Comparing Dynamic User Equilibrium and Noniterative Stochastic Route Choice in a Simulation-Based Dynamic Traffic Assignment Model: Practical Considerations for Large-Scale Networks

Mudabber Ashfaq,S Travis Waller,Meead Saberi,Ziyuan Gu,Jing Dong

doi:10.1155/2021/6667335

Abstract

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 comparison has been made at two levels: link-level flows and network-level congestion patterns. Results suggest that adaptive driving improves the quality of the SRC solution, but its difference from DUE still remains significant at the link level. Results have practical implications for the application of large-scale simulation-based DTA models for planning and operations purposes.

Highlights

Simulation-based dynamic traffic assignment (DTA) models are powerful tools for planning and operations of transportation networks. e two main components of a DTA model are equilibrium-seeking route choice and network loading [1]. e literature clearly distinguishes between two other commonly used terms known as route search and route choice
Finding dynamic equilibrium is computationally expensive in large-scale networks, but it often provides robust and consistent model outcomes given the equilibrium-seeking behavior. e stochastic route choice (SRC) model introduces a random perception error in the path assignment without seeking the equilibrium state of the network, thereby resulting in a more probabilistic DTA model outcomes
Given the increasing use of SRC model in practice and its lower computational costs, in this paper we aimed to provide a quantitative comparison between the two modeling approaches when applied to a largescale network and used in traffic operations optimization

Summary

Introduction

Simulation-based dynamic traffic assignment (DTA) models are powerful tools for planning and operations of transportation networks. e two main components of a DTA model are equilibrium-seeking route choice and network loading [1]. e literature clearly distinguishes between two other commonly used terms known as route search and route choice. Even under the assumption of similar behavior of drivers, the model faces many challenges such as route search from a huge set of possible routes in a large-scale network and real-time access and provision of traffic conditions. Vehicles’ path-changing enroute due to nonrecurrent events such as incidents, vehicle breakdowns, and special events requires special modeling considerations as most existing models in practice apply pre-defined routes before vehicles’ departure [14] Different equilibriumseeking models, such as DUE, dynamic stochastic user equilibrium (DSUE), boundedly rational user equilibrium (BRUE) [15,16,17], multiclass fuzzy user equilibrium (MFUE) [18], prospect-based user equilibrium (PBUE) [19], and behavioral user equilibrium (BUE) [20], adopt unique behavioral assumptions of drivers [1]. Drivers’ route choice using a probabilistic approach considering utilities of different alternative routes results in the so-called stochastic route choice (SRC) models that are not necessarily seeking an equilibrium solution [21]

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