A framework for and empirical study of algorithms for traffic assignment. (February 2015)

• Record Type: Journal Article
• Authors: Perederieieva, Olga; Ehrgott, Matthias; Raith, Andrea; Wang, Judith Y.T.
• Is Part Of: Computers & operations research. Volume 54(2015)
• Page Start: 90
• Page End: 107
• Publication Date: 2015-02
• Subjects: User equilibrium -- Traffic assignment -- Algorithms -- Numerical study; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2014.08.024
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: Traffic congestion is an issue in most cities worldwide. Transportation engineers and urban planners develop various traffic management projects in order to solve this issue. One way to evaluate such projects is traffic assignment (TA). The goal of TA is to predict the behaviour of road users for a given period of time (morning and evening peaks, for example). Once such a model is created, it can be used to analyse the usage of a road network and to predict the impact of implementing a potential project. The most commonly used TA model is known as user equilibrium, which is based on the assumption that all drivers minimise their travel time or generalised cost. In this study, we consider the static deterministic user equilibrium TA model. The constant growth of road networks and the need of highly precise solutions (required for select link analysis, network design, etc.) motivate researchers to propose numerous methods to solve this problem. Our study aims to provide a recommendation on what methods are more suitable depending on available computational resources, time and requirements on the solution. In order to achieve this goal, we implement a flexible software framework that maximises the usage of common code and, hence, ensures comparison of algorithms on common ground. In order to identify similarities and differences of the methods, we analyse groups of algorithms that are based on common principles. In addition, we implement and compare several different methods for solving sub-problems and discuss issues related to accumulated numerical errors that might occur when highly accurate solutions are required.