Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya, 08018 Barcelona, Spain.
Smurfit Business School, University College Dublin, Blackrock, D04 V1W8 Dublin, Ireland.
Sensors (Basel). 2023 Jan 2;23(1):499. doi: 10.3390/s23010499.
Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an 'Internet of vehicles' with the potential to significantly enhance citizens' mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.
车联网(VANETs)是智慧城市智能交通系统的基本组成部分。在开放和实时数据的支持下，这些互联车辆网络构成了一个“车联网”，有可能显著提高城市、郊区和大都市区居民的流动性和最后一英里的配送效率。然而，VANETs 的协调和物流管理提出了许多需要解决的优化挑战。在回顾了车联网优化和智慧城市开放数据的现状后，本文讨论了该领域一些最相关的优化挑战。由于大多数优化问题都与实时解决方案的需求或不确定性和动态环境的考虑有关，因此本文还讨论了如何使用敏捷优化算法以及将元启发式算法与仿真和机器学习方法相结合来解决一些 VANET 挑战。本文还提供了一项数值分析，该分析衡量了在一些相关问题中使用这些优化技术的效果。我们的数值分析基于巴塞罗那开放数据的真实数据，结果表明，在 CDP 与车辆网络相结合的情况下，构造性启发式算法优于随机方案，从而在满足容量要求的情况下，最大化设施之间的最小距离，同时使用最少的设施。
