A data-driven approach for scheduling bus services subject to demand constraints

• Main Authors: BRAHMANAGE JANAKA CHATHURANGA THILAKARATHNA, KANDAPPU, Thivya, ZHENG, Baihua
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Optimisation; Public Transport; Scheduling; Bus Stop; COST; Data Driven Approach; Demand Constraints; Optimization Criteria; PASS; Passenger Satisfaction; Public Transportation System; Real Passenger Demand Dataset; Scheduling Bus Services; User Satisfaction; Waiting Passengers; Waiting Time; Costs; Schedules; Optimal Scheduling; Data Models; Search Problems; Genetic Algorithms; Time Frequency Analysis; Bus Schedule Optimization; Dynamic Bus Scheduling; Greedy Search; Operational Cost; Waiting Time Threshold; Databases and Information Systems; Numerical Analysis and Scientific Computing; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/7897; https://ink.library.smu.edu.sg/context/sis_research/article/8900/viewcontent/Data_Driven_Bus_Services_2023_av.pdf
• Institution: Singapore Management University

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.