护和智能基础设施建设中扮演主导角色，并获得中央政府的政策和资金支持16。这种自上而下的推动机制，使得大规模智慧城市项目的快速落地成为可能。政策鼓励数据共享、开放平台建设以及政企合作，旨在构建一个协同高效的城市治理生态系统。
整体发展阶段上，我国智慧城市已从早期的概念验证和试点阶段，迈向全面推广和深化应用阶段。虽然不同城市间发展水平存在差异，一些新一线城市在智慧基础设施、数字经济和智慧治理方面仍面临挑战，但总体而言，智慧城市已成为推动城市可持续发展和提升公共服务水平的关键战略1718。中国智慧城市的发展正在从强调技术本身转向更加关注技术如何服务于市民福祉和城市的可持续发展，例如，将智慧城市视为实现绿色经济增长的重要途径19。然而，这也带来了如何平衡技术应用与公民隐私保护、确保技术可及性以避免“数字鸿沟”等新的治理挑战2021。
2. 数据与AI赋能公共治理的典型场景创新趋势
2.1 交通优化场景
交通拥堵是全球各大城市面临的普遍挑战，不仅降低了城市运行效率，也增加了能源消耗和环境污染。在智慧城市框架下，数据与AI技术的融合为城市交通优化提供了前所未有的机遇，正在重塑传统的交通管理模式，向更智能、高效和可持续的方向发展。
多源交通感知数据是智能交通系统的基础。这包括来自环路检测器、摄像头、GPS设备、移动通信数据以及物联网（IoT）传感器等多种来源的实时和历史交通信息。这些数据能够提供全面的交通态势视图，例如车流量、车速、道路占用率、排队长度等关键指标22232425。例如，通过摄像头和雷达传感器实时监测路口车流，结合车载探头数据对路段拥堵情况进行精准感知。
车联网（Internet of Vehicles, IoV）数据的兴起，进一步丰富了交通数据的维度和精度。车联网系统允许车辆之间、车辆与基础设施之间进行实时通信，获取车辆位置、速度、行驶方向甚至车辆内部状态等细粒度数据。这为交通管理部门提供了更前瞻性的信息，有助于实现对交通流的预测和控制252627。例如，利用车联网数据可以估算交通信号交叉口的排队长度、探测车辆渗透率以及交通流量24。然而，车联网数据应用的成功在很大程度上取决于市场渗透率，有研究表明，即使在渗透率较低（如1%-5%）的情况下，车联网数据也能有效支持交通优化，如离线优化只需1%的渗透率，在线优化则需要至少5%的渗透率28。
AI预测模型是实现交通智能优化的核心。基于海量多源数据，AI技术（如机器学习、深度学习、强化学习和图神经网络）能够构建复杂的预测模型，识别交通模式、预测拥堵趋势、评估事故风险，并优化交通控制策略2225293031。例如，梯度提升决策树（GBDTs）方法可以预测高速公路事故清除时间，并量化响应时间、交通量等关键影响因素的重要性29。
具体的创新应用实践包括：
- 信号灯动态配时： 传统的固定式信号灯配时难以适应交通流的动态变化。AI驱动的动态信号控制系统能够根据实时车流量、排队长度和OD（起点-终点）需求，通过强化学习（如Deep Q-Learning和Proximal Policy Optimization 22）等算法，实时调整信号灯相位和配时，从而显著减少车辆等待时间、缓解拥堵并提高通行效率2225323334。贝尔格莱德市的模拟研究显示，AI应用使等待时间平均减少了33%，温室气体排放减少了16%，交叉口安全性也有所提高22。
- 拥堵预判与疏导： AI模型能够通过分析历史数据和实时交通状况，预测未来数分钟到数小时内的拥堵区域和程度。一旦预测到拥堵，系统可以自动或辅助人工采取疏导措施，例如通过可变信息标志发布替代路线、引导车辆分流，甚至与自动驾驶车辆协调以优化车队管理和路线规划2630。有研究提出，智能车队管理方案可以在拥堵高峰期有效减少平均出行时间26。
- 绿色出行引导： 智能交通系统不仅关注效率，也致力于促进可持续发展。AI可以结合公共交通实时信息和出行者偏好，为市民提供个性化的绿色出行建议，如推荐步行、骑行或公共交通路线，甚至通过激励机制鼓励低碳出行。例如，基于灰色联合算法的预测方法可以提高智能城市绿色交通碳排放预测的准确性和效率35。
这些创新应用通过提升城市通行效率、减少交通延误、降低燃油消耗和碳排放，为城市居民带来了更便捷、舒适的出行体验，并支持了城市低碳和可持续发展目标的实现。例如，在交通管理中应用AI可以带来系统性能的改进，从而减少交通高峰期的拥堵，应对洪水，甚至在紧急情况发生时提供服务30。
2.2 城市安全场景
城市安全是智慧城市公共治理的基石，涵盖了应对自然灾害、人为事故、恐怖袭击以及日常犯罪等多个方面。数智技术，特别是全域安防传感、公共事件数据与AI风险识别模型的融合应用，正在深刻改变城市安全管理模式，使其从被动响应转向主动预防，并显著提升城市的韧性。
全域安防传感构成了城市安全的“神经系统”。这包括广泛部署在城市各个角落的视频监控系统（CCTV）、环境传感器（如烟雾、水浸、有害气体探测器）、无人机巡检系统 36、以及各类物理访问控制设备。这些传感器能够实时、不间断地收集视觉、环境和行为数据，提供全方位的态势感知能力。例如，智能视频监控系统不仅能录制画面，还能通过内置的边缘计算和AI算法，对异常行为（如徘徊、聚集、遗留物）、异常事件（如火情、交通事故）进行初步识别和报警。数字孪生城市（Digital Twin）技术进一步强化了这一能力，它能够构建城市物理空间的高精度虚拟模型，将来自不同传感器的实时数据映射到数字孪生体上，实现对城市运行状态的精细化监测和可视化管理 3738。
公共事件数据是AI进行风险评估和决策支持的重要输入。这包括历史的犯罪记录、事故报告、应急响应数据、社交媒体上的公众情绪和事件报告、甚至天气预警和地质灾害信息等。这些异构数据源经过整合和清洗，为AI模型提供了训练和分析的基础。通过对历史事件数据的深度学习和模式识别，AI能够理解不同类型事件的发生规律、影响因素和传播机制。
AI风险识别模型是城市安全场景中的“智能大脑”。这些模型能够对海量的实时传感数据和历史公共事件数据进行高级分析，从而实现对潜在风险的早期预警、事件的快速定位与评估，并辅助制定最优响应策略。主要的融合应用和价值体现在：
- 公共安全事件预警： AI模型可以持续分析来自视频监控、社交媒体、异常行为检测等数据流，识别潜在的犯罪活动、人群聚集异常或恐怖袭击威胁。例如，通过对人脸识别、车辆识别和行为分析，AI系统能够追踪可疑人员或车辆，预测潜在冲突区域，并在事件发生前向执法部门发出预警。在突发公共卫生事件中，AI也可用于疫情传播预测和资源调配优化 39。
- 灾害应急响应： 在自然灾害（如洪水、地震、火灾）或人为事故发生时，AI通过整合来自传感器、无人机 3640、卫星图像和实时气象数据，能够快速评估灾情影响范围和严重程度 4041。AI模型可以优化应急资源的分配和调度，例如规划最佳救援路线、识别受灾最严重的区域、预测次生灾害的发生概率，从而提高救援效率，最大限度地减少人员伤亡和财产损失。数字孪生技术在此过程中发挥关键作用，通过模拟不同应急方案的效果，为决策者提供科学依据 41。
- 重点场所安全管控： 对于机场、火车站、大型场馆、关键基础设施等重点区域，AI能够实现更精细化的安全管理。例如，利用AI进行异常入侵检测、人流密度分析、危险品识别等。AI可以分析历史数据，识别高风险时段和区域，并据此调整安保部署，实现预测性安保。此外，AI还能够提升网络安全韧性，保护关键基础设施免受网络攻击，这在智能城市中至关重要，因为许多关键服务都依赖于互联互通的数字系统 424344。
通过上述应用，数据与AI的融合极大地提升了城市的韧性，即城市应对突发事件、抵抗冲击和快速恢复的能力 40。它不仅能帮助城市有效预防和应对各类安全威胁，还能在事件发生后，通过智能化的管理和协调，缩短恢复时间，保障城市功能的正常运行，从而为市民创造一个更安全、更安心的居住环境。例如，AI和物联网（IoT）的结合可以实现对基础设施的自动监控、优化操作，并改善公民体验 45。针对第一响应人员的下一代计算和通信中心（NGFR hub）的开发，也旨在将AI能力嵌入到智能城市的基础设施中，以提升应急服务水平 46。
2.3 住房公平场景
住房公平是智慧城市建设中保障民生福祉、促进社会和谐的关键一环。在这一领域，数智技术通过整合民生政务数据、住房运营数据并运用AI匹配模型，正在创新性地解决住房资源分配不均、保障不足以及市场监管不力等问题，以实现更加公正和高效的住房服务。
民生政务数据是实现住房公平的基础数据来源，它涵盖了居民的收入、户籍、社保、医保、家庭结构、残疾状况等多元信息。这些数据通常分散在不同政府部门，但在智慧城市框架下，通过安全的数据共享和整合平台，可以形成居民的全面画像。例如，可用于精确评估申请保障性住房的居民是否符合特定收入和资产门槛，以及家庭的真实住房需求。
住房运营数据则提供了住房本身的详细信息，包括房产的地理位置、面积、租金或售价、房屋质量状况（如建筑年代、修缮记录）、空置情况、能耗数据以及租赁合同信息等。这些数据可以通过房屋管理部门、物业公司、房地产中介机构，甚至物联网传感器（用于监测房屋结构健康、室内环境等）进行采集。
AI匹配模型是连接民生政务数据与住房运营数据，并实现智能决策的核心技术。利用机器学习、数据挖掘等AI算法，模型能够对海量数据进行深度分析、模式识别和智能推荐。
具体的创新应用实践包括：
- 保障房资格精准核验： 传统的保障房申请审核流程复杂、耗时且容易出现信息不对称或造假。AI模型可以通过交叉比对申请人的多源政务数据（如银行流水、社保记录、不动产登记信息等），自动化、精准地核验申请人是否符合保障房的各项资格条件，包括收入水平、家庭成员数量、现有住房情况等。这种方式显著提高了审核效率和准确性，减少了人为干预带来的潜在不公平，确保有限的保障性住房资源能够分配给真正需要的低收入家庭。例如，非洲的Empowa平台就利用区块链技术管理房地产交易，并结合AI来支持经济适用房的分配47。
- 老旧住房质量动态排查与改造优先级评估： 城市中存在大量老旧住房，其结构安全、居住环境和能源效率往往存在问题。AI模型可以整合房屋的建筑年代、历史修缮记录、居民投诉数据，以及通过物联网传感器实时监测到的结构健康、能耗和环境参数等数据，对老旧住房的质量状况进行动态评估。通过结合地理信息系统（GIS）和居民健康数据，AI还能识别出因住房条件差而导致健康风险较高的区域。基于这些评估结果，AI可以辅助城市管理者确定老旧小区改造的优先级，精准分配修缮资金，优化改造方案，从而提升居民的居住安全性和舒适度。
- 住房租赁市场监管： 住房租赁市场常面临信息不透明、虚假房源、随意涨价、租金贷等问题。AI可以对租赁平台上的房源信息、历史交易数据、租金波动趋势进行实时监测和分析，识别异常行为和潜在风险。例如，通过自然语言处理技术分析租赁合同条款，发现不合理或违法内容；通过数据比对识别虚假房源信息；通过对市场供需和租金走势的预测，为政府制定租金指导价和租赁政策提供数据支持，从而维护租赁双方的合法权益，促进租赁市场的健康发展。
这些结合民生政务数据、住房运营数据与AI匹配模型的应用，能够有效支撑住房资源的公平分配。它通过提高信息透明度、优化资源配置、加强市场监管，不仅能确保住房保障政策的精准落地，也有助于构建一个更加稳定、透明和公正的住房环境，进而提升城市居民的整体生活品质和幸福感。同时，这种实践也呼应了联合国可持续发展目标中关于建设更安全、更有韧性、可持续和包容城市的要求48。然而，在应用这些技术时，也需警惕数据偏见可能导致的服务歧视，例如，基于算法的信用评分系统可能对特定群体造成负面影响，因此在设计和实施过程中必须充分考虑公平性和伦理问题49。
3. 智慧城市数智化治理的核心风险与挑战
3.1 算法偏见引发的公平性问题
在智慧城市数智化治理的快速发展中，算法作为决策的核心驱动力，其内在的偏见问题日益凸显，对城市公共服务的公平性构成了严峻挑战。算法偏见源于算法训练数据偏差、模型逻辑黑箱等多种因素，可能导致资源分配不公、公共服务资格误判以及安全管控差异化对待等一系列问题 750。
算法训练数据偏差是算法偏见最常见的来源之一。算法通过学习历史数据来识别模式并进行预测。如果用于训练的数据在采集过程中存在固有的不平衡、不完整或带有历史偏见，那么算法将会继承并放大这些偏见。例如：
- 社会历史不公的固化： 城市历史数据可能反映了过往的社会不平等现象，如某些社区长期遭受资源投入不足，导致其交通、教育、医疗等基础设施数据表现较差。如果AI系统基于这些数据进行资源分配决策，可能会继续倾斜于已发展良好的区域，从而固化甚至加剧原有的不公平。
- 代表性不足： 训练数据中如果某些特定群体（如少数族裔、低收入人群、老年人、残疾人）的数据量过少或缺失，算法在处理与这些群体相关的决策时，可能会产生不准确或带有歧视性的结果。例如，在面部识别系统中，若训练数据中缺乏足够的多样性，可能导致对特定肤色或性别的人群识别准确率显著下降。
模型逻辑黑箱问题是指许多复杂的AI模型（特别是深度学习模型）的内部运作机制难以被人类理解和解释。这使得当算法做出一个看似不公的决策时，很难追溯其具体原因，也难以确定是数据问题、模型设计缺陷还是其他因素导致了偏见。这种缺乏透明度的情况，使得公众对算法决策的信任度降低，也给监管带来了困难 7。
算法偏见可能引发的资源分配不公问题具体表现为：
- 交通资源倾斜： 在交通优化场景中，如果AI系统基于历史交通数据（可能反映了某些区域优先建设交通基础设施的现实）进行路线规划或信号灯配时，可能会无意识地优先服务于城市的核心商业区或富裕社区，而忽视或低效处理边缘地区或欠发达社区的交通需求，导致这些地区的居民出行效率低下。例如，算法可能倾向于优化整体车流量更大的主干道，而对支路或公共交通不便区域的居民出行需求关注不足。
- 公共服务资格误判： 在住房公平或社会福利分配场景中，AI模型可能被用于评估申请人的资格。如果训练数据带有对某些社会经济群体或人口特征的隐性偏见，算法可能会错误地将符合条件的申请人排除在外，或将不符合条件的人纳入，从而导致公共服务资源的错配。例如，在保障房分配中，若算法过度依赖某些指标（如信用评分或就业稳定性），而这些指标本身就可能因社会结构性不平等而对弱势群体不利，则可能造成这些群体难以获得应有的住房保障。
- 安全管控差异化对待： 在城市安全场景中，基于AI的风险识别和预测系统可能因数据偏见而对特定社区或人群产生过度监控或歧视性执法。例如，如果历史犯罪数据集中在特定区域或与特定人口群体关联，AI模型可能会错误地将这些区域或群体标记为“高风险”，导致警力过度投入，甚至形成“有罪推定”，进而引发社会不满和族群矛盾。
这些公平性问题不仅损害了公民的合法权益，也可能削弱公众对智慧城市治理的信任，甚至引发社会不稳定。因此，在智慧城市数智化治理的设计、部署和运营过程中，必须高度重视算法偏见的识别、评估和缓解，确保技术进步能够真正服务于全体市民的福祉。
3.2 数据全链路隐私保护风险
智慧城市运行的核心在于海量数据的流动与处理，这其中包含了大量的个人信息。从数据采集、存储、共享到使用的全生命周期中，智慧城市面临着严峻的隐私保护挑战，任何环节的疏漏都可能对公众的合法权益造成侵害 5152。
1. 数据采集阶段的个人信息过度采集：
智慧城市的基础设施，如无处不在的传感器、智能摄像头、物联网设备（IoT）以及各类移动应用，能够以前所未有的规模和深度采集公民数据 5354。这些数据包括地理位置、面部特征、生物识别信息、健康状况、消费习惯，甚至实时行为模式等敏感个人信息。过度采集是指在缺乏明确告知、未获得充分授权或超出必要范围的情况下收集个人数据。例如，城市公共区域的智能摄像头可能默认开启人脸识别功能，对所有经过的行人进行识别和记录，而不仅仅是针对犯罪嫌demining人。这种“泛在监控”的模式，在缺乏严格监管的情况下，极易导致个人隐私边界的模糊，使公民处于被持续追踪和分析的状态，侵犯了个人在公共空间的基本隐私权 55。
2. 数据存储阶段的数据泄露风险：
智慧城市所积累的庞大而多样化的数据，往往存储在中心化的云平台或分布式系统中 56。尽管采用了各种安全措施，如加密技术和访问控制 5758，但数据泄露的风险依然存在。网络攻击（如黑客入侵、勒索软件）、内部人员滥用职权、系统漏洞或配置不当都可能导致存储的个人数据被窃取、篡改或非法访问 59。一旦发生大规模数据泄露，公民的身份信息、财务状况、健康记录等敏感信息可能落入不法分子之手，被用于诈骗、身份盗窃甚至勒索，对个人财产安全和人身安全造成直接威胁。例如，医疗健康数据在智能城市中越来越普遍，这些数据虽然能够改善患者护理，但也带来了严重的隐私风险，因为它们是攻击者高度感兴趣的目标 6061。
3. 数据共享阶段的非授权滥用与二次开发风险：
智慧城市强调数据共享以打破“数据孤岛”，提升跨部门协作效率。然而，在数据共享和流通环节，个人信息面临着非授权滥用和二次开发的风险。
- 非授权共享： 数据可能在未经个人同意或授权的情况下，被分享给第三方机构（包括商业公司或研究机构），用于非原始采集目的的分析或商业活动。例如，政府部门为了城市规划或交通分析，将匿名的交通数据分享给企业，但如果匿名化处理不当，仍存在重新识别个人身份的风险。
