This paper concerns operational planning of door-to-door transportation systems for the elderly and/or disabled, who often need a more flexible transportation system than the rest of the population. Highly flexible, but very costly direct transportation is often offered as a complement to standard fixed route public transport service. In the integrated dial-a-ride problem (IDARP), these modes of transport are combined and certain legs of the passengers journeys may be performed with the fixed route public transport system. We extend the IDARP and include timetables for the fixed route services, forcing the fleet of vehicles to schedule the arrival at transfer locations with care. Two mixed integer linear programming formulations of the integrated dial-a-ride problem with timetables (IDARP-TT) are presented and analyzed. The key modeling challenge is that of the transfers between the fleet of vehicles and the fixed route public transport system. The formulations differ in how the transfers are modeled and the differences are thoroughly discussed. The computational study compares the formulations in terms of network size, computational time and memory usage and conclusions about their performances are drawn.
This work concerns evaluation of integrated demand responsive services, in which certain parts of the passengers' door-to-door journeys are served by fixed route public transport. The purpose of combining a special transport service with a fixed route service is to reduce the high, publicly subsidized, operational costs of the special transport service. To be able to recommend in which situations, or areas, it is beneficial to use an integrated service we present a metaheuristic for solving the routing problem in such a service. The metaheuristic can be implemented as part of an evaluation tool for policy makers and officials, providing insights into the effects of an integrated demand responsive service compared to a non-integrated. The metaheuristic is based on the adaptive large neighbourhood search (ALNS) framework. It is applied to a data set from a real-world, rural, demand responsive special transport service and the fixed route service available in the area. The objective used in our heuristic is to minimize the distance driven by the demand responsive vehicles. The distance driven is strongly related to the operational cost of the service. Our tests show that the distance driven by the demand responsive vehicles is reduced by 16%, giving a substantial cost reduction for the special transport service in the given area. Highlights. Evaluation of integrated demand responsive service. Combining special transport with a fixed route service to reduce operational costs. Metaheuristic, based on the adaptive large neighbourhood search (ALNS) framework. Comparing integrated service to non-integrated ARTICLE HISTORY
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