Nonlinearity in Time-Dependent Origin-Destination Demand Estimation in Congested Networks

Shafiei, Sajjad; Saberi, Meead; Vu, Hai L.

doi:10.1109/itsc.2019.8917357

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we perform a mesoscopic simulation-based DTA on a subnetwork of Melbourne metropolitan area (Figure 1) covering the city center using an existing calibrated and validated model in AIMSUN [10] For more details on calibration and validation of the DTA model, please see [10,[37][38][39]. e supply and demand models were calibrated and validated.…”

Section: Model Descriptionmentioning

confidence: 99%

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

Ashfaq

Waller

et al. 2021

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Model Descriptionmentioning

confidence: 99%

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

Ashfaq

Waller

et al. 2021

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, several recent studies have modified the linear functional relation and captured the nonlinearity of the problem ( 18, 19 ). These modifications improve the accuracy of results while they substantially increase the computational burden and limit their application to only medium-sized networks ( 20 ). Given the computational intensity, a practical approach used in recent successful large-scale DTA applications is applying a two-stage OD estimation ( 21, 22 ).…”

Section: Introductionmentioning

confidence: 99%

Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network

Shafiei

Saberi

2020

Transportation Research Record

Self Cite

View full text Add to dashboard Cite

Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.

show abstract

Nonlinearity in Time-Dependent Origin-Destination Demand Estimation in Congested Networks

Cited by 2 publications

References 12 publications

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

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

Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network

Contact Info

Product

Resources

About