Akabane Ademar Takeo, Immich Roger, Pazzi Richard Wenner, Madeira Edmundo Roberto Mauro, Villas Leandro Aparecido
Institute of Computing (IC), University of Campinas (UNICAMP), 1251 Albert Einstein Av., Campinas, SP 13083, Brazil.
Faculty of Business and Information Technology (FBIT), Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1H 7K4, Canada.
Sensors (Basel). 2019 Aug 15;19(16):3558. doi: 10.3390/s19163558.
Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency.
交通部门正在采用先进的交通管理系统（ATMS）来提高车辆交通管理效率。ATMS目前使用沿道路分布的智能交通信号灯和传感器来实现其目标。此外，还有其他一些有前景的技术可以更高效地应用，以取代上述技术，如车辆网络和5G。在ATMS中，由于在高度动态的网络中选择最合适的车辆存在困难，集中式检测拥堵和计算替代路线的方法是最常用的方法之一。这种方法的优点是在每次执行时都能充分考虑场景。另一方面，分布式解决方案需要事先对整个场景进行划分以选择车辆。此外，此类解决方案以自私的方式建议替代路线，这可能导致二次拥堵。这些未解决的问题激发了一种基于车辆社交网络（VSN）中的协作方法的分布式城市移动性管理系统的提议，名为SOPHIA。VSN范式是由移动通信设备及其在车辆环境中的社交关系整合而产生的。因此，社交网络分析（SNA）和社交网络概念（SNC）是可以在VSN中探索的两种方法。我们提出的解决方案以协作方式采用SNA和SNC方法进行替代路线规划。此外，我们使用动态聚类以分布式方式选择最合适的车辆。仿真结果证实，在车辆环境中结合使用SNA、SNC和动态聚类在提高系统可扩展性以及改善城市移动性管理效率方面具有巨大潜力。