- 二次开发与目的漂移： 即使数据最初以合法目的采集和共享，但在后续的二次开发和应用中，其使用目的可能发生漂移，超出原有的授权范围。例如，环境监测数据可能被与个人出行轨迹数据结合，用于推断个人的健康状况甚至生活习惯。这种目的漂移使得个人信息被用于原先不可预见的方式，导致个人无法有效控制自己的数据使用权。
4. 跨境数据传输与法律适用困境：
随着全球化和云服务的普及，智慧城市数据可能涉及跨境传输。不同国家和地区对于数据保护和隐私权的法律法规存在差异，这给个人信息的保护带来了复杂性。当数据传输至法律保护标准较低的司法管辖区时，公民的隐私权可能无法得到充分保障。例如，某些国家可能允许政府部门更广泛地访问个人数据，这可能与数据来源国公民的预期隐私权发生冲突。
这些隐私保护风险的累积，不仅侵犯了公民的知情权、同意权和个人数据控制权，还可能导致“数据歧视”，即基于个人数据分析结果，在就业、信贷、教育等方面对特定群体或个人实施不公平待遇。长远来看，如果公众对智慧城市数据处理的安全性与合规性失去信任，将阻碍智慧城市战略的有效推行，甚至引发社会对技术应用的抵触情绪，从而影响城市治理的效率和效果。因此，构建完善的数据安全和隐私保护框架，是智慧城市数智化治理不可或缺的一环。
4. 多元协同的智慧城市治理体系优化趋势
4.1 公众数字化参与机制创新
在智慧城市建设中，技术的进步不仅赋能了政府治理能力的提升，也为公众参与城市治理提供了前所未有的数字化途径。这种数字化转型使得公众参与机制更加便捷、高效和包容，从传统的线下参与模式转向线上线下融合的多元协同模式，强调以公民为中心的设计理念 6263。
1. 公共决策线上听证与协商平台：
传统的公共政策制定过程往往涉及听证会、座谈会等线下形式，受限于时间、地点和参与门槛，公众参与度有限。智慧城市通过建设线上听证和协商平台，能够极大地拓宽公众参与的广度和深度。
- 互动式政策咨询： 政府部门可以通过专门的门户网站、移动应用程序或社交媒体发布拟议政策草案，详细阐释政策背景、目标和预期影响。市民可以在线上提交意见、建议，进行投票或参与讨论。例如，一些城市已经开始利用在线平台，让市民就城市规划、基础设施建设、环境保护等议题发表看法，并直接与政府官员进行互动。
- 虚拟现实（VR）/增强现实（AR）辅助决策： 对于城市规划、建筑设计等空间性较强的决策，可以引入VR/AR技术，让市民在沉浸式体验中直观感受方案效果，并提出改进意见。例如，市民可以通过VR模拟体验新的公园设计或交通线路，提出更符合实际需求的建议。
- 大数据分析民意： 线上平台收集的海量公众意见可以通过自然语言处理（NLP）和情感分析等AI技术进行处理，提取核心诉求、识别社会热点和潜在冲突点，为政府决策提供量化支持，确保民意能够被有效倾听和反映。
2. 算法异议反馈通道的建设：
随着AI在城市治理中的广泛应用，算法偏见（如前文所述）和算法决策的公平性问题日益突出。为了应对这一挑战，建立畅通的算法异议反馈通道至关重要。
- 透明化算法决策过程： 政府应尽可能公开算法在公共服务领域（如交通管理、住房分配、社会福利审核）的应用范围和基本决策逻辑（在不泄露商业机密和保障数据安全的前提下），让公众了解算法如何影响他们的生活。
- 异议提交与审查机制： 市民应有权对算法作出的不利于其的决策提出异议，并要求获得解释。这需要建立一个明确的线上或线下通道，供市民提交申诉。例如，如果市民认为其保障房申请被算法错误拒绝，可以提交申诉并要求人工复核。
- 独立审计与监督： 为了确保算法的公平性和透明度，可以引入独立的第三方机构对政府使用的关键算法进行定期审计，评估其潜在偏见并提出改进建议。同时，公众可以监督这些审计结果的公开性和算法改进的落实情况。
3. 公共数据开放申请与创新：
公共数据是智慧城市的重要资产，其开放不仅能促进社会创新，也能提升政府透明度，并鼓励公众参与。
- 开放数据门户平台： 建立统一的城市公共数据开放平台，按照“默认开放”原则，将经过脱敏处理的、不涉及个人隐私和国家安全的公共数据（如交通流量、环境质量、公共设施分布、政府预算开支等）分类分级向社会开放。
- 数据申请与反馈机制： 对于尚未开放但公众有需求的数据，应提供便捷的申请通道，并明确申请流程和反馈时限。鼓励公众基于开放数据进行二次开发和创新应用，例如开发新的城市服务App或进行社会研究。
- 公民数据素养提升： 开放数据需要公民具备一定的数据素养才能有效利用。政府和社会组织应合作开展数据素养教育，帮助市民理解数据、分析数据，并能够基于数据进行理性讨论和参与。
通过这些数字化参与机制的创新，智慧城市能够从单一的政府主导模式转向政府、企业、公民多方协同共治的模式 64。这不仅有助于提升治理决策的科学性和合法性，增强公众对城市治理的认同感和信任度，也有利于激发社会活力，共同应对城市发展的复杂挑战，最终构建一个更具韧性、更包容、更可持续的智慧城市 656667。值得注意的是，欧洲和中亚国家在利用电子参与工具建设智慧城市方面展现出不同的实践，发达国家和发展中国家在目标和方法上有所差异，这提示我们在推广这些机制时需充分考虑地区经济水平和文化背景 65。
4.2 数智治理合规监管体系构建
随着数据和人工智能（AI）在智慧城市公共治理中扮演越来越核心的角色，构建一套健全、前瞻性的数智治理合规监管体系已成为当务之急。这一体系旨在平衡技术创新与风险控制、提升治理效率与保障公民权益，其发展趋势主要体现在可解释AI技术落地、算法审计制度建立以及数据分类分级保护机制的完善等方面 6869。
1. 可解释AI（XAI）技术落地
AI技术在城市治理中的广泛应用，如交通优化、公共安全预警和住房资格核验等，往往依赖于复杂的模型，其决策过程对普通用户甚至开发者而言如同“黑箱”，难以理解和追溯。这种不透明性不仅引发了公平性担忧，也阻碍了问责机制的建立。因此，推动可解释AI（Explainable AI, XAI）技术落地成为构建合规监管体系的关键一环 6970。
XAI的目标是使AI系统的决策过程变得透明、可理解和可信任。在智慧城市治理实践中，这意味着：
- 决策路径可视化： 开发工具和界面，能够展示AI系统做出某个特定决策所依据的关键数据特征、权重和推理路径。例如，在交通信号灯动态配时场景中，XAI可以解释为何在某个时间点选择了特定的信号配时方案，是基于车流量、行人密度还是紧急车辆通行需求。
- 影响因素分析： 能够识别和量化不同输入变量对AI模型输出结果的影响程度。这有助于发现潜在的算法偏见，并为模型优化提供依据。例如，在保障房资格核验中，XAI可以指出哪些因素（如收入、家庭构成、户籍）在决策中占主导地位，并评估这些因素是否可能导致不公平结果。
- 人性化解释： 将复杂的算法决策转化为非技术人员也能理解的语言和概念。这对于公民理解政府基于AI做出的决策至关重要，有助于提升公众对数智治理的信任度。
通过XAI技术，监管机构能够更好地审查AI系统的合规性，公民也能够对算法决策提出有理有据的质疑，从而实现更加公正透明的治理 6970。
2. 算法审计制度建立
为了确保AI算法在公共治理中的公平、透明和负责任使用，建立独立的算法审计制度是不可或缺的。算法审计是对AI系统在设计、开发、部署和运行全生命周期进行系统性审查和评估的过程，旨在发现并纠正算法偏见、技术漏洞和潜在的伦理风险。
- 审计范围与标准： 算法审计应涵盖数据采集与预处理的偏见检测、模型选择与训练的公平性评估、决策输出的准确性与鲁棒性测试，以及对算法长期影响的监测。审计标准应结合国际最佳实践（如欧盟的AI法案）和本地法律法规，并针对不同应用场景制定具体细则。
- 独立审计机构： 鉴于算法的复杂性和潜在的利益冲突，算法审计应由独立的第三方机构或专门设立的政府部门负责，确保审计结果的客观性和公正性。这些机构应具备跨学科的专业知识，包括计算机科学、伦理学、社会学和法律等。
- 周期性与触发式审计： 算法审计可以是周期性的，定期对所有在用关键算法进行审查；也可以是触发式的，即当出现重大争议、投诉或系统表现异常时启动审计。审计结果应在适当保密的前提下向公众披露摘要，以增加透明度。
- 问责机制： 算法审计应与问责机制相结合。一旦审计发现算法存在偏见或不当之处，应明确责任主体，并强制要求采取纠正措施，包括模型修正、数据调整或流程优化。
3. 数据分类分级保护机制完善
数据是数智治理的“燃料”，其安全与隐私保护是构建合规监管体系的基石。完善数据分类分级保护机制是应对数据泄露、非授权滥用等风险的关键策略 6869。
- 数据分类： 根据数据的敏感程度、泄露后果和应用场景，将智慧城市中的数据划分为不同的类别，如公开数据、内部数据、敏感个人信息、国家秘密数据等。例如，公民的生物识别信息、健康记录和财务数据属于最高敏感级别，而城市公共设施位置、交通流量数据可能属于一般敏感或公开数据。
- 数据分级： 在数据分类的基础上，进一步根据其重要性、影响范围和安全防护要求进行分级。不同级别的数据应匹配不同强度的安全控制措施，包括访问控制、加密技术、匿名化/假名化处理、审计日志和备份恢复机制等。例如，欧盟的《通用数据保护条例》（GDPR）和《加州消费者隐私法案》（CCPA）等全球框架提供了个人数据保护的监管范式，这些都强调了数据分类分级的必要性 69。
- 全生命周期管理： 数据分类分级保护机制应贯穿数据的全生命周期，从数据采集、传输、存储、处理、共享、使用到销毁的每一个环节。确保在数据共享时，敏感数据经过严格的匿名化或差分隐私处理，最大限度地降低个人身份被重新识别的风险。
- 隐私增强技术（PETs）： 推广应用隐私增强技术，如联邦学习、安全多方计算和同态加密等，在不直接暴露原始数据的情况下进行数据分析和协作，为高敏感度数据的处理提供更强的隐私保障。
通过实施这些措施，智慧城市能够构建一个更加安全、公正、透明且负责任的数智治理环境，确保技术创新在法律和伦理的框架内运行，从而持续提升公共服务的质量和效率，同时赢得公民的信任和支持。COBIT 2019等治理框架也为智慧城市AI治理提供了实践指导，旨在强化信息技术治理和数据安全，支持创新并确保合规 68。
5. 未来发展展望与实施路径
未来3-5年，智慧城市数智化公共治理将进入一个更加成熟和精细化的发展阶段。技术迭代与制度优化将并行推进，旨在更好地平衡治理效率、公共公平与权益保护，构建一个以人为本、可持续发展的城市新范式。
技术迭代方向：
超自动化与AI自主决策深化： 随着AI技术（特别是生成式AI、强化学习）的不断成熟，智慧城市治理将逐步实现更高级别的自动化和自主决策。例如，在交通管理中，AI系统将不仅仅是动态调整信号灯，而是能够根据实时路况、气候条件、突发事件甚至市民出行偏好，自主优化整个区域的交通流，甚至与自动驾驶车辆协同，实现交通的“零干预”管理。在公共服务领域，AI将能够更精准地识别服务需求，主动提供个性化服务，并自动化处理大部分行政审批流程。数字孪生技术也将从静态建模向动态模拟、实时预测和情景推演方向发展，成为城市治理的“模拟实验室”和“决策大脑” 71。
边缘计算与分布式智能普及： 考虑到数据隐私、实时性要求和网络带宽限制，边缘计算将在智慧城市中扮演更重要的角色。大量数据将在设备端或靠近数据源的边缘节点进行处理和分析，减少对云中心的依赖，提高响应速度和数据安全性。这将促进分布式AI的发展，使得城市治理能够在更去中心化、更具韧性的架构下运行。例如，城市安防中的AI视觉分析，可利用边缘网络和设备进行实时小目标检测，有助于提升监控效率 72。
区块链技术赋能数据信任与协同： 区块链作为一种分布式账本技术，其不可篡改、可追溯的特性将为智慧城市的数据共享、身份验证和公共服务提供强大的信任基础 73。未来，区块链有望应用于城市政务数据的安全共享与协同、数字身份管理、公共资源交易监管、甚至公民参与的激励机制设计。例如，通过区块链构建的数字身份系统，市民可以更安全地管理和授权其个人数据的使用，政府部门之间也能在确保数据完整性和来源可信的前提下进行数据交换 73。
隐私计算与联邦学习技术应用： 随着数据隐私保护法规的日益严格，如何在保护个人隐私的前提下，充分挖掘数据的价值成为关键。隐私计算（如同态加密、差分隐私、安全多方计算）和联邦学习等技术将得到更广泛的应用。这些技术允许在不暴露原始数据的情况下进行联合分析和模型训练，有助于打破数据孤岛，实现跨部门、跨机构的数据协同治理，同时最大限度地保护公民的隐私权。
制度优化方向：
AI伦理与治理框架的制度化： 面对AI技术带来的伦理挑战（如算法偏见、责任归属），未来将有更完善的法律法规和伦理指南出台，指导AI在公共治理领域的负责任应用 7475。这包括建立AI影响评估机制，强制对重要公共领域AI系统进行伦理审查；设立独立的AI伦理委员会或监管机构，负责监督AI的开发和部署；以及探索AI决策的法律效力、责任分配和申诉机制。
数据要素市场化与安全流通机制： 城市公共数据作为重要的生产要素，其价值释放依赖于健全的数据要素市场和安全流通机制。未来将进一步完善数据确权、定价、交易和监管体系，鼓励政府、企业、社会组织之间在确保安全和隐私的前提下，进行数据的合法合规流通与合作。同时，将加强数据安全立法和执法，严厉打击数据泄露和滥用行为。
公众参与与共治模式的常态化： 城市治理将从“政府主导”向“多元共治”转变，公众数字化参与机制将更加成熟和常态化。除了线上听证、算法异议反馈外，政府将探索通过公民提案、众包治理等方式，让市民更深度地参与到城市问题的发现、解决方案的设计和实施评估中。这将要求政府具备更强的沟通、协商和回应能力，并借助技术平台实现公众意见的精准汇聚和高效反馈。
跨区域、跨层级协同治理机制： 智慧城市的复杂性决定了单一行政主体难以有效治理。未来将加强跨区域、跨层级的协同治理机制建设，实现城市群、都市圈乃至国家层面的数据共享、政策协同和应急联动。这将通过统一的技术标准、数据接口和法律框架来实现，例如通过建立区域性的数据交换平台和协同决策中心。
实施框架建议：
为了兼顾治理效率、公共公平与权益保护，未来智慧城市数智化公共治理的实施应遵循以下差异化框架：
分级分类、差异化治理： 针对不同敏感度、不同影响范围的公共治理场景，采取差异化的数智化策略。对于涉及个人敏感信息和重大公共利益的决策，应更加强调算法透明度、可解释性、人工干预和伦理审查；对于常规性、低风险的城市管理任务，可以赋予AI更高的自动化和自主决策权。
创新驱动与风险防范并重： 鼓励技术创新和应用，但同时要建立健全的风险评估和防范机制。在技术部署前进行全面的风险评估，包括技术风险、伦理风险和社会风险，并制定相应的缓解预案。例如，在试点项目中，可以先小范围测试，评估其对公平性和隐私的影响，再逐步推广。
以人为本、包容普惠： 智慧城市建设的最终目标是提升全体市民的福祉。这意味着在技术应用和制度设计中，要充分考虑不同群体的需求和数字素养水平，避免“数字鸿沟”的扩大 76。例如，提供多渠道、易理解的数字化服务接口，同时保留必要的线下服务选项，确保老年人、残疾人等弱势群体也能享受到数智化治理的便利。此外，注重培养公民的数字技能和批判性思维，使其能够理性看待和参与数智治理。
持续学习与迭代优化： 智慧城市数智化治理是一个动态演进的过程，不可能一蹴而就。应建立持续的监测、评估和反馈机制，定期评估数智化治理的效果、公平性和合规性，根据实际运行情况和出现的新问题，及时调整技术策略、优化制度设计，实现敏捷治理和持续改进。
通过上述技术迭代与制度优化，并遵循差异化的实施框架，智慧城市数智化公共治理有望在未来3-5年内，在提高效率的同时，更好地维护公共公平，有效保护公民权益，最终建成一个更加智能、公正、和谐和可持续的城市社会。
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参考文献
1Embracing the Future: AI and ML Transforming Urban Environments in Smart CitiesOpenAlex
Gagan Deep, Jyoti Verma
