Manual driving, automatic driving and public transportation are three types of important travel modes forming a future traffic network, and the existing road congestion charging theory is rarely researched on the basis of simultaneously relating to the multi-mode travel modes. The multi-mode traffic network including manual driving, automatic driving and public traffic is established, the strategy travel behaviors of travelers in the network and the characteristics of each travel mode are considered, the travel strategies of various travelers are further designed, the strategies are represented by using the over-path structure, and the travel speed of the travelers is improved. A traffic flow balanced distribution model under a mixed travel behavior is constructed based on a strategy, a brand new road congestion charging double-layer planning model is finally established, the balanced distribution model serves as a lower-layer model, the target of the upper-layer model is that the difference between the total network travel time and the total income is minimum, and a model solving method is designed based on a genetic algorithm, an MSA and other algorithms. The test result shows that the provided model and algorithm can better realize the congestion charging target in the multi-mode traffic network.
人工驾驶、自动驾驶和公共交通是构成未来交通网络的三类重要的出行方式,现有的道路拥挤收费理论极少在同时涉及上述多方式出行方式的基础上进行研究。本发明建立了包含人工驾驶、自动驾驶、公共交通的多模式交通网络,考虑了出行者在网络中策略出行行为以及每种出行方式的特征,进一步设计了各类出行者的出行策略,利用超路径的结构表示策略,基于策略构建混合出行行为下的交通流均衡分配模型,最终建立一种全新的道路拥挤收费双层规划模型,均衡分配模型作为下层模型,上层模型目标为网络总出行时间与总收益之差最小,并基于遗传算法、MSA等算法设计模型求解方法,测试结果显示所提出的模型和算法能够较好地实现多模式交通网络中拥挤收费目标。
Policy-based road congestion charging method in multi-mode traffic network
多模式交通网络中基于策略的道路拥挤收费方法
25.10.2024
Patent
Elektronische Ressource
Chinesisch
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