响应动态需求的灵活型公交路径优化调度模型

    Flexible Bus Route Optimal Scheduling Model in Response to Dynamic Demand

    • 摘要: 为解决灵活公交乘客需求差异性大、实时变化性大的问题,提出一种考虑乘客动态需求的灵活公交路径优化调度模型.在已知乘客预约需求量、车辆载客容量、车队规模等条件下,根据乘客需求动态变化特征对接驳行程时间进行实时迭代更新,将车辆的运营成本(车辆行驶时间)和乘客的时间成本(乘客上车前等待车辆的时间、实际到达时间与期望到达时间之间的差值)最小化作为目标,构建了考虑乘客动态需求的灵活型公交路径优化调度模型,并采用基于引力模型的启发式算法进行求解.最后,通过实例分析验证了模型和算法的可行性.结果表明:对随机产生的15个需求点的102个出行需求,全部服务完成所需车辆为17~21辆,平均每辆车的旅行时间为24.59 min,100组数据的求解时间均在25.00 s以内,计算耗时平均为12.04 s.可见该优化模型能够在实时调整接驳规划时间的前提下,更大程度满足乘客动态需求,有效减小规划路径的误差,缩短行车距离和乘客出行时间,相比忽略接驳行程时间变化的灵活公交调度模型结果更优.

       

      Abstract: To solve the problem that the demand of flexible bus passengers varies significantly and the demand of flexible bus passengers varies significantly in real time, a flexible bus route optimization scheduling model considering the dynamic demand of passengers was proposed. Under the conditions of known passenger reservation demand, vehicle passenger capacity and the team known condition such as size, according to the dynamic changes of passenger demand for real-time iterative update shuttle travel time, the operating costs of the vehicle (vehicle) and time cost for passengers before (the passengers waiting time of the vehicle, the actual time of arrival and the difference in value between expected time of arrival) minimization as the target, was established considering the passenger dynamic demand type flexible bus route optimization scheduling model, and USES the heuristic algorithm based on gravity model. Finally, the feasibility of the model and algorithm was verified by an example. The analysis results show that for the 102 travel demands of 15 randomly generated demand points, the number of vehicles needed to complete all the services is 17-21, the average travel time of each vehicle is 24.59 minutes, the solution time of 100 sets of data is all within 25.00 seconds, and the average calculation time is 12.04 seconds. It can be seen that under the premise of real-time adjustment of connection planning time, this optimization model can satisfy the dynamic demand of passengers to a greater extent, effectively reduce the error of the planning path, shorten the driving distance and passenger travel time, and achieve better results than the flexible bus scheduling model that ignores the change of connection travel time.

       

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