共享单车影响下接驳公交线路设计与车辆配置方法

针对轨道交通的“第一/最后一公里”问题，接驳公交和共享单车是通勤用户最常选择的两种公共交通方式。为理解共享单车对接驳公交出行需求和线路设计等规划运营方面的影响，提出供需交互状态下的接驳公交线路设计与车辆配置模型。需求端考虑出行时间和出行费用，基于用户在共享单车和接驳公交之间的模式选择行为，动态计算接驳公交实际出行需求；供应端考虑车辆容量、数量和流平衡约束，以最小化公交运营成本和用户出行成本之和为目标，建立混合整数非线性规划模型，优化接驳公交线路设计及车辆配置。模型采用拉格朗日松弛算法进行求解。该方法应用于北京市回龙观地铁站周边出行小区接驳公交线路设计，公交及单车出行需求采用真实的IC卡数据，以及摩拜单车骑行数据，站点间行驶时长采用高德驾车路径规划API(Application Programming Interface)数据。实验结果表明，车辆总数为10，线路数量为2时，考虑共享单车影响的接驳公交规划模型相较于只考虑单一模式可以有效避免规划需求误差。此时，各站点到地铁站的平均运行时间是15.58 min，乘客平均等待时间是3.35 min；在线路数量为4时，各站点到地铁站的平均运行时间...

Feeder Bus Route Design and Vehicle Allocation Under Influence of Shared Bikes

For the "first-and last-mile" of rail transit, feeder buses and shared bikes are two most prevalent modes to provide connection with rail transit for commuters. To understand the impact of bike-sharing on the planning and operation of feeder bus travel demand and route design, this study examines the feeder bus route design and vehicle allocation challenges based on the interaction of demand and supply. From the demand side, the actual travel demand of feeder buses is dynamically estimated depending on the user's mode choice between shared bikes and feeder buses, considering the travel time and travel cost. Comparatively, a mixed-integer non-linear programming model with the objective of minimizing the sum of bus operating cost and user travel cost is developed from the supply perspective to optimize the bus route design and vehicle allocation, including vehicle capacity, vehicle quantity, and flow balance constraints. The Lagrangian relaxation algorithm is used to solve the model. This strategy is applied to the planning of feeder bus routes in the Beijing suburbs surrounding the Huilongguan Metro Station. The actual smart card data and Mobike cycling data are used to obtain the total travel demand. The travel time by various modes between stops is derived from the AutoNavi route planning API (Application Programming Interface). In the case where the total number of vehicles is 10 and the number of lines is 2, the experimental results show that the difference between the assumed bus travel demand and the computed bus ridership can be effectively avoided if the influence of bike-sharing on bus travel demand is considered. The average running time between each bus stop and the station is 15.58 minutes, while the average passenger waiting time is 3.35 minutes. In the case where there are four lines, the average running time from each bus stop to the station is 8.53 minutes, which is almost half of the case with only two lines; the average waiting time for passengers is 3.44 minutes. Nonetheless, the computing time for the model grows exponentially with the increasing of the number of lines. Consequently, from the perspective of model calculation efficiency, both scenarios in which the number of lines is set to 2 or 3 can satisfy the application requirement of updating lines every half hour.