Demand-Anticipatory Flexible Public Transport Service

Development in vehicle automation and the shared economy contribute to a growing interest in introducing flexible services. This paper investigates how archived passenger data can be used in the form of demand forecasts to improve routing of vehicles in a Dail-a-Ride problem. Two improvements to the Dail-a-Ride problem are investigated. Empty vehicle rerouting deals with the relocation of idle vehicles. We also introduce a new type of online dynamic insertion algorithm with demand forecasts, which can be incorporate demand forecasts in determining which vehicle serves which passenger.
The performance of these algorithms is tested in a simulation model for a case study network in the Netherlands. The inclusion of empty vehicle rerouting reduces passenger rejections by 98% and reduces passenger travel and waiting times by 5 and 16% respectively. This induces an increase in vehicle distance driven per passenger by 25%. The insertion algorithm with demand forecasts reduces travel and waiting times by 6 and 30% respectively, with only a very minor increase in vehicle distance driven. The main conclusion of this paper is that if both measures are applied simultaneously, the strength of both are combined. Passenger rejections are all but eliminated, while travel and waiting time are reduced by up to 10 and 50% respectively. This causes a 25% increase in vehicle distance driven per passenger. A sensitivity analysis tested the robustness of the proposed routing measures to variations in operational and demand conditions. Demand forecasts proved to be a very strong instrument in improving the solution to Dail-a-Ride problems.