A simulation-based optimization approach for designing transit networks

Abstract

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

Process Model for the Introduction of Automated Buses

“…Operation costs and travel costs were minimized under the framework of a mixed-integer nonlinear programming problem using a branch and bound algorithm [84]. Nnene et al presented a simulation-based optimization for designing public transport networks [85]. An activity-based simulation was used and it was shown by the example of Cape Town (South Africa) that efficient networks can be created [85].…”

“…Nnene et al presented a simulation-based optimization for designing public transport networks [85]. An activity-based simulation was used and it was shown by the example of Cape Town (South Africa) that efficient networks can be created [85]. The study by Yoon and Chow suggested an artificial intelligence-driven algorithm to combine transit network design with optimal learning [86].…”

The complex web of land use planning, legislation and urban mobility in Maseru, Lesotho

“…These can be seen through the development of integrated transport systems in countries like South Africa, Nigeria, Tanzania, India, Brazil and Vietnam [24,42]. In South Africa, the development of a bus rapid transit (BRT) system was meant to cater to the urban poor's public transport needs and provide sustainable mobility options [43]. Likewise, the Dakar Bus Rapid Transit Pilot Project in Senegal approved in 2017 is currently being implemented and is envisaged to carry approximately 300,000 passengers per day while reducing their travel times from 95 to 45 min [44].…”

A Three-Level Decision Support Approach Based on Multi-objective Simulation-Optimization and DEA: A Supply Chain Application

“…The decision-makers may require proper methods to help them, especially when dealing with complex systems where the objectives under analysis conflict with themselves, such as in multi-objective complex problems [ 1]. Some of these methods include the use of optimization and simulation to find solutions that are suitable for decision-makers [2][3][4].…”

“…Ramirez et al [ 4] used simulation and optimization to minimize oil and gas industry costs and time. Nnene et al [ 2] used simulation and optimization to solve a transit network design problem, in which simulation evaluates alternative network solutions by simulating travel demand on them while a multi-objective optimization algorithm searches for efficient network solutions. Simulation-optimization is thus a powerful decision-making tool that has the ability to capture intricate relationships and interactions among several entities in a real-world complex system and identify the best design point [ 6].…”