A chance-constrained dial-a-ride problem with utility-maximizing demand and multiple pricing structures

TL;DR

This paper introduces a chance-constrained dial-a-ride model incorporating user utility maximization and multiple pricing strategies, providing a new decision-support tool for revenue and fleet management in demand-responsive transportation.

Contribution

It develops a novel mixed-integer programming model for CC-DARP with utility-based user preferences and pricing structures, solved via a custom local search heuristic.

Findings

Zonal fare structure maximizes revenue and ridership.

Heuristic achieves an average optimality gap of 2.69%.

Model effectively captures long-term user preferences.

Abstract

The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing that accommodates a set of user requests under constraints at an operations planning level, where users' preferences and revenue management are often overlooked. In this paper, we present a mechanism for accepting/rejecting user requests in a Demand Responsive Transportation (DRT) context based on the representative utilities of alternative transportation modes. We consider utility-maximizing users and propose a mixed-integer programming formulation for a Chance Constrained DARP (CC-DARP), that captures users' preferences in the long run via a Logit model. We further introduce class-based user groups and consider various pricing structures for DRT services. A customised local search based heuristic is developed to solve the proposed CC-DARP. We report numerical results for both DARP benchmarking instances and a realistic case study based on New York City yellow taxi trip data. Computational experiments performed on 105 benchmarking instances with up to 96 nodes yield an average optimality gap of 2.69% using the proposed local search heuristic. The results obtained on the realistic case study reveal that a zonal fare structure is the best strategy in terms of optimising revenue and ridership. The proposed CC-DARP formulation provides a new decision-support tool to inform on revenue and fleet management for DRT systems at a strategic planning level.