A Data-Driven Approach for Scheduling Bus Services Subject to Demand Constraints

• Published in: IEEE transactions on knowledge and data engineering Vol. 35; no. 7; pp. 6534 - 6547
• Main Authors: Brahmanage, Janaka Chathuranga, Kandappu, Thivya, Zheng, Baihua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bus schedule optimization; Buses (vehicles); Costs; Data models; dynamic bus scheduling; Genetic algorithms; greedy search; Operating costs; operational cost; Optimal scheduling; Optimization; Passenger satisfaction; Passengers; Public transportation; Schedules; Search problems; Time-frequency analysis; Transportation systems; Travel demand; User satisfaction; waiting time threshold
• ISSN: 1041-4347; 2326-3865; 1558-2191; 1558-2191
• DOI: 10.1109/TKDE.2022.3188243

Passenger satisfaction is extremely important for the success of a public transportation system. Many studies have shown that passenger satisfaction strongly depends on the time they have to wait at the bus stop (waiting time) to get on a bus. To be specific, user satisfaction drops faster as the waiting time increases. Therefore, service providers want to provide a bus to the waiting passengers within a threshold to keep them satisfied. It is a two-pronged problem: (a) to satisfy more passengers the transport planner may increase the frequency of the buses, and (b) in turn, the increased frequency may impact the service operational costs. To address it, we propose PASS and COST as the two variants that satisfy different optimization criteria mentioned above. The optimization goal of PASS is the number of satisfied passengers while the optimization goal of COST is the number of passengers served per unit of driving time. Consequently, PASS utilizes resources to the maximum to satisfy the highest number of passengers, while COST optimizes for both passenger satisfaction and operational costs. Accordingly, we propose two algorithms to solve PASS and COST respectively and evaluate their performance based on real passenger demand data-set.