国家自然科学基金(62002117, 61862023); 江西省自然科学基金重点项目(20202ACBL202009); 江西省教育厅科技项目(GJJ190325, GJJ200627)
为了缓解城市交通拥堵、避免交通事故的发生, 城市路网的路径选择一直以来是一个热门的研究课题. 随着边缘计算和车辆智能终端技术的发展, 城市路网中的行驶车辆从自组织网络朝着车联网(Internet of vehicles, IoV)范式过渡, 这使得车辆路径选择问题从基于静态历史交通数据的计算向实时交通信息计算转变. 在城市路网路径选择问题上, 众多学者的研究主要聚焦如何提高出行效率, 减少出行时间等. 然而这些研究并没有考虑所选路径是否存在风险等问题. 基于以上问题, 首次构造了一个基于边缘计算技术的道路风险实时评估模型(real-time road risk assessment model based on edge computing, R3A-EC), 并提出基于该模型的城市路网实时路径选择方法(real-time route selection method based on risk assessment, R2S-RA). R3A-EC模型利用边缘计算技术的低延迟, 高可靠性等特点对城市道路进行实时风险评估, 并利用最小风险贝叶斯决策验证道路是否存在风险问题, 最后在此基础上对城市路网路径选择进行优化, 实现实时动态低风险的路径选择方法. 实验通过与传统的最短路径Dijkstra算法、基于VANET的最短时间算法、基于MEC的动态路径规划算法以及双向A*最短路径优化算法对比, 得出R2S-RA方法可以更好地选择兼顾道路风险和行驶时间的优化路径, 从而大大减少交通拥堵和交通事故等事件的发生.
In order to alleviate urban traffic congestion and avoid the traffic accident, the route selection in urban road networks has been a hot research topic. With the development of edge computing and vehicle intelligent terminal technology, driving vehicles in urban road network are transiting from self-organizing network to Internet of vehicles (IoV) paradigm, which makes the route selection of vehicles change the computation based on static historic traffic data to real-time traffic information. In the current research on the route selection in urban road networks, many scholars focus on how to improve the efficiency of travel, reduce travel time, etc. Nevertheless, these studies do not consider the possible risk on the selected route. Based on the above issues, this study constructs a real-time road risk assessment model based on edge computing (R3A-EC) for the first time. Besides, it proposes a real-time route selection method based on risk assessment (R2S-RA). The R3A-EC model makes full use of the characteristics of low latency and high reliability of the edge computing technology to assess the risk on the urban road in real time, and uses the minimum risk Bayes decision making to validate whether there is a risk. Finally, based on the real-time risk assessment model, the route selection of urban road network is optimized to realize the real-time dynamic and low-risk route selection method. Compared with the traditional shortest path method Dijkstra and the shortest time method based on VANET, the dynamic path planning algorithm based on MEC and the bidirectional A* shortest path optimization algorithm, the proposed R2S-RA method can better choose the optimal route that takes road risk and travel time into account, so as to reduce the occurrence of traffic congestion and traffic accidents.