实时信息下共线公交线路发车时刻表的协同优化

Collaborative Optimization of Departure Timetable for Common Bus Lines Under Real-Time Information

为改善实时信息下共线公交线路的运营服务水平,提出了一种实时信息下的共线公交线路发车时刻表优化模型。首先,基于时变的客流需求和路况信息,考虑实时信息下乘客出行行为动态变化导致的共线线路客流分布变化,以乘客出行成本、公交企业运营成本为优化目标建立共线公交线路发车时刻表协同优化模型。然后,选择基于动态拓扑结构的改进粒子群优化算法(PSO-DT)对模型进行求解。最后,以大连市两条共线线路为例进行分析,给出了优化后的发车时刻表、车辆运行轨迹图,分析了有无实时信息下客流分布的差异。结果表明:与现有方案相比,文中方案可使乘客出行成本降低12.6%,公交公司运营成本降低8.3%,总成本降低12.3%,发车次数整体上减少3次,乘客平均承载量增加3人,瞬时最高承载量减少4人,各车次承载量标准差降低2.192人,优化后线路服务水平有所提升,各车次的载客量分布更加均衡,说明文中模型是有效的。

In order to improve the level-of-service of common bus line operation under real-time information,this paper proposed an optimization model of common line departure schedule based on real-time information.Firstly,it considered the change of passenger flow distribution caused by the dynamic change of passenger travel behavior under real-time information based on the time-varying passenger flow demand and real-time road condition information,and established the optimization model of departure schedule of common bus lines with the optimization objectives of passenger travel cost and bus enterprise operation cost.Then,it selected the improved particle swarm optimization algorithm(PSO-DT)based on dynamic topology to solve the model.Based on the analysis of two common lines in Dalian city,the optimized departure schedule and vehicle trajectory were given and the difference of pa-ssenger flow distribution with and without real-time information was analyzed.The results show that,as compared with the current scheme,the proposed scheme can reduce the travel cost of passengers by 12.6%,the operating cost of bus companies by 8.3%,the total cost by 12.3%,the number of bus departure by 3,the instantaneous maximum carrying capacity by 4 people,and the standard deviation of carrying capacity of each train by 2.192 people,and increase the average carrying capacity of passengers by 3 people.Therefore,the level-of-service of the optimized line is improved,and the passenger flow distribution among vehicles is more balanced,which verifies the effectiveness of the proposed model.