This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information on the integration of AI and ML technologies in various aspects of smart cities, including infrastructure optimization, public safety enhancement, and citizen services improvement. The findings suggest that AI and ML technologies enable data-driven decision-making, predictive analytics, and optimization in smart city development. They are vital to the development of transport infrastructure, optimizing energy distribution, improving public safety, streamlining governance, and transforming healthcare services. However, ethical and privacy considerations, as well as technical challenges, need to be solved to guarantee the ethical and responsible usage of AI and ML in smart cities. The study concludes by discussing the challenges and future directions of AI and ML in shaping urban environments, highlighting the importance of collaborative efforts and responsible implementation. The findings highlight the transformative potential of AI and ML in optimizing resource utilization, enhancing citizen services, and creating more sustainable and resilient smart cities. Future studies should concentrate on addressing technical limitations, creating robust policy frameworks, and fostering fairness, accountability, and openness in the use of AI and ML technologies in smart cities.
3A Comprehensive Study of the IoT Cybersecurity in Smart CitiesOpenAlex
Roberto Andrade, Sang Guun Yoo, Luis Tello-Oquendo, et al.
Smart cities exploit emerging technologies such as Big Data, the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) to enhance public services management. The use of IoT allows detecting and reporting specific parameters related to different domains of the city, such as health, waste management, agriculture, transportation, and energy. LoRa technologies, for instance, are used to develop IoT solutions for several smart city domains thanks to its available features, but sometimes people (i.e., citizens, information technology administrators, or city managers) might think that these available features involve cybersecurity risks. This study explores the cybersecurity aspects that define an assessment model of cybersecurity maturity of IoT solutions to develop smart city applications. In that sense, we perform a systematic literature review based on a top-down approach of cybersecurity incident response in IoT ecosystems. Besides, we propose and validate a model based on risk levels to evaluate the IoT cybersecurity maturity in a smart city.
4Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart CitiesOpenAlex
Talha Saeed, Chu Kiong Loo, Muhammad Shahreeza Safiruz Kassim
Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.
5Smart City and Halal Tourism during the Covid-19 Pandemic in IndonesiaOpenAlex
Aan Jaelani, Slamet Firdaus, Didi Sukardi, et al.
This article will explore the use of technology in smart cities for the development of Halal Tourism during the Covid-19 pandemic in Indonesia. The function of technology for Halal Tourism services can be utilized for the prevention and transmission of Covid-19 and for realizing changes in the tourism system that are integrally developed with aspects of public health. The method in this article uses content analysis techniques that are based on written or visual material with the main content sourced from journal articles indexed by Scopus and WoS, then operationalized by determining the aims and objectives of the research, compiling the latest content, analyzing content, comparing results analysis, refine results, and conclude findings. This article concludes that smart cities can improve services rapidly to the public in accessing information about Halal Tourism and help control and manage the Covid-19 pandemic in tourism places by increasing detection, mitigating outbreaks, and making effective decisions when situations are critical. Social protection and economic stimulus by the government for tourism actors affected by Covid-19 and technological innovations such as virtual tourism as new services in developing local tourism potential are solutions to new normal conditions while preserving the values of Indonesia's cultural heritage. KEYWORDS Tourism; Covid-19; Halal Tourism; Smart City; Indonesia. RESUMO Este artigo analisa o uso de tecnologia por cidades inteligentes, para o desenvolvimento do Turismo Halal durante a pandemia Covid-19, na Indonésia. A presença da tecnologia nos serviços associados ao Turismo Halal pode auxiliar na prevenção à transmissão do Covid-19 e na realização de mudanças no sistema turístico, que são desenvolvidas integralmente em termos de saúde pública. Neste artigo, o método inclui a Análise de Conteúdo de materiais visuais ou escritos, provenientes de artigos em periódicos indexados nas bases Scopus e WoS, operacionalizados a partir os objetivos e metas da pesquisa. A seguir os dados coletados mais recentes foram compilados, comparados e analisados; a análise refinou os resultados, concluindo-se com os principais achados da pesquisa. Neste artigo conclui-se que as cidades inteligentes podem qualificar os serviços oferecidos ao público, através do acesso rápido a informações sobre o Turismo Halal, e assim contribuindo com o controle e gerenciamento da pandemia Covid-19 em locais turísticos, aumentando a detecção, mitigando surtos e tomando decisões eficazes quando as situações são críticas. A proteção social e o estímulo econômico do governo para com os atores do turismo afetados pela Covid-19 e as inovações tecnológicas, como o turismo virtual e os novos serviços no desenvolvimento do potencial turístico local, são soluções para quando das novas condições normais, e para preservação dos valores do patrimônio cultural indonésio. PALAVRAS-CHAVE Turismo; Covid-19; Turismo Halal; Cidade Inteligente; Indonésia. AUTORIA Aan Jaelani – Doctor. Associate Professor Department of Islamic Economics, Institut Agama Islam Negeri Syekh Nurjati, Cirebon, Indonesia. Orcid : http://orcid.org/0000-0003-2593-7134 . Email: iainanjal@gmail.com Slamet Firdaus – Doctor. Associate Professor Department of Islamic Studies, IAIN Syekh Nurjati, Cirebon, Indonesia. Orcid : http://orcid.org/0000-0002-0011-277X . Email: slamet.firgo@gmail.com Didi Sukardi – Doctor. Associate Professor Department of Islamic Business Law, Institut Agama Islam Negeri Syekh Nurjati, Cirebon, Indonesia. 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6Digital & data-driven transformations in governance: a landscape reviewOpenAlex
Sarah Giest, Keegan McBride, Anastasija Nikiforova, et al.
Abstract Data for Policy (dataforpolicy.org), a global community, focuses on policy–data interactions by exploring how data can be used for policy in an ethical, responsible, and efficient manner. Within its journal, six focus areas, including Data for Policy Area 1: Digital & Data-driven Transformations in Governance, were established to delineate the evolving research landscape from the Data for Policy Conference series. This review addresses the absence of a formal conceptualization of digital and data-driven transformations in governance within this focus area. The paper achieves this by providing a working definition, mapping current research trends, and proposing a future research agenda centered on three core transformations: (1) public participation and collective intelligence; (2) relationships and organizations; and (3) open data and government. The paper outlines research questions and connects these transformations to related areas such as artificial intelligence (AI), sustainable smart cities, digital divide, data governance, co-production, and service quality. This contribution forms the foundational development of a research agenda for academics and practitioners engaged in or impacted by digital and data-driven transformations in policy and governance.
