不同信息模式下车辆多站点目标选择及影响分析

特定条件下，车辆能源补给行为会恶化道路交通状况，而交通信息引导、共享技术的应用为这一问题的解决提供了可能性.为分析和明确该应用的效果，本文以道路和多个能源供应站所构成的系统作为对象，通过系统和车辆行驶的特征分析，构建了基于多服务台多排队的干线多站点元胞自动机模型.不同信息共享模式下的模型实验表明：模型能够模拟需求车辆对目标站点的选择过程；信息共享时，需求车辆聚集程度能够得到缓解，进而延缓了交通流饱和状态的出现；站点信息共享的系统优化效果好于路况信息共享，而相应拥堵边界条件的分析也明确了信息共享介入的时机.

Vehicle’s Multi-objective Station Selection and Influence Based on Different Information Scenarios

The traffic flow might be influenced by the vehicle energy demand on some conditions, and the possibility of solving this problem is the application of traffic information sharing technology. To confirm the validity of the application, after analyzing of the driving features of vehicle in a multi-stations traffic system, a cellular automata model with multi-stations in urban trunk road based on the multi-server multi-queueing system is proposed in this paper. The numerical experiment based on different information sharing mode show that: the cellular automata model could simulate the vehicle’s selection process of target station; the clustering of vehicle is reduced by the application of traffic information sharing, then saturation state of traffic is delayed; the application of station information sharing performs better than the road information, and the application opportunity of different mode is confirmed by the analysis of boundary conditions.