Customized Bus Route Optimization with the Real-Time Data

Abstract

This paper investigates the real-time customized bus (CB) route optimization problem, which aims to maximize the service rate for clients and profits for operators. The on-road bus has a flexible route, which can be updated based on the real-time data and route optimization solutions. A two-phase framework is established. In phase 1, the vehicle-related data including existing route and schedule, client-related data involving pick-up/drop-off location, and time windows are collected once receiving a new CB request. The second phase optimizes the bus route by establishing three nonlinear programming models under the given data from phase 1. A concept of profit difference is introduced to decide the served demand. To improve computation efficiency, a real-time search algorithm is proposed that the neighboring buses are tested one by one. Finally, a numerical study based on Sioux Falls network reveals the effectiveness of the proposed methodology. The results indicate that the real-time route optimization can be achieved within the computation time of 0.17-0.38 seconds.