7The Algorithmic SocietyOpenAlex
Schuilenburg, Marc, Peeters, Rik
We live in an algorithmic society. Algorithms have become the main mediator through which power is enacted in our society. This book brings together three academic fields – Public Administration, Criminal Justice and Urban Governance – into a single conceptual framework, and offers a broad cultural-political analysis, addressing critical and ethical issues of algorithms. Governments are increasingly turning towards algorithms to predict criminality, deliver public services, allocate resources, and calculate recidivism rates. Mind-boggling amounts of data regarding our daily actions are analysed to make decisions that manage, control, and nudge our behaviour in everyday life. The contributions in this book offer a broad analysis of the mechanisms and social implications of algorithmic governance. Reporting from the cutting edge of scientific research, the result is illuminating and useful for understanding the relations between algorithms and power.Topics covered include: Algorithmic governmentality Transparency and accountability Fairness in criminal justice and predictive policing Principles of good digital administration Artificial Intelligence (AI) in the smart city This book is essential reading for students and scholars of Sociology, Criminology, Public Administration, Political Sciences, and Cultural Theory interested in the integration of algorithms into the governance of society.
8A Systematic Literature Review of Smart GovernanceOpenAlex
Febri Naldy Purba, Arry Akhmad Arman
Technology development has an impact on a wide range of aspects of life. The success of any firm depends on technology. Technology alters business processes to produce new business models in a variety of fields, including both the commercial and public sectors. In line with its development, technology became an enabler in business. The role of technology is increasingly essential in the era of digital transformation. Technology can replace traditional ways with more innovative and collaborative ways. Behind the advantages offered by technology, big challenges must be faced, such as the processing of increasingly complex and increasing data, plus the use of Artificial Intelligence (AI) poses ethical and legal challenges. Therefore, governance is needed to answer these challenges. The question that appears then is how the governance model can be used to manage business processes that are currently technology intensive. To answer this, it is necessary to study literature to find the existing models, frameworks, or smart governance architectures and how to implement them. From the literature study, no systematic research has been found that focuses on aspects of smart governance, including definitions, domains, technologies, components, and characteristics of smart governance. To answer these problems, this study conducted a Systematic Literature Review (SLR) related to smart governance. According to the research results, smart governance is the ability or capacity to carry out smart activities, whether or not using technology that supports collaboration to produce efficient decision-making. There are three main characteristics of smart governance, namely participation and partnership, collaboration, and transparency. This study also finds that there are still few models, frameworks, or architectures for smart governance. Most research has created a model, framework, or architecture for smart cities. With this fact, research on developing models, frameworks, or architectures of smart governance is still open for further research.
9The role of artificial intelligence in achieving the Sustainable Development GoalsOpenAlex
Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, et al.
The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.
10Incorporating Artificial Intelligence for Urban and Smart Cities' SustainabilityOpenAlex
Wasswa Shafik
The study provides an overview of the comprehensive exploration of smart cities and their integration of artificial intelligence (AI) for urban sustainability. It covers the definition of smart cities, the importance of AI, key challenges and opportunities, foundational aspects of AI, sustainable infrastructure development, enhancing public services, data governance, citizen engagement, economic development, policy frameworks, future trends, case studies, and conclusions. The study demonstrated that AI assist in surveillance and mitigating environmental threats, such as deforestation, environment destruction, and contamination. Citizen engagement additionally promotes transparency and responsibility, encouraging residents to hold federal government agencies and other stakeholders accountable for their information techniques. The chapter encapsulates the breadth and depth of the discussion within the provided chapter layout, offering insights into the transformative potential of AI in shaping sustainable and inclusive smart cities.
11Smart Cities—A Structured Literature ReviewOpenAlex
Jose Sanchez Gracias, Gregory S. Parnell, Eric Specking, et al.
Smart cities are rapidly evolving concept-transforming urban developments in the 21st century. Smart cities use advanced technologies and data analytics to improve the quality of life for their citizens, increase the efficiency of infrastructure and services, and promote sustainable economic growth. Smart cities integrate multiple domains, including transportation, energy, health, education, and governance, to create an interconnected and intelligent urban environment. Our research study methodology was a structured literature review using Web of Science and Google Scholar and ten smart city research questions. The research questions included smart city definitions, advantages, disadvantages, implementation challenges, funding, types of applications, quantitative techniques for analysis, and prioritization metrics. In addition, our study analyzes the implementation of smart city solutions in international contexts and proposes strategies to overcome implementation challenges. The integration of technology and data-driven solutions in smart cities has the potential to revolutionize urban living by providing citizens with personalized and accessible services. However, the implementation also presents challenges, including data privacy concerns, unequal access to technology, and the need for collaboration across private, public, and government sectors. This study provides insights into the current state and future prospects of smart cities and presents an analysis of the challenges and opportunities they present. In addition, we propose a concise definition for smart cities: “Smart cities use digital technologies, communication technologies, and data analytics to create an efficient and effective service environment that improves urban quality of life and promotes sustainability”. Smart cities represent a promising avenue for urban development. As cities continue to grow and face increasingly complex challenges, the integration of advanced technologies and data-driven solutions can help to create more sustainable communities.
12Artificial intelligence of things for synergizing smarter eco-city brain, metabolism, and platform: Pioneering data-driven environmental governanceOpenAlex
Simon Elias Bibri, Jeffrey Huang, John Krogstie
Emerging smarter eco-cities, inherently intertwined with environmental governance, function as experimental sites for testing novel technological solutions and implementing environmental reforms aimed at addressing complex challenges. However, despite significant progress in understanding the distinct roles of emerging data-driven governance systems—namely City Brain, Smart Urban Metabolism (SUM), and platform urbanism—enabled by Artificial Intelligence of Things (AIoT), a critical gap persists in systematically exploring the untapped potential stemming from their synergistic and collaborative integration in the context of environmental urban governance. To fill this gap, this study aims to explore the linchpin potential of AIoT in seamlessly integrating these data-driven governance systems to advance environmental governance in smarter eco-cities. Specifically, it introduces a pioneering framework that effectively leverages the synergies among these AIoT-powered governance systems to enhance environmental sustainability practices in smarter eco-cities. In developing the framework, this study employs configurative and aggregative synthesis approaches through an extensive literature review and in-depth case study analysis of publications spanning from 2018 to 2023. The study identifies key factors driving the co-evolution of AI and IoT into AIoT and specifies technical components constituting the architecture of AIoT in smarter eco-cities. A comparative analysis reveals commonalities and differences among City Brain, SUM, and platform urbanism within the frameworks of AIoT and environmental governance. These data-driven systems collectively contribute to environmental governance in smarter eco-cities by leveraging real-time data analytics, predictive modeling, and stakeholder engagement. The proposed framework underscores the importance of data-driven decision-making, optimization of resource management, reduction of environmental impact, collaboration among stakeholders, engagement of citizens, and formulation of evidence-based policies. The findings unveils that the synergistic and collaborative integration of City Brain, SUM, and platform urbanism through AIoT presents promising opportunities and prospects for advancing environmental governance in smarter eco-cities. The framework not only charts a strategic trajectory for stimulating research endeavors but also holds significant potential for practical application and informed policymaking in the realm of environmental urban governance. However, ongoing critical discussions and refinements remain imperative to address the identified challenges, ensuring the framework's robustness, ethical soundness, and applicability across diverse urban contexts.
14Methodological Framework for Digital Transition and Performance Assessment of Smart CitiesOpenAlex
Dessislava Petrova‐Antonova, Sylvia Ilieva
The ultimate goal of smart cities is to improve citizens' quality of life in a scenario where technological solutions challenge urban governance. However, the knowledge and framework for data use for smart cities remain relatively unknown. The actual translation of city problems into diverse actions requires specific methodologies to guide digital transitions of cities and to assess to what extent the smart cities' initiatives pursue sustainable development goals. This paper proposes a methodological framework for digital modelling of cities allowing assessment of their performance and supporting decision making. The city model adopts the concept of digital twin as a powerful tool for discussion between stakeholders, as well as citizens to find the smartest solutions and get valuable insight after their deployment. The methodological framework is presented as a set of digital twin concept, stages of digital twinning and implementation strategy. Furthermore, the most common city information models, suitable for implementation of digital twins are summarized.
15The Development and Construction of City Information Modeling (CIM): A Survey from Data PerspectiveOpenAlex
Wei Yu, Xiaowei Zhou, Dongsheng Wang, et al.
With rapid urbanization exacerbating the challenges in resource allocation, environmental sustainability, and infrastructure management, City Information Modeling (CIM) has emerged as an indispensable digital solution for smart city development. CIM represents an advanced urban management paradigm that integrates Geographic Information Systems (GISs), Building Information Modeling (BIM), and the Internet of Things (IoT) to establish a multidimensional digital framework for comprehensive urban data management and intelligent decision making. While the existing research has primarily focused on technical architectures, governance models, and application scenarios, a systematic exploration of CIM’s data-driven characteristics remains limited. This paper reviews the evolution of CIM from a data-centric view introducing a research framework that systematically examines the data lifecycle, including acquisition, processing, analysis, and decision support. Furthermore, it explores the application of CIM in key areas such as smart transportation and digital twin cities, emphasizing its deep integration with big data, artificial intelligence (AI), and cloud computing to enhance urban governance and intelligent services. Despite its advancements, CIM faces critical challenges, including data security, privacy protection, and cross-sectoral data sharing. This survey highlights these limitations and points out the future research directions, including adaptive data infrastructure, ethical frameworks for urban data governance, intelligent decision-making systems leveraging multi-source heterogeneous data, and the integration of CIM with emerging technologies such as AI and blockchain. These innovations will enhance CIM’s capacity to support intelligent, resilient, and sustainable urban development. By establishing a theoretical foundation for CIM as a data-intensive framework, this survey provides valuable insights and forward-looking guidance for its continued research and practical implementation.
16Smart city initiatives: A comparative study of American and Chinese citiesOpenAlex
Qian Hu, Yueping Zheng
Taking a comparative approach, this study conducted text-mining and content analysis of smart city initiatives to evaluate the status and progress of smart city development in China and the United States. Despite sharing similarities in developing and applying technology to improve public services and promote economies, these cities have taken different approaches to building smart cities. The American cities adopted a participatory governance structure whereas the Chinese cities demonstrated a hierarchical model that highlighted a proactive role of government and lacked systematic mechanisms for stakeholder engagement. The American smart city initiatives emphasized the improvement of public services to attract businesses and the use of a collaborative approach to building digital infrastructure. With a higher level of policy support and funding support from the central government, local Chinese governments have assumed a leading role in nurturing and developing the smart technology industry, protecting the environment, and building intelligent infrastructures.
17Evaluation on new first-tier smart cities in China based on entropy method and TOPSISOpenAlex
Yao Zhang, Yongjian Zhang, Hong Zhang, et al.
Overload of infrastructure, shortages in energy resources and environmental pollution constitute the main challenges facing current urban management and development. As the best solution to these challenges, smart cities enjoy increasing attention around the world. As China speeds up the urbanization process, the number of pilot smart cities in China continues to increase. Under such a context, it is of great significance to arrange effective and comprehensive evaluations on the construction level of these smart cities. Based on the development realities of cities in China, this study arranged an in-depth analysis of the current literature and policies and established an evaluation system with five dimensions and 30 indicators. The entropy method and TOPSIS (technique for order preference by similarity to an ideal solution) were comprehensively applied to evaluate 15 new first-tier cities in China. The study results demonstrated that these 15 new first-tier cities still had a relatively low smart level, and the gaps among different cities were significant. Because of the differences in the emphasis of the smart city construction of various cities, their performances under different dimensions were widely divergent. Among the five dimensions, smart infrastructure, economy and life had poor performance compared with smart governance and the environment. In addition, some recommendations were initiated to boost the construction of smart cities in China, including accelerated construction for new infrastructure, more emphasis on the digital economy, acceleration in the development of important mechanisms, etc.
20Datapolis: A Public Governance Perspective on “Smart Cities”OpenAlex
Albert Meijer
Smart cities are presented as both inevitable and benign futures: the technological development is unstoppable and will bring us wealthier, safer and more sustainable cities. Putting technology to use, however, is never entirely a matter of engineering but of strategic, political and value-laden choices. This article combines the literature on technology in the public sector and on (urban) governance to develop a public governance perspective on smart cities. The central theoretical concept “datapolis” highlights how the construction of smart cities can be understood in term of “actors,” “rules,” and “games” of urban governance. The theoretical analysis results in the identification of three types of actors (state, market, and civil society), three governance challenges (balancing perceptions, guaranteeing checks and balances and building upon different forms of knowing), and five governance games (the politics of data collection, data storage, data usage, data visualization and data access). The governance of smart cities is re-conceptualized as the socio-technical structures that emerge from the governance games. The governance challenge is to develop legitimate rules for perceptions, power relations and decisions in smart cities.
21Digital Governance and Digital Divide: A Matrix of the Poor's VulnerabilitiesOpenAlex
Rutiana Dwi Wahyunengseh, Sri Hastjarjo, Tri Mulyaningsih, et al.
Although the internet penetration has reached 64.8% of the population (APJII, 2019), yet the digital divide is still a major issue in Indonesia; especially among the poor. This paper aimed to study and to explain the risks of digital governance implementation in the poverty reduction policies; with the study on how the poor obtains and shares public information in the digital governance era as the focal point. The study was conducted in a city in Indonesia that has been awarded with the Smart City Award. The data was analyzed using descriptive statistics and Social Network Analysis. The study found that some of the poor groups are adapting to the digital governance with the help of the social network in their community; and some of them are alienated digitally. They have been at risk of become marginalized both socially and economically. This paper suggested further studies focusing on the information demand among the poor and the use of new communication technology in the poverty reduction policy making that is inclusive.
22Dynamic Traffic Flow Optimization Using Reinforcement Learning and Predictive Analytics: A Sustainable Approach to Improving Urban Mobility in the City of BelgradeOpenAlex
Volodymyr N. Skoropad, Stevica Deđanski, Vladan Pantović, et al.
