Complete hierarchical multi-objective genetic algorithm for transit network design problem. (30th December 2018)

• Record Type: Journal Article
• Authors: Owais, Mahmoud; Osman, Mostafa K.
• Is Part Of: Expert systems with applications. Volume 114(2018)
• Page Start: 143
• Page End: 154
• Publication Date: 2018-12-30
• Subjects: Public transportation -- Genetic algorithm -- Transit routing -- Constructive algorithms -- Frequency setting; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.07.033
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A Novel complete hierarchical GA methodology for the TNDP. Multi-objective nature of the problem is tackled efficiently. General and adaptable methodology for real size networks. The methodology is statistically verified. Abstract: Transit Network Design Problem is a multi-disciplinary problem that is considered one of the most intractable problems for real size networks. In the late 90s, Meta-heuristics started to prove more reliability to the problem. Genetic Algorithm (GA) is one of the popular Meta-heuristics which is usually implemented because it is simply adapted to the problem. In this study, GA is presented as a complete constructive multi-objective algorithm that creates its own routes from scratch then assembles the routes into efficient transit networks. Finally, it handles the multi-criteria nature of the problem until producing the optimal (near optimal) Pareto front solutions. A new frequency setting algorithm is also developed based on simulation results at the bus stop level which takes the bi-level decision making of both users and operators implicitly. Experimental studies on two real size networks are conducted to validate the methodology performance and robustness.