Abstract

In major metropolitan areas, ride-sharing systems can help reduce traffic congestion and increase the transportation system's efficiency. In this paper, we propose a Branch-and-Price based approach for solving the ride-share routing problem with flexible pickup and drop-off points. We assume a ride-sharing system where drivers have their own origins and destinations, where all the drivers’ and passengers’ information is known beforehand, and all the problem data information is static and deterministic. We assume that drivers can pick up or drop off passengers from or to flexible meeting points that are within a passenger's walking time limit from their origin or destination and are determined on a continuous plane. We formulate a mixed integer nonlinear model for routing and selecting pickup and drop-off points. Our solution approach decomposes this problem in two: selecting pickup and drop-off points and a rideshare routing problem. We develop an efficient algorithm to select the best pickup and drop-off points and show computationally that it is more efficient at finding pickup and drop-off points than considering a fixed set of discrete meeting points. To evaluate the performance of our approach, we perform numerical experiments on a San Francisco Taxicab dataset. Results show that our approach is efficient, solving instances with up to 600 points within 31 CPU minutes. For these datasets, incorporating flexible pickup and drop-off points can reduce the total vehicle travel time of the rideshare system by 4% on average. © 2025 Elsevier Ltd