Efficient traffic management in urban areas represents a key challenge for modern cities, particularly in the context of sustainable development and reducing negative environmental impacts. This paper explores the application of artificial intelligence (AI) in optimizing urban traffic through a combination of reinforcement learning (RL) and predictive analytics. The focus is on simulating the traffic network in Belgrade (Serbia, Europe), where RL algorithms, such as Deep Q-Learning and Proximal Policy Optimization, are used for dynamic traffic signal control. The model optimized traffic signal operations at intersections with high traffic volumes using real-time data from IoT sensors, computer vision-enabled cameras, third-party mobile usage data and connected vehicles. In addition, implemented predictive analytics leverage time series models (LSTM, ARIMA) and graph neural networks (GNNs) to anticipate traffic congestion and bottlenecks, enabling initiative-taking decision-making. Special attention is given to challenges such as data transmission delays, system scalability, and ethical implications, with proposed solutions including edge computing and distributed RL models. Results of the simulation demonstrate significant advantages of AI application in 370 traffic signal control devices installed in fixed timing systems and adaptive timing signal systems, including an average reduction in waiting times by 33%, resulting in a 16% decrease in greenhouse gas emissions and improved safety in intersections (measured by an average reduction in the number of traffic accidents). A limitation of this paper is that it does not offer a simulation of the system’s adaptability to temporary traffic surges during mass events or severe weather conditions. The key finding is that integrating AI into an urban traffic network that consists of fixed-timing traffic lights represents a sustainable approach to improving urban quality of life in large cities like Belgrade and achieving smart city objectives.
23Smart Cities: Recent Trends, Methodologies, and ApplicationsOpenAlex
Damianos Gavalas, Petros Nicopolitidis, Achilles Kameas, et al.
Worldwide forecasts indicate that the size and population of cities will increase even further. This immense growth will put a strain on resources and pose a major challenge in many aspects of everyday life in urban areas, such as the quality of services in the medical, educational, environmental, public safety, and security sectors, indicatively. Thus, novel methods of management must be put in place for these cities to continue to be sustainable. The wide adoption of pervasive and mobile computing systems gave rise to the term of “smart cities,” which implies the ability of sustainable city growth by leading to major improvements in city management and life in the above-mentioned sectors and other aspects such as energy efficiency, traffic congestion, pollution reduction, parking space, public safety, and recreation. This has been made possible in recent years due to the widespread availability of commodity low-power sensors, smart phones, tablets, and the necessary wireless networking infrastructure, which, along with technologies such as AI and management of big data, may be utilized to address the challenges of sustainable urban environments. \n \nIn this special issue, articles regarding the use of technologies, methodologies, and applications for smart cities are invited. Authors are encouraged to submit articles mainly describing original research, presenting results that advance the state of the art and fuel more efforts in the future. Review articles are also welcome.
24Estimation of Queue Lengths, Probe Vehicle Penetration Rates, and Traffic Volumes at Signalized Intersections using Probe Vehicle TrajectoriesOpenAlex
Yan Zhao, Jianfeng Zheng, Wai Wong, et al.
With the development of connected vehicle technologies and the emergence of e-hailing services, a vast amount of vehicle trajectory data is being collected every day. This massive amount of trajectory data could provide a new perspective for sensing, diagnosing, and optimizing transportation networks. There has been some literature estimating traffic volumes and queue lengths at intersections using the data collected from these probe vehicles. Nevertheless, some of the existing methods only work when the penetration rate of the probe vehicles is high enough. Some other methods require two critical inputs, the distribution of the queue lengths and the penetration rate of the probe vehicles. However, these two inputs might vary a lot both spatially and temporally and are not usually known in the real world. To fill the gap, this paper proposes a novel method for the estimation of queue lengths, probe vehicle penetration rates, and traffic volumes at signalized intersections. The key step is to estimate the penetration rate of the probe vehicles from the distribution of their stopping positions at the intersections. Then, scaling up the number of probe vehicles in the queues and in the traffic according to the estimated penetration rate will give an estimate of the total queue length and the total traffic volume, respectively. The proposed method has been validated by both simulation data and real-field data. The testing results have shown that the method is ready for large-scale real-field applications.
25A Review of Research on Intersection Control Based on Connected Vehicles and Data-Driven Intelligent ApproachesOpenAlex
Kai Gao, Shuo Huang, Jin Xie, et al.
Benefiting from the application of vehicle communication networks and new technologies, such as connected vehicles, video monitoring, automated vehicles and vehicle–road collaboration, traffic network data can be observed in real-time. Applied in the field of traffic control, these technologies can provide high-quality input data and make a more comprehensive evaluation of the effectiveness of traffic control. However, most of the control theories and strategies adopted by adaptive control systems cannot effectively use these real-time, high-precision data. In order to adapt to the development of the times, intersection control theory needs to be further developed. This paper reviews the intersection control strategies from many perspectives, including intelligent data-driven control, conventional timing control, induction control and model-based traffic control. There are three main directions for intersection control based on the connected vehicle environment: (1) data-driven reinforcement learning control; (2) adaptive performance optimization control; (3) research on traffic control based on the environment of connected vehicles (CV); and (4) multiple intersection control based on the CV environment. The review gives a clear view of the data-driven intelligent control theory and its application for intelligent transportation systems.
26Big Data Analytics and Network Calculus Enabling Intelligent Management of Autonomous Vehicles in a Smart CityOpenAlex
Qimei Cui, Yingze Wang, Kwang‐Cheng Chen, et al.
Artificial intelligence (AI) and big data analytics enable autonomous vehicles (AVs) to dramatically change future intelligent transportation in smart cities. AVs are envisaged to evolve to a service rather than a product in the future. To provide best user experience of such services, three primary factors, namely, waiting time, travel time, and supply of AV services, are taken into consideration in a multiobjective optimization. Conventional optimization of services relies on traffic flow analysis over a queuing network model. However, due to the mobility of vehicles and the transfer uncertainty of road networks, the queuing network analysis is too complicated and practically intractable. For accuracy and convenient processing, network calculus (NC) is extended to model the queueing problem in this paper. The optimal number of available AVs can be identified by guaranteeing the waiting time of customers. The satisfaction of AV services can be viewed as a supply and demand problem, and optimized by bipartite graph matching. In order to reduce the average travel time, especially for rush hours with heavy traffic, we further propose a new online AVs fleet management scheme with congestion control for smart cities. It is shown that the intelligent management of AV fleet can be efficiently achieved, outperforming the cases of traditional vehicles. NC-assisted AI enables an efficient intelligent transportation paradigm in smart cities, while achieving substantial energy saving.
27Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control SystemOpenAlex
Zhaolong Ning, Kaiyuan Zhang, Xiaojie Wang, et al.
Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.
28Detector-Free Signal Offset Optimization with Limited Connected Vehicle Market Penetration: Proof-of-Concept StudyOpenAlex
Christopher M. Day, Darcy M. Bullock
Connected vehicle (CV) data have the potential to transform traffic signal operations, but the success of control methods that are based on CV data depends on the level of market penetration. Recent studies of real-time operational strategies in CV environments suggest that penetrations exceeding 20% is required. This study explored the feasibility of using CV data to generate arrival profiles for optimizing arterial progression. Applications to offline (3-h analysis period) and online (15-min analysis period) offset optimization were considered. Vehicle arrival profiles obtained from real-world measurement were used as a basis for comparison. Subsampled distributions were used to estimate the potential distributions that might be obtained from CVs, and these distributions were statistically analyzed to explore the effects of penetration rate, analysis period, and traffic volume. For selected penetration rates ranging from 0.1% to 50%, the subsampled distributions were used to optimize the corridor, and the results were evaluated in the complete data model. The results show that over a 3-h window, successful offline optimization can be achieved with a CV penetration rate as low as 1%. Layering multiple days of data might allow offline optimization with penetration rates as low as 0.1%. Online optimization with 15-min windows requires somewhat higher penetration rates of at least 5%. The results suggest that early applications of CV data may be possible at very low levels of market penetration. In corridors with a high penetration rate of connected mobile devices, some private-sector probe data services may be on the cusp of providing the necessary data to facilitate detector-free optimization.
29Prioritizing Influential Factors for Freeway Incident Clearance Time Prediction Using the Gradient Boosting Decision Trees MethodOpenAlex
Xiaolei Ma, Chuan Ding, Sen Luan, et al.
Identifying and quantifying the influential factors on incident clearance time can benefit incident management for accident causal analysis and prediction, and consequently mitigate the impact of non-recurrent congestion. Traditional incident clearance time studies rely on either statistical models with rigorous assumptions or artificial intelligence (AI) approaches with poor interpretability. This paper proposes a novel method, gradient boosting decision trees (GBDTs), to predict the nonlinear and imbalanced incident clearance time based on different types of explanatory variables. The GBDT inherits both the advantages of statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. One-year crash data from Washington state, USA, incident tracking system are used to demonstrate the effectiveness of GBDT method. Based on the distribution of incident clearance time, two groups are categorized for prediction with a 15-min threshold. A comparative study confirms that the GBDT method is significantly superior to other algorithms for incidents with both short and long clearance times. In addition, incident response time is found to be the greatest contributor to short clearance time with more than 41% relative importance, while traffic volume generates the second greatest impact on incident clearance time with relative importance of 27.34% and 19.56%, respectively.
30Leveraging AI in Urban Traffic Management: Addressing Congestion and Traffic Flow with Intelligent SystemsOpenAlex
Rama Chandra Rao Nampalli
Traffic congestion across the globe is a multimodal problem, intertwining vehicular, pedestrian, and bicycle traffic. The relationship between the multimodal traffic flow is a key factor in understanding urban traffic dynamics. The impact of excessive congestion extends to the excessive cost spent on traffic maintenance, as well as the inherent transportation inefficiency and delayed travel times. From an urban transportation standpoint, an immediate consideration on one hand is monitoring traffic conditions and demand cycles, while on the other hand inducing flow modifications that benefit the traffic network and mitigate congestion. Embedded and centralized control systems that characterize modern traffic management systems extract traffic conditions specific to their regions but lack communication between networks. Moreover, innovative methods are required to provide more accurate up-to-date traffic forecasts that characterize real-world traffic dynamics and facilitate optimal traffic management decisions. In this chapter, we briefly outline the main difficulties and complexities in modeling, managing, and forecasting traffic dynamics. We also compare various conventional and modern Intelligent Transportation Strategies in terms of accuracy and applicability, their performance, and potential opportunities for optimization of multimodal traffic flow and congestion reduction. This chapter introduces various proposed data-driven models and tools employed for traffic flow prediction and management, investigating specific strategies' strengths, weaknesses, and benefits in addressing various real-world traffic management problems. We describe that the design phase of dependable Intelligent Transportation Systems bears unique requirements in terms of the robustness, safety, and response times of their components and the encompassing system model. Furthermore, this architectural blueprint shares similarities with distributed coordinate searching and collective adaptive systems. Town size-independent models induce systemic performance improvements through reconfigurable embedded functionality. These AI techniques feature elaborate anytime planner-engagers ensuring near-optimal performances in an unbiased behavior when the model complexity is varied. Sustainable models minimize congestion during peaks, flooding, and emergency occurrences as they adhere to area-specific regulations. Security-aware and fail-safe traffic management systems relinquish reasonable assurances of persistent operation under various environmental settings, to acknowledge metropolis and complex traffic junctions. The chapter concludes by outlining challenges, research questions, and future research paths in the field of transportation management.
31Applications of Artificial Intelligence in Transport: An OverviewOpenAlex
Rusul Abduljabbar, Hussein Dia, Sohani Liyanage, et al.
The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.
32Implementation of a Real-Time Analysis Simulation System for Traffic Signal Control Algorithms to Reduce Vehicle Exhaust Emissions and Improve Traffic FlowOpenAlex
Hyeokgyu Kwon, Jinhwan Jang, Jongsik Kim, et al.
Traffic signal control (TSC) is a part of intelligent transportation systems to reduce traffic congestion and emissions. Recently, dynamic traffic signal control systems using artificial intelligence and reinforcement learning have been studied to achieve these goals. Although verification of TSC algorithms is ideally performed in a real environment, traffic simulation tools are widely used due to safety issues. However, existing simulation tools have limitations in real-time result analysis, making it difficult to analyze the strengths and weaknesses of each algorithm according to each traffic situation. In this paper, we propose RTASS, a real-time analysis simulation system for TSC algorithms that enables real-time comparison and result analysis, to improve the problems of such a simulation environments. To validate RTASS, we experiment using actual intersection data. As a result of comparing the traffic volume of the actual intersection data and the simulation, we confirmed that the error rate was maintained within 2% on average. In addition, we evaluate the performance of each TSC algorithm using RTASS and verify the results by comparing two existing methods and two proposed TSC methods. We confirmed that real-time comparison of traffic analysis and algorithms is possible using the proposed simulation system.
33Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search AlgorithmsOpenAlex
Arshad Jamal, Muhammad Tauhidur Rahman, Hassan M. Al-Ahmadi, et al.
Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.
34Traffic Signaling optimization for Intelligent and Green Transportation in Smart CitiesOpenAlex
Musa Balta, İbrahim Özçeli̇k
In today's world, population growth causes more vehicle traffic and also this increase in vehicle traffic causes more traffic congestion, air pollution, fuel consumption, health problems and so on. In order to solve these problems in this study, we applied traffic signaling optimization by using ant colony algorithm on an urban isolated intersection structure. Vehicles movement are set according to VANET architecture and traffic data are transmitted from road to center via VANET. It was aimed to show that decreasing in average waiting times of vehicles at intersections reduces the CO2 emissions, fuel consumption and noise ratios in the study. For this purpose, simulations run using traffic scenarios with different vehicle density and results obtained were also compared with Webster's equations and fixed time systems in current traffic signaling techniques. Thus, it was realized that the use of ACO (Ant Colony optimization) in traffic signaling systems gives better results in dense and variable traffic conditions.
