Application of Cross-Nested Logit Route Choice Model in Stochastic User Equilibrium Traffic Assignment (original) (raw)
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Effects of Choice Set Size and Route Choice Models on Path-Based Traffic Assignment
Few of the recently developed route choice models have actually been applied in traffic assignment problems. This paper discusses the implementation of selected route choice models in stochastic user equilibrium algorithms. The focus of the paper is on path-based assignment, which is essential in the implementation of route choice models. The paper analyzes the effect of choice set size and selected choice models on problem convergence, running time and selected results. The results presented in the paper indicate that for real-size networks, generation of a large number of alternative routes is needed. Furthermore, convergence properties greatly improve if the generated routes are sufficiently disjointed.
Link- and Path-Based Traffic Assignment Algorithms: Computational and Statistical Study
Transportation Research Record: Journal of the Transportation Research Board, 2002
The computational performance of five algorithms for the traffic assignment problem (TAP) is compared with that of mid- to large-scale randomly generated grid networks. The applied procedures include the Frank-Wolfe, PARTAN, gradient projection, restricted simplicial decomposition, and disaggregate simplicial decomposition algorithms. A statistical analysis is performed to determine the relative importance of various properties (network size, congestion level, solution accuracy, zone-node ratio) of the traffic assignment problem for the five selected algorithms. Regression models, which measure central processing unit time and number of iterations consumed by each algorithm using various factors and their combinations, are derived to provide a quantitative evaluation. Ultimately, the findings of this research will be useful in guiding transportation professionals to choose suitable solution algorithms and to predict the resulting algorithm performance in TAPs.
A Path-Based Algorithm for the Cross Nested Logit Stochastic User Equilibrium Traffic Assignment
This paper investigates the single-class static stochastic user equilibrium (SUE) problem with separable and additive link costs. A SUE assignment based on the Cross-Nested Logit (CNL) route choice model is presented. The CNL model can better represent route choice behavior compared to the Multinomial Logit (MNL) model, while keeping a closed form equation. The paper uses a specific optimization formulation developed for the CNL model, and develops a path-based algorithm for the solution of the CNL-SUE problem based on adaptation of the disaggregate simplicial decomposition (DSD) method. The paper illustrates the algorithmic implementation on a real size network and discusses the trade-offs between MNL-SUE and CNL-SUE assignment.
A computational study of traffic assignment algorithms
Traffic congestion is an issue in most cities worldwide. One way to model and analyse the effect of congestion and other factors on route choice behaviour and to predict the impact of traffic management projects and transport policies is traffic assignment (TA). The most commonly used TA model is known as user equilibrium (UE), which is based on the assumption that all drivers want to minimise their travel time or generalised cost. As a result, an equilibrium is achieved when no one has an incentive to switch to another route. Although the conventional mathematical model of TA belongs to the convex optimisation domain and, hence, is relatively easy to solve, efficient algorithms are required in order to be able to solve TA in a reasonable amount of time for realistic transport networks. This motivates researchers to propose numerous methods and algorithms to solve this problem in the literature. However, there is no comprehensive empirical study that compares the performance of different approaches on benchmark instances. In this study, our objective is to fill this gap. We provide a literature review of the most promising methods. We classify algorithms according to the way the solution is represented, namely, link-based (solution is represented by link flows), path-based (solution is represented by path flows) and origin-based (solution is represented by link flows corresponding to each origin), and implement the most representative algorithms in each group. We perform numerical tests on benchmark instances of various sizes, compare the algorithms and analyse the impact of their main components on their running time. We also study the convergence behaviour of the methods with respect to different levels of solution accuracy.
