Abstract
In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We propose a two-phase algorithm that combines the single-objective covariance matrix adaptation evolution strategy with a problem-specific multi-directional local search. With a computational study, we compare our proposed method against both a multi-objective covariance matrix adaptation evolution strategy and a non-dominated sorting genetic algorithm. The integrated discrete event simulation model has several stochastic elements. Fluctuating demand (i.e., creation of passengers) is driven by hourly origin-destination-matrices based on mobile phone and infrared count data. We also consider the passenger distribution along waiting platforms and within vehicles. Our two-phase optimization scheme outperforms the comparative approaches, in terms of both spread and the accuracy of the resulting Pareto front approximation.
Highlights
In 1950, 29.6% of the world’s population lived in urban areas
We propose a multi-objective simulation-based headway optimization scheme for complex urban mass rapid transit systems that is inspired by a real-world case but generic enough to fit other cities’ complex rail-bound public transport systems
Because the results from the first phase in our two-phase optimization scheme (Sect. 5.1) do not provide good results in terms of Z2 for test instances with a high number of decision variables, our multi-directional local search managed to improve that end of the final front quite a bit (e.g., 0.1324–0.0037 for the largest instance)
Summary
In 1950, 29.6% of the world’s population lived in urban areas. Since this percentage has increased every year, reaching 55.3% in 2018. Complex urban mass rapid transit systems (e.g., subway, metro, tube, underground, heavy rail) are a critical component of successful cities They were introduced to improve movement in urban areas and reduce congestion. Changes to headway (or its inverse, frequency, defined by vehicles per unit of time) might lead to overcrowded train stations and vehicles, unless they are carefully planned This challenge constitutes the transit network frequencies setting problem (TNFSP), for which the solution demands a balance between capital and operational expenditures (e.g., infrastructure preservation, potential expansion) with passenger satisfaction (i.e., service level). We propose a multi-objective simulation-based headway optimization scheme for complex urban mass rapid transit systems that is inspired by a real-world case but generic enough to fit other cities’ complex rail-bound public transport systems.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