35Prediction method of green transportation carbon emission in smart city based on gray joint algorithmOpenAlex
Runze Gao, Xiao Li, Haitao Yu
In order to improve the accuracy and efficiency of green transportation carbon emission prediction in smart city, this paper proposes a new green transportation carbon emission prediction method based on gray joint algorithm. This method first analyzes the factors that affect the green transportation carbon emission of smart city, and selects the carbon emission intensity and urban energy efficiency as the key factors to calculate. Based on the calculation results, support vector machine is used to solve the linear prediction problem of traffic carbon emissions. The result of support vector regression is regarded as gray value, and the gray value fitting calculation is completed by using accumulation method. Based on the gray value calculation results, combined with the residual calculation results, the gray prediction model of carbon emission is constructed, and the prediction of green transportation carbon emission in smart city is significantly improved.
36Advancements in Machine Learning and AI for Intelligent Systems in Drone Applications for Smart City DevelopmentsOpenAlex
Sampath Boopathi
The chapter explores the integration of drones, machine learning, and artificial intelligence (AI) in smart city development. Drones can revolutionize urban planning, energy efficiency, noise reduction, environmental monitoring, traffic management, infrastructure inspection, public safety, data security, and privacy protection. AI-driven solutions enable data-driven decision-making for resource allocation, sustainability, and predictive modeling. AI optimizes flight paths for energy efficiency, noise reduction strategies enhance drone social acceptance, and autonomous drone navigation is crucial for safe urban deployment. Drones optimize traffic flow, reduce congestion, and enhance safety. AI provides predictive insights into traffic patterns, and drones aid in law enforcement, emergency response, and first responder support. Data security and privacy protection measures are essential for maintaining public trust.
37The Microverse: A Task-Oriented Edge-Scale MetaverseOpenAlex
Qian Qu, Mohsen Hatami, Ronghua Xu, et al.
Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), blockchain, and digital twins (DTs). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces the microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike all-encompassing metaverses, each microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge–fog–cloud computing framework. The microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the metaverse.
38Microverse: A Task-Oriented Edge-Scale MetaverseOpenAlex
Qian Qu, Mohsen Hatami, Ronghua Xu, et al.
Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge-fog-cloud computing, and machine learning (ML)/Artificial Intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), Blockchain, and Digital Twins (DT). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR Metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces Microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike an all-encompassing Metaverses, each Microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each Microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge-fog-cloud computing framework. The Microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the Metaverse.
39Revolutionizing healthcare: the role of artificial intelligence in clinical practiceOpenAlex
Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany, et al.
INTRODUCTION: Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE: This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS: The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS: Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION: AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.
40Artificial intelligence for enhancing resilienceOpenAlex
Nitin Rane, Saurabh Choudhary, Jayesh Rane
In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes. In climate change, AI-driven strategies are assessed for their effectiveness in fostering climate resilience, including predictive modeling of extreme weather events and optimizing resource allocation. The paper also discusses AI applications in healthcare resilience, such as enhancing diagnostics, patient care, and operational efficiency during crises. Business continuity and crisis management are examined, highlighting AI's capability to anticipate disruptions and maintain operational stability. The paper emphasizes the importance of strengthening cybersecurity resilience using AI to detect and mitigate threats proactively. AI's role in enhancing community and social resilience is analysed, particularly in supporting vulnerable populations and fostering social cohesion. Additionally, we explored AI-powered solutions for urban resilience, focusing on smart cities and sustainable development. The study also covers AI's contributions to environmental and ecological resilience, resilient supply chain management, and resilience in the hospitality and tourism industry. Finally, we investigated AI's potential in fostering psychological resilience, providing personalized mental health support and stress management tools. Through these diverse applications, the paper underscores AI's critical role in building a resilient future.
42A Master Attack Methodology for an AI-Based Automated Attack Planner for Smart CitiesOpenAlex
Gregory Falco, Arun Viswanathan, Carlos Caldera, et al.
America's critical infrastructure is becoming “smarter”and increasingly dependent on highly specialized computers called industrial control systems (ICS). Networked ICS components now called the industrial Internet of Things (IIoT) are at the heart of the “smart city”, controlling critical infrastructure, such as CCTV security networks, electric grids, water networks, and transportation systems. Without the continuous, reliable functioning of these assets, economic and social disruption will ensue. Unfortunately, IIoT are hackable and difficult to secure from cyberattacks. This leaves our future smart cities in a state of perpetual uncertainty and the risk that the stability of our lives will be upended. The Local government has largely been absent from conversations about cybersecurity of critical infrastructure, despite its importance. One reason for this is public administrators do not have a good way of knowing which assets and which components of those assets are at the greatest risk. This is further complicated by the highly technical nature of the tools and techniques required to assess these risks. Using artificial intelligence planning techniques, an automated tool can be developed to evaluate the cyber risks to critical infrastructure. It can be used to automatically identify the adversarial strategies (attack trees) that can compromise these systems. This tool can enable both security novices and specialists to identify attack pathways. We propose and provide an example of an automated attack generation method that can produce detailed, scalable, and consistent attack trees-the first step in securing critical infrastructure from cyberattack.
43Hybrid quantum architecture for smart city securityOpenAlex
Vita Santa Barletta, Danilo Caivano, Mirko De Vincentiis, et al.
Currently and in the near future, Smart Cities are vital to enhance urban living, address resource challenges, optimize infrastructure, and harness technology for sustainability, efficiency, and improved quality of life in rapidly urbanizing environments. Owing to the high usage of networks, sensors, and connected devices, Smart Cities generate a massive amount of data. Therefore, Smart City security concerns encompass data privacy, Internet-of-Things (IoT) vulnerabilities, cyber threats, and urban infrastructure risks, requiring robust solutions to safeguard digital assets, citizens, and critical services. Some solutions include robust cybersecurity measures, data encryption, Artificial Intelligence (AI)-driven threat detection, public–private partnerships, standardized security protocols, and community engagement to foster a resilient and secure smart city ecosystem. For example, Security Information and Event Management (SIEM) helps in real-time monitoring, threat detection, and incident response by aggregating and analyzing security data. To this end, no integrated systems are operating in this context. In this paper, we propose a Hybrid Quantum-Classical Architecture for bolstering Smart City security that exploits Quantum Machine Learning (QML) and SIEM to provide security based on Quantum Artificial Intelligence and patterns/rules. The validity of the hybrid quantum-classical architecture was proven by conducting experiments and a comparison of the QML algorithms with state-of-the-art AI algorithms. We also provide a proof of concept dashboard for the proposed architecture.
45Impact of Integrated Artificial Intelligence and Internet of Things Technologies on Smart City TransformationOpenAlex
Van-Thanh Hoang
Rapid urbanization is placing tremendous pressure on limited resources and aging infrastructure in cities worldwide. Meanwhile, new technologies are emerging to help address urban challenges through data-driven solutions. This paper explores how the strategic integration of artificial intelligence (AI) and Internet of Things (IoT) can transform urban management and services delivery for smart and sustainable cities. The Internet of Things enables the ubiquitous collection of real-time data across urban systems through embedded sensors. However, extracting actionable insights requires advanced analytics. Concurrently, artificial intelligence provides techniques to autonomously analyze huge volumes of IoT-sensed urban data. When combined effectively, AI and IoT can automatically monitor infrastructure, optimize operations, and enhance citizen experiences. This paper first defines key concepts and outlines applications of AI and IoT independently in areas like transportation, energy, environment, and public safety. It then examines how both technologies can be integrated for mutual benefit. Examples of integrated solutions such as predictive maintenance, intelligent transportation, and emergency response optimization are discussed. Challenges to adoption like data privacy, infrastructure costs, skills gaps, and technical standardization are also covered. The conclusion underscores the tremendous potential of AI and IoT to create efficient, resilient and livable urban environments through ubiquitous sensing and autonomous management. With proper policy support and collaborations, cities worldwide can leverage these smart technologies to sustainably combat problems facing urbanization.
46Next Generation Computing and Communication Hub for First Responders in Smart CitiesOpenAlex
O. A. Shaposhnyk, Kenneth Lai, Gregor Wolbring, et al.
This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service.
47Technological Transformation in Infrastructure & Real Estate: Artificial Intelligence (AI), Blockchain (DLT), Project Management & Policy Implications across Leading Markets in Africa (Egypt, South-Africa & Nigeria)OpenAlex
Abel Eseoghene Owotemu, Ayo Ibaru
Artificial Intelligence (AI) and Blockchain (Distributed Ledger Technology or DLT) are revolutionizing industries globally, with real estate and project management emerging as key beneficiaries of its transformative impact. In Africa, AI is redefining real estate and housing finance by enhancing operational efficiency, optimizing project management, and addressing critical challenges such as affordability, accessibility, and sustainability. This study investigates trends and developments in AI applications within infrastructure, real estate and housing finance from 2019 to 2023, focusing on leading markers across Africa. It explores AI technologies such as predictive analytics, generative AI, DLT, and computer vision, which are empowering project managers with tools for data-driven decision-making, risk mitigation, and enhanced resource allocation. Key developments include Distributed Ledger Technology (Blockchain) and AI-driven solutions for predictive analytics in property valuation, automation in credit scoring and loan servicing, and urban planning innovations that promote sustainable communities in terms of infrastructure development. Case studies and use cases highlight platforms such as HouseAfrica and Empowa, both of which leverage Blockchain for managing real estate transactions and AI for affordable housing across Africa, and as well as the surge in adoption of global tools like EDGE for green building certifications. Despite these advancements, the study acknowledges ethical concerns, including data bias and job displacement risks, emphasizing the need for responsible technology adoption. This research underscores the potential of technologies like AI and Blockchain in driving innovation, bridging gaps in housing and infrastructure development, and contributing to economic growth, particularly in emerging markets. By aligning AI advancements with inclusive policies, stakeholders can unlock opportunities for transforming infrastructure, real estate and housing finance in leading African countries and beyond.
48Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic CitiesOpenAlex
Carlos Moreno, Zaheer Allam, Didier Chabaud, et al.
The socio-economic impacts on cities during the COVID-19 pandemic have been brutal, leading to increasing inequalities and record numbers of unemployment around the world. While cities endure lockdowns in order to ensure decent levels of health, the challenges linked to the unfolding of the pandemic have led to the need for a radical re-think of the city, leading to the re-emergence of a concept, initially proposed in 2016 by Carlos Moreno: the “15-Minute City”. The concept, offering a novel perspective of “chrono-urbanism”, adds to existing thematic of Smart Cities and the rhetoric of building more humane urban fabrics, outlined by Christopher Alexander, and that of building safer, more resilient, sustainable and inclusive cities, as depicted in the Sustainable Development Goal 11 of the United Nations. With the concept gaining ground in popular media and its subsequent adoption at policy level in a number of cities of varying scale and geographies, the present paper sets forth to introduce the concept, its origins, intent and future directions.
49The Scored Society: Due Process for Automated PredictionsOpenAlex
Danielle Keats Citron, Frank Pasquale
Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers—or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail—credit—the law focuses on credit history, not the derivation of scores from data. Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The American due process tradition should inform basic safeguards. Regulators should be able to test scoring systems to ensure their fairness and accuracy. Individuals should be granted meaningful opportunities to challenge adverse decisions based on scores miscategorizing them. Without such protections in place, systems could launder biased and arbitrary data into powerfully stigmatizing scores.
50The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and OpportunitiesOpenAlex
Elda Cina, Ersin Elbaşı, Gremina Elmazi, et al.
As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities.
51Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction FrameworkOpenAlex
Elvira Ismagilova, Laurie Hughes, Nripendra P. Rana, et al.
The complex and interdependent nature of smart cities raises significant political, technical, and socioeconomic challenges for designers, integrators and organisations involved in administrating these new entities. An increasing number of studies focus on the security, privacy and risks within smart cities, highlighting the threats relating to information security and challenges for smart city infrastructure in the management and processing of personal data. This study analyses many of these challenges, offers a valuable synthesis of the relevant key literature, and develops a smart city interaction framework. The study is organised around a number of key themes within smart cities research: privacy and security of mobile devices and services; smart city infrastructure, power systems, healthcare, frameworks, algorithms and protocols to improve security and privacy, operational threats for smart cities, use and adoption of smart services by citizens, use of blockchain and use of social media. This comprehensive review provides a useful perspective on many of the key issues and offers key direction for future studies. The findings of this study can provide an informative research framework and reference point for academics and practitioners.
52A Secure and Privacy-preserving Internet of Things Framework for Smart CityOpenAlex
Moussa Witti, Dimitri Konstantas
Since the widespread use of smart devices, Internet of Things becomes a fundamental component of the smart city to collect citizen data for e-governance, manage household electric and gas consumption, provide real-time remote patient monitoring via smart home, assess risk of air pollution or traffic jam before it occurs. Thus, the use of Internet of Things enabled technologies promise smart citizenship management and governance to improve the life in the city. Because of the problems related to IOT based devices, which are security and privacy concerns in personal data collection, building a smart city platform require an adapted framework to protect citizen household data. In this paper, we propose a framework to ensure security and protecting citizen's privacy for smart city.
53Review on Security of Internet of Things Authentication MechanismOpenAlex
Tarak Nandy, Mohd Yamani Idna Idris, Rafidah Md Noor, et al.
Internet of things (IoT) is considered as a collection of heterogeneous devices, such as sensors, Radio-frequency identification (RFID) and actuators, which form a huge network, enabling non-internet components in the network to produce a better world of services, like smart home, smart city, smart transportation, and smart industries. On the other hand, security and privacy are the most important aspects of the IoT network, which includes authentication, authorization, data protection, network security, and access control. Additionally, traditional network security cannot be directly used in IoT networks due to its limitations on computational capabilities and storage capacities. Furthermore, authentication is the mainstay of the IoT network, as all components undergo an authentication process before establishing communication. Therefore, securing authentication is essential. In this paper, we have focused on IoT security particularly on their authentication mechanisms. Consequently, we highlighted enormous attacks and technical methods on the IoT authentication mechanism. Additionally, we discussed existing security verification techniques and evaluation schemes of IoT authentication. Furthermore, analysis against current existing protocols have been discussed in all parts and provided some recommendation. Finally, the aim of our study is to help the future researcher by providing security issues, open challenges and future scopes in IoT authentication.
