Approximate multi-objective optimization for integrated bus route design and service frequency setting. (January 2022)

• Record Type: Journal Article
• Authors: Ahern, Zeke; Paz, Alexander; Corry, Paul
• Is Part Of: Transportation research. Volume 155(2022)
• Page Start: 1
• Page End: 25
• Publication Date: 2022-01
• Subjects: Metaheuristics -- Route design -- Frequency setting -- Simulated annealing -- Multi-objective; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2021.10.007
• Languages: English
• ISSNs: 0191-2615

Abstract

Abstract: This paper focuses on the transit network design and frequency setting problem for bus transit systems, where solutions are simultaneously generated in favor of the passengers and operator. While multi-objective solutions exist in the literature, simplifications and inconsistent assumptions make them hard to compare. For example; researchers have proposed solutions that report on a minimal fleet size without considering the service frequencies that guide the passenger's travel options, as a result, logical choices made by all passengers would cause the buses in the transit system to be overcrowded. To address this issue, we have developed a mathematical model which couples service frequencies and passenger assignment reflective of available choice options a passenger can take in the network. This approach can achieve lower operating costs while still ensuring that passengers can reach their destination at no detriment to journey times. We have constructed a complete set of multi-objective solutions, and compared them with those in the literature. The experiments demonstrate that our current solutions generally dominate those in the literature. Graphical abstract: Highlights: Simultaneously minimizes passenger and bus operation costs. Mathematical Programming formulation, coupling service frequency with passenger flows. Integrated heuristic: algorithm for efficient service frequencies. Improved passenger waiting time calculation. Multi-objective simulated annealing to approximate a Pareto front