Collaborative Optimization of Company Commuter Bus Stop Selection and Route Programming Considering Stochastic Travel Time and Demand

Abstract

Company commuter bus service (CCBS) is a convenient commuting mode provided for employees. which has always been popular because of its flexible route planning and low cost. Some companies offer CCBS to their employees for free. However, there are also some companies that need employees to pay the fare for CCBS. For fee-based CCBS, profit is an important consideration. Appropriate stop setting and route programming can attract more commuters and generate greater profits. This paper studies the stop selection and route planning for CCBS considering uncertain demand and travel time. In the data preparation phase, we propose an improved fuzzy c-means clustering to obtain appropriate clusters of commuters’ addresses. In the solution phase, we designed a collaborative framework for stop selection and route programming with the objective to maximize the profit of CCBS. A novel heuristic stochastic dynamic programming (H-SDP) method is then designed for the stop selection sub-problem considering the uncertainties of both traveling time and commuting demand. Meanwhile, we employ a variable neighborhood search algorithm with a novel shaking operation suitable for the routing problem. Finally, we conduct a series of computational experiments to demonstrate the effectiveness and efficiency of the developed algorithms.