54Anatomy of Threats to the Internet of ThingsOpenAlex
Imran Makhdoom, Mehran Abolhasan, Justin Lipman, et al.
The world is resorting to the Internet of Things (IoT) for ease of control and monitoring of smart devices. The ubiquitous use of IoT ranges from industrial control systems (ICS) to e-Health, e-Commerce, smart cities, supply chain management, smart cars, cyber physical systems (CPS), and a lot more. Such reliance on IoT is resulting in a significant amount of data to be generated, collected, processed, and analyzed. The big data analytics is no doubt beneficial for business development. However, at the same time, numerous threats to the availability and privacy of the user data, message, and device integrity, the vulnerability of IoT devices to malware attacks and the risk of physical compromise of devices pose a significant danger to the sustenance of IoT. This paper thus endeavors to highlight most of the known threats at various layers of the IoT architecture with a focus on the anatomy of malware attacks. We present a detailed attack methodology adopted by some of the most successful malware attacks on IoT, including ICS and CPS. We also deduce an attack strategy of a distributed denial of service attack through IoT botnet followed by requisite security measures. In the end, we propose a composite guideline for the development of an IoT security framework based on industry best practices and also highlight lessons learned, pitfalls and some open research challenges.
55Making Data Visible in Public SpaceOpenAlex
Sage Cammers-Goodwin, Naomi Van Stralen
“Transparency” is continually set as a core value for cities as they digitalize. Global initiatives and regulations claim that transparency will be key to making smart cities ethical. Unfortunately, how exactly to achieve a transparent city is quite opaque. Current regulations often only mandate that information be made accessible in the case of personal data collection. While such standards might encourage anonymization techniques, they do not enforce that publicly collected data be made publicly visible or an issue of public concern. This paper covers three main needs for data transparency in public space. The first, why data visibility is important, sets the stage for why transparency cannot solely be based on personal as opposed to anonymous data collection as well as what counts as making data transparent. The second concern, how to make data visible onsite, addresses the issue of how to create public space that communicates its sensing capabilities without overwhelming the public. The final section, what regulations are necessary for data visibility, argues that for a transparent public space government needs to step in to regulate contextual open data sharing, data registries, signage, and data literacy education.
56Data Security and Privacy Protection for Cloud Storage: A SurveyOpenAlex
Pan Yang, Naixue Xiong, Jingli Ren
The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.
57Securing Spatial Data Infrastructures for Distributed Smart City applications and servicesOpenAlex
Kanishk Chaturvedi, Andreas Matheus, Son H. Nguyen, et al.
Smart Cities are complex distributed systems which may involve multiple stakeholders, applications, sensors, and IoT devices. In order to be able to link and use such heterogeneous data, spatial data infrastructures for Smart Cities can play an important role in establishing interoperability between systems and platforms. Based on the open and international standards of the Open Geospatial Consortium (OGC), the Smart District Data Infrastructure (SDDI) concept integrates different sensors, IoT devices, simulation tools, and 3D city models within a common operational framework. However, such distributed systems, if not secured, may cause a major threat by disclosing sensitive information to untrusted or unauthorized entities. Also, there are various users and applications who prefer to work with all the systems in convenient ways using Single-Sign-On. This paper presents a concept for securing distributed applications and services in such data infrastructures for Smart Cities. The concept facilitates privacy, security and controlled access to all stakeholders and the respective components by establishing proper authorization and authentication mechanisms. The approach facilitates Single-Sign-On (SSO) authentication by a novel combination in the use of the state-of-the-art security concepts such as OAuth2 access tokens, OpenID Connect user claims and Security Assertion Markup Language (SAML). An implementation of this concept for the district Queen Elizabeth Olympic Park in London is shown in this paper and is also provided as an online demonstration. Such access control and security federation based realization has not been considered in spatial data infrastructures for Smart Cities before.
58A lightweight three factor authentication framework for IoT based critical applicationsOpenAlex
Manasha Saqib, Bhat Jasra, Ayaz Hassan Moon
IoT is emerging as a massive web of heterogeneous networks estimated to interconnect over 41 billion devices by 2025, generating around 79 zettabytes of data. The heterogeneous network shall bring in a plethora of digital services leveraging cloud and communication technologies to drive smart city applications. As users access these services remotely in a ubiquitous environment over public channels, it becomes imperative to secure their communication. Both entity and message authentication emerge as a critical security primitive to thwart unauthorized access and prevent the falsification of messages. While researchers have given due attention to achieving mutual authentication between the subscriber (remote user) and gateway node (broker), the mutual authentication between the gateway node and an IoT sensor node is left to be desired. It could be done at the peril of a rogue or a shadow IoT device unauthorizedly joining an IoT-based network. Some of the widely used IoT-specific application layer protocols like constrained application protocol (COAP) and message queue telemetry transport (MQTT) protocol are not inherently equipped with adequate security safeguards. They, therefore, rely on underlying transport layer security protocols, which are highly computationally intensive. To address this issue, this paper proposes a three-factor authentication framework suitable for IoT-driven critical applications based upon identity, password and a digital signature scheme. The framework employs publish-subscribe pattern leveraging elliptical curve cryptography (ECC) and computationally low hash chains. The formal and informal security analysis shows that the framework is resistant to different types of cryptographic attacks. Furthermore, the automated validation performed with the Scyther tool verifies that there are no cryptographic attacks found on any of the claims stated in the proposed framework. Finally, a comparison of the framework security features, computational, and communication overheads is carried out with other existing protocols.
59SECURING THE SMART CITY: A REVIEW OF CYBERSECURITY CHALLENGES AND STRATEGIESOpenAlex
Johnson Sunday Oliha, Preye Winston Biu, Ogagua Chimezie Obi
In the era of rapid urbanization and technological advancement, the emergence of smart cities promises innovative solutions to urban challenges. However, the integration of various technologies into urban infrastructures also exposes cities to unprecedented cybersecurity threats. This review presents a comprehensive review of the cybersecurity challenges faced by smart cities and explores the strategies to mitigate these risks. Smart cities leverage interconnected networks of sensors, devices, and systems to enhance efficiency, sustainability, and citizen services. Yet, this interconnectedness creates a complex attack surface vulnerable to cyber threats. One of the primary challenges is the diverse range of IoT devices deployed across smart city infrastructures, often lacking robust security mechanisms. These devices are susceptible to exploitation by cybercriminals for malicious activities, such as data breaches, sabotage, and surveillance. Moreover, the interconnected nature of smart city systems amplifies the potential impact of cyberattacks, posing significant risks to critical infrastructure, public safety, and privacy. Threat actors can exploit vulnerabilities in interconnected systems to disrupt essential services, manipulate data, or even cause physical harm. As smart cities rely on data-driven decision-making, the integrity and confidentiality of data become paramount concerns. To address these challenges, various cybersecurity strategies have been proposed and implemented. These strategies encompass a multi-layered approach, integrating technical solutions, regulatory frameworks, and collaboration among stakeholders. Technical measures include encryption, authentication mechanisms, intrusion detection systems, and secure software development practices. Additionally, implementing robust access controls and network segmentation can limit the scope of potential attacks. Furthermore, regulatory initiatives play a crucial role in enhancing cybersecurity standards and promoting compliance among smart city stakeholders. Establishing clear guidelines for data protection, privacy rights, and incident response protocols is essential to safeguarding citizens' interests. Collaboration among government agencies, private sector partners, academia, and cybersecurity experts fosters information sharing and collective defense against emerging threats. Securing smart cities against cybersecurity threats requires a concerted effort to address the multifaceted challenges posed by interconnected technologies. By implementing comprehensive strategies encompassing technical measures, regulatory frameworks, and collaborative approaches, smart cities can mitigate risks and foster a resilient and secure urban environment for all citizens. Keywords: Smart City, Cybersecurity, AI, Technology, Security, Review.
60Preserving the Privacy of Electronic Health Records using BlockchainOpenAlex
Yogesh Sharma, B. Balamurugan
Electronic health records (EHRs) are health information of patients that are saved digitally in a network. Various opportunities to enhance patient care, performance measures in clinical practice and contribute to clinical research in the future are provided by EHRs. The schemes used to store EHRs have been very insecure in the present era of smart cities and homes. The data can be easily breached by hackers and unauthorized external parties. Also, the data is not accessible to patients and care providers. These schemes are unable to create a balance between data security and data accessibility. But blockchain can resolve these issues. Blockchain creates a ledger system that is immutable and allows the transactions to take place in a decentralized manner. The three main features of blockchain technology - Security, Decentralization, and Transparency make any application built using it secure and not accessible by unauthorized parties. The manipulation of data is almost impossible to do in a blockchain network. In this project, we propose a system to implement EHRs using blockchain technology and make EHRs more secure and private. The blockchain technology will keep control over access to information using its cryptographic techniques and decentralization. It will also maintain the balance between data privacy and data accessibility. Our main objective of this project is the framing of data privacy and security issues in electronic healthcare
61A Decentralized Privacy-Preserving Healthcare Blockchain for IoTOpenAlex
Ashutosh Dhar Dwivedi, Gautam Srivastava, Shalini Dhar, et al.
Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology-remote patient monitoring-is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient's life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.
62Citizen-centric Smart City DevelopmentOpenAlex
Lama Zakzak
Developing a “smart city” can follow numerous pathways. Policymakers can follow a technology-focused pathway, a data-driven approach or an environmentally-influenced perspective, among many others. However, following a people-centric smart city developmental path requires a societal approach that involves public engagement and participation as a core developmental philosophy. This requires the city to develop the capacity to widely measure wellbeing and the state of “happiness”, and respond through its public policy frameworks. City-wide innovations using societal big data are making such engagements feasible today. The case of Dubai's “Happiness Agenda” is an example of a smart city initiative that applies a hybrid of big data-driven tools and more traditional approaches in citizen-government interactions. Dubai's transformation plan, is primarily fueled by digital technology and data, with a core focus on public “happiness” and making Dubai one of the “happiest” places to live — as stated in the vision of Smart Dubai Office. Within the smart city framework, such vision entails the challenge of shaping happiness into national policy goals and employing digital technology to apply this vision. This paper reports the findings of semi-structured interviews with the people behind the design of Dubai's Happiness Agenda. The findings highlight the challenges and enablers of developing a people-centric developmental pathway towards “happiness” in the age of big data and smart cities. Even when a positive view towards citizen-engagement exists in government, how can the smart city stakeholders reach a unified definition for happiness in the digital age? How can big data enable the mapping of various needs of a cosmopolitan and culturally diverse society? This paper provides an exploratory case study of citizen-centric and data-driven smart city development initiative, which can provide valuable policy lessons to smart city policymakers and enrich the smart city and e-participation literature.1
63Searching for the real sustainable smart city?OpenAlex
C. William R. Webster, Charles Leleux
The emergence of ‘Smart Cities’ is a contemporary global phenomenon which is closely aligned to a vision of modern technologically advanced sustainable urban environments. However, public policy and academic discourses differ about what constitutes a city that is either ‘smart’ or ‘sustainable’, and assumptions are frequently made about the positive impact of technology and its potential benefit to the environment. Whilst a smart city is not necessarily a sustainable city, the terms ‘smart city’ and the ‘sustainable city’ are increasingly being fused together in the concept of the Sustainable Smart City (SSC). This article seeks to explore the conceptual components of the SSC, with a particular focus on the participatory role of the citizen, where this involves the use of new digital technologies. Conventional eGovernment has tended to focus on service delivery rather than engaging citizens in participatory activity, whilst traditional discourses on sustainability focus on environmentalism rather than broader societal sustainability. Sustainability in the context of the SSC is a much wider concept, where the aspiration is also to improve the quality life by engaging citizens in participatory governance, by co-creating sustainability values, and by developing relationships, trust and sustainable mechanisms for ongoing engagement. In this respect, new digital technology is understood according to its transformational potential and the opportunities which it offers to citizens in delivery of services, meaningful participation and of sustainable societal solutions. This article explores the three underlying conceptual pillars of the SCC, namely insights deriving from perspectives associated with (1) sustainability, (2) new technology and (3) participation, where each of these perspectives offers up its own rationale and institutional logic. Here, it is argued, that whilst practice around SSC’s differs considerably, the ‘real’ SSC stands at the nexus of new technology, citizen engagement and sustainable outcomes.
64Moving from e-Gov to we-Gov and beyond: a blockchain framework for the digital transformation of citiesOpenAlex
Ioannis Tsampoulatidis, Dimitrios Bechtsis, Ioannis Kompatsiaris
The smart city concept gives prominence to the use of ICT for enabling the digital transformation of established public services, processes and policies. This roadmap started from the e-Gov 1.0 initiative and spans to the e-Gov 3.0 phase with special focus on interconnected citizens, smart and interconnected devices, big data analytics and cloud computing. As the smart cities initiative flourishes there is an overwhelming need for robust, secure and flexible solutions that will pave the way to citizens’ participation and inclusion. The proposed Blockchain Framework will not only facilitate acceptance of new governance models from the official stakeholders, but it will also dynamically identify, create and propose new policies, regulations and initiatives according to social trends and the citizens’ maturity level. It takes into consideration social, environmental and economic aspects as well as established policies and good practices for proposing solutions to stakeholders.
