基于强化学习的限行政策下双模式出发时间选择仿真研究

A Simulation Study of Departure Time Selection in Dual-modal with Impacts of Vehicle Restriction Policies Based on Reinforcement Learning

结合多Agent方法和强化学习模型,建立了城市高峰时段通勤者出行方式及出发时间选择的计算机仿真模型。仿真研究了限行政策下通勤者的出行选择行为,再现了交通均衡的形成过程。根据仿真结果分析了不同公交改善措施的实施效果。结果表明限行政策实施后,公交出行人数增加18%,一定程度上缓解了高峰时段的拥堵状况,但也会导致出行者在非禁行日公交出行的概率减小,因此仅采取限行政策起到的作用是有限的。在小汽车限行政策下,提高公共交通发车频率,能够使公交出行人数增加17.5%,小汽车拥堵等待时间减少85%,有效地改善了道路交通状况,相比之下,降低公交价格的改善作用不明显。研究中采用的多Agent方法可以直观方便地描述丰富的个体行为,同时在描述个体行为与系统的互动方面具有一定的优势,为探索复杂交通现象的形成和演化过程提供了一种有效的途径。

Combining multi-agent technology with a reinforcement learning model,a computer simulation model of travel modes and choice of departure time of commuters in peak hours is established.In a simulation,travel choice behaviors of commuters are studied with the consideration of impacts of vehicle restriction policies,and the formation of commuting equilibrium in peak periods is also reproduced.Based on simulation results,the effects of different measures for improving public transportations are analyzed.The results show that the number of commuters by bus increases by 18%after the implementation of restriction policies,which eases congestions in peak periods to a certain extent.Meanwhile,the probabilities that commuters travel by bus in unrestricted days become smaller,which means the effects of adopting restriction policies exclusively are fairly limited.Under the influences of restriction policies,if departure frequencies of public transport increase,the number of commuters travel by bus increases by 17.5%,and drivers′waiting time in congestions decreases by 85%,which can effectively improve the traffic situations.Compared with that,reducing ticket price of public transport is less effective.The multi-agent approach applied in this study shows the richness in individual behaviors which can be realized intuitively and conveniently.It also has advantages in describing interactions between individuals and traffic systems,which provides an effective way to explore formation and evolution of complicated traffic phenomena.