Developing an optimal algorithm for demand responsive feeder transit service accommodating temporary stops

Abstract

Demand responsive feeder transit can minimize passengers’ travel times and operator’s costs by optimizing routing based on real demand. One question about the demand response feeder transit operation is whether it can be optimized with door-to-door service or with temporary stops for picking up and delivering passengers. Obviously, door-to-door service eliminates passengers’ walking distances, but it increases passenger in-vehicle travel times and vehicle operating distance and costs. On the other hand, demand responsive feeder transit with temporary stops, which designates the temporary locations picking up and dropping passengers, minimizes bus operating distance and time as well as passenger in-vehicle times, although it increases passengers’ walking distances and times. The developed model uses metaheuristic approaches, including two main algorithms; a passenger’s clustering algorithm based on Particle swarm optimization (PSO) approach and a vehicle routing algorithm that uses simulated annealing (SA) solving method. The algorithm developed an optimal algorithm for clustering and grouping of passengers considering their physical locations and time windows then it was integrated with the authors’ previously developed algorithm for the optimal flexible feeder bus routing as a mixed integer model that objects to minimize the total costs including both passengers traveling times and operator’s operating costs The results of this study showed that although feeder networks with temporary stops always lower operating costs and lessen in-vehicle travel time compared to those with a door-to-door option, the total costs and optimal routings are highly sensitive to the location of passengers.