65Different Levels of Smart and Sustainable Cities Construction Using e-Participation Tools in European and Central Asian CountriesOpenAlex
Laura Alcaide Muñoz, Manuel Pedro Rodríguez Bolívar
Cities are developing strategies to deal with the complex challenges of global change and sustainability. These initiatives have involved the implementation of Information and Communication Technologies (ICTs) as a good driver for achieving sustainability because digital transformation can boost sustainable development strategies, providing opportunities to accelerate transformation. Smart City (SC) models built on empowering people in making public decisions favor access to sustainable development solutions based on knowledge and innovation. Nonetheless, SC experiences around the world denote divergent conceptions of SCs which could lead to different SCs construction. It deserves a more thorough understanding of the nature of collaboration in different settings. Therefore, this paper contributes to the debate on the different uses of ICTs in SCs construction in developing vs. developed countries, by examining the use of ICTs for creating collaborative environments in a sample of SCs in different countries, depending on their economic level, and seeking to identify differences in the objectives pursued by city governments with the use of these technologies. To achieve this aim, e-participation platforms, apps or social media platforms (European and Central Asia SCs) are examined for identifying SCs construction differences between developed vs. developing countries. The findings of this paper put an emphasis on the need for taking into account the differences among SCs in developed vs. developing countries when raking or when performance measurement is designed, because the assessment should be tailored to the cities’ particular visions and priorities for achieving their objectives.
66Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScienceOpenAlex
Auriol Degbelo, Carlos Granell, Sergio Trilles, et al.
The holy grail of smart cities is an integrated, sustainable approach to improve the efficiency of the city’s operations and the quality of life of citizens. At the heart of this vision is the citizen, who is the primary beneficiary of smart city initiatives, either directly or indirectly. Despite the recent surge of research and smart cities initiatives in practice, there are still a number of challenges to overcome in realizing this vision. This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens’ data literacy, the pairing of quantitative and qualitative data, the need for open standards, the development of personal services, and the development of persuasive interfaces. The article furthermore advocates the use of methods and techniques from GIScience to tackle these challenges, and presents the concept of an Open City Toolkit as a way of transferring insights and solutions from GIScience to smart cities.
67Developing a Collective Awareness Platform for Urban Sustainability Challenges: Case Study of the POWER ProjectOpenAlex
Mathias Becker Ksenia Koroleva, Kalina Drenska Diogo Vitorino, Jasminko Novak
In this paper we describe the socio-technical approach developed in the POWER project extending traditional forms of citizen engagement for local sustainability challenges with a collective awareness platform in order to increase public awareness, knowledge and engagement. We show how a citizen-centered design and implementation process integrating different technological enablers such as gamification, real-time open data integration or knowledge visualization results in a platform for sustainability issues that can drive collective awareness and collaborative knowledge sharing. Against the background of four different pilot cities with distinct water-related sustainability challenges, we present the implementation of the local platforms and how these have been effectively used by almost 1.000 active users, supporting an innovative engagement model that employed collaborative open innovation in online and offline settings for citizen-driven solution development to local sustainability challenges. We present the results from three series of citizen workshops where 150 participants provided valuable feedback, which was integrated in the further platform improvement. The evaluation confirmed its usability, enabling platform uptake among the target groups and its social impact by increasing awareness and knowledge. Finally, we derive a set of implications for similar initiatives addressing sustainability challenges helping them to overcome common barriers to participation and engagement.Keywords: citizen engagement, digital citizenship, sustainable urbanism, education, governance sustainability, gamification, water conservation, disaster risk reduction
68An Ambidextrous AI Governance Framework for Smart Cities to Enhance IT Governance and Data SecurityOpenAlex
Agustinus Fritz Wijaya, A’ang Subiyakto, Adhe Ronny Julians, et al.
This study proposes an ambidextrous AI governance framework grounded in COBIT 2019 to guide the deployment of artificial intelligence in smart city infrastructures while ensuring robust IT governance and data security. The framework blends exploration‑exploitative governance strategies to support innovation in smart urban management, while maintaining accountability, risk control, and regulatory compliance. State‑of‑the‑art review includes AI governance adaptations of COBIT 2019. The study achieved measurable improvements in governance maturity, with notable increases such as a 69.2% enhancement in DSS05 (Manage Security Services) and a 60.7% improvement in APO12 (Managed Risk). The literature review was based on a targeted analysis of peer-reviewed sources published between 2020 and 2025, ensuring relevance to AI governance in smart cities. Keywords are arranged from general to specific to improve indexing and clarity: AI Governance, Smart Cities, COBIT 2019, Ambidextrous Governance, Data Security.
69Digital Transformation and Data Governance: Strategies for Regulatory Compliance and Secure AI-Driven Business OperationsOpenAlex
James Paul Onoja, Oladimeji Hamza, Anuoluwapo Collins, et al.
Digital transformation has redefined business operations, driving efficiency, innovation, and competitiveness through artificial intelligence (AI) and advanced analytics. However, the rapid adoption of AI-driven processes introduces significant regulatory and security challenges, necessitating robust data governance frameworks to ensure compliance, mitigate risks, and protect sensitive information. This study explores the intersection of digital transformation and data governance, highlighting strategies for regulatory compliance and secure AI-driven business operations. The paper first examines the evolving landscape of AI regulation, emphasizing global frameworks such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and emerging AI governance policies. It underscores the critical role of compliance in mitigating data privacy concerns, ensuring transparency, and fostering ethical AI implementation. Next, the study explores data governance strategies essential for AI-driven enterprises. These strategies include data classification, access control mechanisms, encryption protocols, and real-time auditing to enhance data integrity and security. The importance of explainable AI (XAI) is also discussed, demonstrating how organizations can achieve regulatory alignment while maintaining AI model interpretability. Furthermore, the research highlights best practices for integrating digital transformation initiatives with data governance frameworks. It presents case studies on AI-driven businesses that have successfully implemented compliance-driven operational models, showcasing how enterprises can balance innovation with regulatory adherence. Key elements such as risk-based approaches, third-party data audits, and compliance automation tools are analyzed. Finally, the paper provides insights into future trends in AI governance, predicting the increasing convergence of digital transformation, AI ethics, and regulatory policies. As AI adoption accelerates, enterprises must adopt proactive data governance frameworks to address security vulnerabilities, regulatory obligations, and ethical considerations. This study serves as a comprehensive guide for organizations navigating the complexities of digital transformation while ensuring data security, regulatory compliance, and responsible AI implementation. By integrating strategic data governance practices, businesses can unlock AI's full potential while safeguarding consumer trust and regulatory alignment.
70AI integration in financial services: a systematic review of trends and regulatory challengesOpenAlex
Darko Vuković, Senanu Dekpo-Adza, Stefana Matović
The integration of Artificial Intelligence (AI) into financial services represents a developmental shift in the industry, presenting unprecedented opportunities and challenges. This scientometric review examines the evolution of AI in finance from 1989 to 2024, analyzing its pivotal applications in credit scoring, fraud detection, digital insurance, robo-advisory services, and financial inclusion. The analysis reveals significant trends, particularly the growing adoption of machine learning, natural language processing, and blockchain technologies in reshaping financial operations and decision-making processes. The review addresses critical regulatory and ethical challenges, emphasizing the imperative for explainable AI (XAI) and robust governance frameworks to ensure transparency, fairness, and accountability in AI-driven systems. Despite rapid advancements, persistent gaps remain, the most notable of which is the lack of standardized frameworks for AI implementation across financial sectors. The findings support the need for a balanced approach that promotes innovation while addressing ethical, regulatory, and societal concerns. This comprehensive synthesis maps the trajectory of AI in finance, identifies key areas for future research, and recommends interdisciplinary collaboration to advance responsible and sustainable AI integration within the financial ecosystem.
71Research and application of digital twin technology in smart cityOpenAlex
Tao Yang, Wen Gao
Digital twin is a forward-looking notion in digital governance and the development direction of smart cities. Digital twin technology, as a novel technological means, offers favorable support for decision-making and prediction in smart city construction by simulating real-world information. This paper commences from the concept formation of digital twin smart cities, elaborates on the research and application of its technology based on practical practices, and provides application strategies in traffic management, energy systems, environmental protection detection, and public safety. This approach can achieve real-time monitoring and optimization of urban operational status, promote intelligent and efficient urban management, and offer a new concept for the development and construction of smart cities.
72DAE-YOLO: An IoT-Optimized UAV Edge Network for Real-Time Small Target Detection in Smart City SurveillanceOpenAlex
Yuxin Wu, Yuan Chen, Zhiyong Tan, et al.
Small object detection in urban environments is crucial for UAV-edge networks, supporting smart city surveillance and privacy protection industrial Internet of Things systems. To address limitations like sub-100-pixel targets, motion blur, and occlusion, this paper proposes an IoT-optimized lightweight framework named Dynamic Adaptive Enhancement YOLO (DAE-YOLO).The framework integrates three key components: an adaptive resolution attention (ARA) mechanism for efficient focus on critical zones, A hybrid CSP Bottleneck with 2 Convolutions integrated with BiFormerBlock (C2fBiFormer) module combining multi-scale feature extraction with deformable transformer-based context modeling, and The Inner IoU Loss function (Inner-IoU) for occlusion-robust localization. When benchmarked on DOTA-v1.0 datasets, DAE-YOLO is shown to achieve 61.0% mAP@0.5 (0.5% higher than YOLOv8n) and 71.5% classification accuracy (5.5% higher), with 8.0 GFLOPs and 6.2MB model size. Sub-30ms latency is demonstrated in field validation for detecting traffic violations, structural defects, and unauthorized intrusions in smart city deployments. The proposed framework bridges UAV edge computing with industrial-grade reliability, establishing a federated-ready real-time perception paradigm for urban digital governance and IIoT ecosystems.
73Building a Secure Platform for Digital Governance Interoperability and Data Exchange Using Blockchain and Deep Learning-Based FrameworksOpenAlex
Varun Malik, Ruchi Mittal, Dinesh Mavaluru, et al.
A secured platform is a critical component of digital governance, as it helps to ensure the privacy, security, and reliability of the electronic platforms and systems used to manage and deliver public services. Interoperability and data exchange are essential for digital governance, as they enable different government agencies and departments to share data, information, and resources seamlessly, regardless of the platforms and technologies they use. In this paper, we build a secure platform to enhance the trustworthiness of digital governance interoperability and data exchange using blockchain and deep learning-based frameworks. Initially, an optimal blockchain leveraging approach is designed using the bonobo optimization algorithm to authenticate data generated from smart city environments. Furthermore, we introduce the integration of a lightweight Feistel structure with optimal operations to enhance privacy preservation. This integration provides two levels of security and ensures interoperability and double-secured data exchange in digital governance systems. In addition, we utilize a deep reinforcement learning (DRL) model to detect and prevent intrusions such as fraud/corruption in the smart city data. This approach enhances transparency and accountability in accessing the data and shows its predominance over other cutting-edge techniques on two benchmark datasets, BoT-IoT and ToN-IoT. Furthermore, the effectiveness of the framework in real-time scenarios has been demonstrated through two case studies. Overall, our proposed framework provides a trustworthy platform for digital governance, interoperability, and data exchange, addressing the challenges of privacy, security, and reliability in managing and delivering public services.
74AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and RecommendationsOpenAlex
Luciano Floridi, Josh Cowls, Monica Beltrametti, et al.
This article reports the findings of AI4People, an Atomium-EISMD initiative designed to lay the foundations for a "Good AI Society". We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations-to assess, to develop, to incentivise, and to support good AI-which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
75The Ethics of AI Ethics: An Evaluation of GuidelinesOpenAlex
Thilo Hagendorff
Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved.
76A HEALTHY SOCIETY: SOCIAL CHALLENGES OF DIGITALIZATION AND THE WAYS TO OVERCOME THEM (THE UKRAINIAN EXPERIENCE).PubMed
Svіtlana Storozhuk, Andrii Petraniuk, Andrii Lenov, et al.
Wiad Lek. 2023;76(9):2103-2111. doi: 10.36740/WLek202309129.
OBJECTIVE: The aim: The article examines the peculiarities of the Ukrainian state policy in the field of digitalization, reveals the social challenges caused by this phenom¬enon, and outlines the ways to overcome them. PATIENTS AND METHODS: Materials and Methods: The data collection was carried out using PubMed, Scopus, Google Scholar databases. Research papers were identified according to the search terms: "digitalization", "digital transformations", "Internet", "digital services", "smart city", "smart urbanization", "inclusion", "social exclusion", "community mental health", "volunteering", "social partnership". The authors analyzed international and domestic official strategies, programs, and messages along with statistical data and social surveys conducted by foreign and Ukrainian institutions, public organizations, and analytical centers. The authors used the interdisciplinary approach along with the principles of objectivity, tolerance, and impartiality, and general scientific methods, such as induction, deduction, generalization, etc. CONCLUSION: Conclusion: The rapid spread of digital technologies is associated with the growth of social cohesion, inclusion, solidarity, and the development of a healthy harmonious society that will provide all the conditions for a decent life for a human being and the comprehensive development of his/her abilities and talents. These hopes are not groundless, because digitalization is accompanied by a number of structural shifts in economics and public administration, which contribute to overcoming subjectivity in making management decisions and increasing the level of "intellectualization" of the environment. In addition, digitalization is becoming a significant driver of the sustainable growth in labor productivity, employment levels, personal and social well-being; and the spread of digital technologies provides an opportunity to overcome various social challenges. As the Ukrainian experience reveals, despite a number of positive shifts, digitalization can also give rise to destructive social trends, among which the digital gaps caused by the uneven access to digital technologies and services occupy a special place. People in the city outskirts, small towns, and especially in the remote rural areas often have extremely limited access to the Internet that significantly reduces their social opportunities. These problems became more acute after the full-scale invasion of the russian federation into Ukraine. The occupation of the part of Ukraine, hostilities, and missile attacks damaged the energy sector blocking telecommunication networks, which led to the social exclusion of a significant part of the population in some Ukrainian regions. The harsh living conditions during the war, the social exclusion as a result of the occupation, as well as the destruction of energy infrastructure and civilian objects fueled the activities of the Ministry of Digital Transformation of Ukraine. The proposed services and transformations provided social opportunities for a part of the population, while remaining unable to overcome social exclusion generated by the digital, social, or other gap. Volunteers and social activists usually help to bridge the gap and maintain mental health of the community, which has been suffering from the horrors of the war for more than a year. Their activities and public position lay the ground for the establishment of social partnership aimed at the harmonious development of every individual and the community as a whole.