Maximizing Ridership through Integrated Bus Service Considering Travel Demand Elasticity with Genetic Algorithm

Developing efficient operational strategies to improve service quality of bus transit, such as reducing travel time, can stimulate ridership. A mathematical model is formulated to optimize integrated bus service which maximizes ridership considering demand elasticity with respect to travel time and fare. The proposed integrated service, consisting of local (e.g., all-stop) and express (e.g., stop-skipping) services, is optimized using a genetic algorithm (GA) subject to minimum service frequency and fleet size constraints. A numerical analysis is conducted under various operation scenarios based on a real-world bus route in Chengdu, China. The results suggest that the optimized integrated service may increase the ridership. The sensitivity analysis is conducted, and the impacts of model parameters on decision variables to the ridership are explored.