What is Dynamic Traffic Assignment? A Dynamic Traffic Assignment, known as DTA, model estimates the evolution and propagation of congestion through detailed models that capture travel demand, network supply and their complex interactions. Unlike a static model, a DTA model can describe time-dependent dynamics of traffic and replicate the interactions between travelers and the transportation network. A simulation-based DTA model is an analysis tool to address complex and dynamic transportation operations and planning issues of urban road networks. It overcomes the limitations of traditional static assignment models by using advanced traffic modeling techniques to capture the dynamics of congestion formation and dissipation associated with time-dependent demand and network conditions.
Benefits and Applications: A simulation-based DTA model supports transportation network planning and traffic operations decisions. It overcomes many of the known limitations of static models used in current planning practice. It can be used to assess the impacts of ITS and non-ITS technologies on the transportation network, to support decision-making for work zone planning and traffic management, to evaluate different congestion pricing schemes, to plan for special events and emergency situations including evacuation scenarios, and more importantly to perform traffic assignment analyses in conjunction with classical four-step demand models or emerging activity-based and tour-based models..
Figure 1 A snapshot of the simulation-based dynamic traffic assignment model of Melbourne
- Shafiei, S., Gu, Z., Saberi M. (2017) Calibration and Validation of a Simulation-based Dynamic Traffic Assignment Model for a Large-Scale Congested Network using Multi-Source Data: A Case for Melbourne, Australia. (working paper)
- Shafiei, S., Gu Z., Sarvi M., Saberi M. (2017) Deployment and Calibration of a Large-Scale Mesoscopic Dynamic Traffic Assignment Model of Melbourne, Australia. Proceedings of The Transportation Research Board (TRB) 96th Annual Meeting, Transportation Research Council of the U.S. National Academies, Washington, D.C., January 8–12, 2017.
- Shafiei, S., Saberi M., Zockaie, A., Sarvi, M. (2017) A Sensitivity-Based Linear Approximation Method to Estimate Time-Dependent Origin-Destination Demand in Congested Networks. Transportation Research Record: Journal of the Transportation Research Board.
- Shafiei, S., Saberi, M., Sarvi M. (2016) Application of an Exact Gradient Method to Estimate Dynamic Origin-Destination Demand for Melbourne Network. The 19th International IEEE Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, November 1-4, 2016.
- Gu, Z., Saberi, M., Sarvi, M., Liu, Z. (2016) Calibration of Traffic Flow Fundamental Diagrams for Network Simulation Applications: A Two-Stage Clustering Approach. Proceedings of The 19th International IEEE Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, November 1-4, 2016.