Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network

Shafiei, Sajjad; Gu, Ziyuan; Saberi, Meead

doi:10.1016/j.simpat.2018.04.006

Cited by 65 publications

(38 citation statements)

References 33 publications

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“…We use empirical data from Google that contains estimated time-dependent traffic speeds on every link in the road network across six different cities in the world, namely Chicago, London, Paris, Sydney, Melbourne, and Montreal (Methods). We also use simulated data from a calibrated mesoscopic dynamic traffic assignment model of Melbourne (Methods) [1]. Using both empirical data and simulation results, we demonstrate that the proposed modeling framework can successfully describe the dynamics of congestion propagation and dissipation in urban networks.…”

Section: Resultsmentioning

confidence: 99%

“…Traffic jams in cities propagate over time and space. Existing approaches to model city traffic often rely on microscopic models with high computational burden as well as excessive parameterization required for calibration [1][2][3]. Further, the lack of available transport infrastructure data in many countries, especially those that are developing, poses a challenge for traffic modelers.…”

Section: Introductionmentioning

confidence: 99%

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A simple contagion process describes spreading of traffic jams in urban networks

et al. 2020

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The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate and congestion dissipation rate . We describe the dynamics of congestion propagation and dissipation using these new parameters, , and , embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.Unlike individual link traffic shockwaves in a two-dimensional time-space diagram, which are categorized as forward or backward moving, network traffic jams evolve in multi directions over space. Therefore, we propose that a network's propagation and recovery can be characterized by two average rates, namely the congestion propagation rate and a congestion recovery rate , which together reflect the number of congested links in the network over time. These two macroscopic characteristics are critical in modeling congestion propagation and dissipation as a simple contagion process [21].Despite the complex human behavior-driven nature of traffic, we demonstrate that urban network traffic congestion follows a surprisingly similar spreading pattern as in other systems, including the spread of infectious disease in a population or diffusion of ideas in a social network, and can be described using a similar parsimonious theoretical network framework. Specifically, we model the spread of congestion in urban networks by adapting a classical epidemic model to include a propagation and recovery mechanism dependent on time-varying travel demand and consistent with fundamentals of network traffic flow theory. We illustrate the model to be a robust and predictive analytical model, and validate the framework using empirical and simulation-based numerical experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A simple contagion process describes spreading of traffic jams in urban networks

et al. 2020

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“…Signal controls at intersections are set as actuated signals using the Sydney Coordinated Adaptive Traffic System (SCATS) data including the maximum cycle time, minimum green time, and turning movements for each phase. While traffic flow fundamental diagrams are calibrated against freeway loop detector data from multiple months (Gu et al, 2016, the timedependent OD demand is calibrated and validated using multi-source traffic data (Shafiei et al, 2018). Appendix D. Results of the non-tolling and optimal cordon toll scenarios…”

Section: Appendix B Proof Of Propositionmentioning

confidence: 99%

Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram

Shafiei

Liu

et al. 2018

Transportation Research Part C: Emerging Technologies

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Given the efficiency and equity concerns of a cordon toll, this paper proposes a few alternative distance-dependent area-based pricing models for a large-scale dynamic traffic network. We use the Network Fundamental Diagram (NFD) to monitor the network traffic state over time and consider different trip lengths in the toll calculation. The first model is a distance toll that is linearly related to the distance traveled within the cordon. The second model is an improved joint distance and time toll (JDTT) whereby users are charged jointly in proportion to the distance traveled and time spent within the cordon. The third model is a further improved joint distance and delay toll (JDDT) which replaces the time toll in the JDTT with a delay toll component. To solve the optimal toll level problem, we develop a simulation-based optimization (SBO) framework. Specifically, we propose a simultaneous approach and a sequential approach, respectively, based on the proportional-integral (PI) feedback controller to iteratively adjust the JDTT and JDDT, and use a calibrated large-scale simulation-based dynamic traffic assignment (DTA) model of Melbourne, Australia to evaluate the network performance under different pricing scenarios. While the framework is developed for static pricing, we show that it can be easily extended to solve time-dependent pricing by using multiple PI controllers. Results show that although the distance toll keeps the network from entering the congested regime of the NFD, it naturally drives users into the shortest paths within the cordon resulting in an uneven distribution of congestion. This is reflected by a large clockwise hysteresis loop in the NFD. In contrast, both the JDTT and JDDT reduce the size of the hysteresis loop while achieving the same control objective. We further conduct multiple simulation runs with different random seed numbers to demonstrate the effectiveness of different pricing models against simulation stochasticity. However, we postulate that the feedback control is not applicable with guaranteed convergence if the periphery of the cordon area becomes highly congested or gridlocked.

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“…In this paper, we employ a recently developed large‐scale simulation‐based DTA model of Melbourne, Australia (Shafiei, Gu, & Saberi, ). The model is deployed in AIMSUN with time‐varying commuting demand during the 6–10 AM peak period.…”

Section: Resultsmentioning

confidence: 99%

Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

Waller

Saberi

2019

Computer aided Civil Eng

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Toll optimization in a large‐scale dynamic traffic network is typically characterized by an expensive‐to‐evaluate objective function. In this paper, we propose two toll‐level problems (TLPs) integrated with a large‐scale simulation‐based dynamic traffic assignment model of Melbourne, Australia. The first TLP aims to control the pricing zone (PZ) through a time‐varying joint distance and delay toll such that the network fundamental diagram (NFD) of the PZ does not enter the congested regime. The second TLP is built upon the first TLP by further considering the minimization of the heterogeneity of congestion distribution in the PZ. To solve the two TLPs, a computationally efficient surrogate‐based optimization method, that is, regressing kriging with expected improvement sampling, is applied to approximate the simulation input–output mapping, which can balance well between local exploitation and global exploration. Results show that the two optimal TLP solutions reduce the average travel time in the PZ (entire network) by 29.5% (1.4%) and 21.6% (2.5%), respectively. Reducing the heterogeneity of congestion distribution achieves higher network flows in the PZ and a lower average travel time or a larger total travel time saving in the entire network.

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Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network

Cited by 65 publications

References 33 publications

A simple contagion process describes spreading of traffic jams in urban networks

A simple contagion process describes spreading of traffic jams in urban networks

Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram

Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

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