A Heuristic for Learn-and-Optimize New Mobility Services with Equity and Efficiency Metrics

Abstract

We are motivated by a common dilemma that exists when coupling new mobility services with existing transportation systems — the unbalanced nature of operations with regard to efficiency and equity objectives. To address this issue, we study the joint routing and resource allocation problem. Vehicles need to repeatedly and simultaneously choose the route and the resource (i.e., capacity) allocation policy with unknown demand. Efficiency is measured by the total travel distance, and equity is measured by the minimum service level. We propose a two-phase heuristic that solve the learn-and-optimize problem iteratively with small cumulative regret. In Phase 1, the algorithm selects the best demand estimator; In Phase 2, it finds the near optimal operational plan. We examine the effectiveness of the algorithm in a case study from the Miami Dade County that uses idle shuttle vehicles to deliver packages during off-peak hours. The results show that we can improve the minimum service level from 33% to approximately 68% while maintaining small incremental travel costs. This heuristic can provide a general guidance for practitioners and researchers on operating new mobility services in a stochastic network.