随机路网拥堵程度频繁变化的步进式诱导算法

Step-by-step Guidance Algorithm for the Frequently Changing Congestion Degree of Stochastic Road Networks

随机路网拥堵程度频繁变化时,车辆所处路段或交叉口拥堵程度的变化可能直接导致车辆遭遇拥堵,使诱导失效.针对这一问题,首先,以拥堵程度时变的随机路网可变元胞传输模型为基础,进行随机路网模型的拥堵程度频繁变化分析,并建立了拥堵程度变化频次指标.之后,以模拟导弹制导的时间最短路径算法为基础,建立了适用于路网拥堵程度频繁变化情况的步进式诱导算法.针对北京地区的部分路网进行仿真,分为随机路网模型的拥堵程度频繁变化验证和步进式诱导算法验证.仿真结果表明,随机路网模型的拥堵程度变化频次和时间具有随机性;步进式诱导算法更适用于路网拥堵程度频繁变化、起讫点距离远、路网路段长的情况.

When the congestion degree of stochastic road networks changes frequently, shifts in the congestion degree of roads or intersections may lead directly to vehicle congestion. This occurrence can cause guidance algorithms to fail. To address this problem, the frequent changes in the congestion degree of stochastic road networks are first analyzed with the use of a novel variable cell transmission model for stochastic road networks with a time-varying congestion degree. Moreover, a frequency index is established for changes in congestion degree. Second, a step-by-step guidance algorithm is established for the frequently changing congestion degree of stochastic road networks based on the least-time path algorithm. Part of a road network in Beijing is used in the simulation, which validates the frequent changes in the congestion degree of stochastic road networks and the step-by-step guidance algorithm. The simulation results indicate that the change frequency and time of the congestion degree of stochastic road networks are random. Furthermore, the step-by-step guidance algorithm is suitable for investigating the frequently changing congestion degree of stochastic road networks, the long distances between origins and destinations, and long road sections.