Computational Study of Traffic Assignment Algorithms
2013
Traffic congestion is an issue in most cities worldwide. One way to model and analyse the effect of congestion and other factors on route choice behaviour and to predict the impact of traffic management projects and transport policies is traffic assignment (TA). The most commonly used TA model is known as user equilibrium (UE), which is based on the assumption that all drivers want to minimise their travel time or generalised cost. As a result, an equilibrium is achieved when no one has an incentive to switch to another route. Although the conventional mathematical model of TA belongs to the convex optimisation domain and, hence, is relatively easy to solve, efficient algorithms are required in order to be able to solve TA in a reasonable amount of time for realistic transport networks. This motivates researchers to propose numerous methods and algorithms to solve this problem in the literature. However, there is no comprehensive empirical study that compares the performance of different approaches on benchmark instances. In this study, our objective is to fill this gap. We provide a literature review of the most promising methods. We classify algorithms according to the way the solution is represented, namely, link-based (solution is represented by link flows), path-based (solution is represented by path flows) and origin-based (solution is represented by link flows corresponding to each origin), and implement the most representative algorithms in each group. We perform numerical tests on benchmark instances of various sizes, compare the algorithms and analyse the impact of their main components on their running time. We also study the convergence behaviour of the methods with respect to different levels of solution accuracy.
A Hybrid Route Choice Model for Dynamic Traffic Assignment
choices, 2011
Network user equilibrium or user optimum is an ideal state that can hardly be achieved in real traffic. More often than not, every day traffic tends to be in disequilibrium rather than equilibrium, thanks to uncertainties in demand and supply of the network. In this paper we propose a hybrid route choice model for studying non-equilibrium traffic. It combines pre-trip route choice and en-route route choice to solve dynamic traffic assignment (DTA) in large-scale networks. Travelers are divided into two groups, habitual travelers and adaptive travelers. Habitual travelers strictly follow their pre-trip routes which can be generated in the way that major links, such as freeways or major arterial streets, are favored over minor links, while taking into account historical traffic information. Adaptive travelers are responsive to real-time information and willing to explore new routes from time to time. We apply the hybrid route choice model in a synthetic medium-scale network and a large-scale real network to assess its effect on the flow patterns and network performances, and compare them with those obtained from Predictive User Equilibrium (PUE) DTA. The results show that PUE-DTA usually produces considerably less congestion and less frequent queue spillback than the hybrid route choice model. The ratio between habitual and adaptive travelers is crucial in determining realistic flow and queuing patterns. Consistent with previous studies, we found that, in non-PUE DTA, supplying a medium sized group (usually less than 50%) of travelers real-time information is more beneficial to network performance than supplying the majority of travelers with real-time information. Finally, some suggestions are given on how to calibrate the hybrid route choice model in practice to produce realistic results.
Implementation Issues of Route Choice Models in Path-Based Algorithms
Few of the recently developed route choice models have actually been applied in traffic assignment models. This paper discusses the implementation of selected route choice models in stochastic user equilibrium algorithms. The focus of the paper is on path-based algorithms, since they are essential in the implementation of route choice models. The paper analyzes the effect of choice set size and selected choice models on problem convergence, running time and selected results. The results presented in the paper indicate that for real-size networks, it is needed to generate a large number of alternative routes; in addition, convergence properties greatly increase if the generated routes are sufficiently disjointed.
Keywords Dynamic Traffic Assignment, Route choice modelling
2008
This chapter focuses on dynamic traffic assignment from both a theoretical and practical perspective. In paragraph 2.1 traffic assignment will be introduced in general, followed by discovering different types of traffic assignment in paragraph 2.2. Paragraphs 2.3 and 2.4 focus on static and dynamic assignment respectively. The chapter ends with paragraph 2.5 on the MetaNET/MaDAM model.
Journal of Advanced Transportation, 2021
Simulation-based dynamic traffic assignment (DTA) models play a vital role in transportation planning and operations. While the widely studied equilibrium-seeking DTA including dynamic user equilibrium (DUE) often provides robust and consistent outcomes, their expensive computational cost for large-scale network applications has been a burden in practice. The noniterative stochastic route choice (SRC) model, as a nonequilibrium seeking DTA model, provides an alternative for specific transportation operations applications that may not require equilibrium results after all (e.g., evacuation and major network disruptions) and thus tend to be computationally less expensive, yet may suffer from inconsistent outcomes. While DUE is a widely accepted approach for many strategic planning applications, SRC has been increasingly used in practice for traffic operations purposes. This paper aims to provide a comparative and quantitative analysis of the two modeling approaches. Specifically, a co...