Department of Computer Engineering, Institute of Information Science Technology, Jeju National University, Jejusi 63243, Korea.
Sensors (Basel). 2021 May 11;21(10):3314. doi: 10.3390/s21103314.
The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system's efficacy and performance compared with other state-of-art models.
出租车需求服务的预测已成为智能交通系统中一个具有大规模和潜在应用的热门研究领域。需求过程分为两大部分：基于乘客习惯的接送需求。出租车需求预测对司机和乘客来说都是一个很好的概念，也是为打车和市政管理人员设计的平台。大多数研究都集中在预测需求服务的接送部分，并指定空间和时间相关性的互联上。在本研究中，主要重点是克服非注册用户访问点，这些用户使用出租车服务进行虚假交易，并预测出租车接送信息。将机器学习技术和区块链框架集成被认为是解决这个问题的一种可能的方法。区块链技术被选为保护和控制实时系统的有效技术。通过提取时间间隔的三个更高相关部分（接近度和趋势）来处理历史数据分析。接下来，基于构建多任务学习和时空特征提取的组件，处理出租车接送预测任务。通过融合历史时空特征，特征嵌入性能和长短期记忆（LSTM）的组合获取接送相关性。最后，基于外部因素的组合来处理出租车接送预测。实验结果基于韩国济州岛的真实数据集，以显示与其他最先进模型相比，所提出系统的有效性和性能。
