考虑乘客出行体验的需求响应式公交规划

作为传统公交车的有力补充,需求响应式公交的出现为人们提供了解决问题的新思路,它能够即时采集乘客出行需求信息,确定走行路线,提供个性化定制服务。但自需求响应式公交运营以来,步行距离长、候车时间久等问题也日益凸显,极大地影响了乘客的出行体验。文章充分考虑乘客的步行距离及等待时间成本,基于DBSCAN算法、K-means算法,就需求响应式公交合乘站点布设问题进行研究,采用启发式插入算法对建立的软时间窗、多车队模型进行求解。可以实现对具有时间窗空间分散点的聚类及路径规划,对优化需求响应式公交的乘客出行体验,提高车辆上座率具有重要意义。

Demand responsive bus planning considering passenger travel experience

As a powerful complement to the traditional bus,the emergence of demand-responsive bus provides people with new ideas to solve the problem.It can collect passengers'travel demand information instantly,determine the route,and provide personalized customized services.However,since the demand-responsive bus operation,problems such as long walking distance and long waiting time have become increasingly prominent,greatly affecting the travel experience of passengers.Considering the passenger's walking distance and waiting time cost,this paper studies the layout of demand-responsive bus stops based on DBSCAN algorithm and K-means algorithm,and uses heuristic insertion algorithm to solve the soft time window and multi-fleet model.It can realize clustering and path planning of spatial dispersed points with time windows,optimize passenger travel experience of demand-responsive bus,and improve vehicle occupancy rate.