Inclusion of Time-Dependent Networks in Maryland Statewide Transportation Model

Sponsors

  • Federal Highway Administration
  • Maryland State Highway Administration

Partner

  • Federal Highway Administration
  • Maryland State Highway Administration

 

Overview

This report describes the development steps, results and the lessons learned from the Inclusion of Time-dependent Networks in the Maryland Statewide Transportation Model (MSTM) project. The report covers the following elements:

  • Modeling tool selection
  • Input data overview
  • Generating and Testing Time-Dependent MSTM Model
  • Validation
  • Scenario testing
  • Output visualization
  • Conclusions and recommendations

 

Current static statewide or regional models help planning and decision-making but they lack the level of detail to analyze the temporal aspects of congestion. On the other hand, planning agencies are required/in need of conducting more detailed, project level analyses that require capturing spatial and temporal changes in traffic patterns such as congestion, queue buildup and dissipation. Therefore, for effective planning, representing user response to emerging strategies which require a time dimension such as Travel Demand Management (TDM), congestion management strategies, ITS applications, and emissions modeling, is becoming increasingly important. Dynamic Traffic Assignment (DTA) methodology can provide measures such as time-dependent link volumes, speed, density, queue length, and can track individual vehicles. Modeling the Maryland Statewide Transportation Model (MSTM) in a Dynamic Traffic Assignment (DTA) platform can provide a more realistic analytic capability through the consideration of the time dimension. This will allow the model to analyze the buildup and decline in congestion, shifting of travel times and queuing on the highway network. The applications that a time-dependent modeling approach can be useful at statewide level include: (i) tracking statewide time-dependent flows, (ii) more accurate representation of congestion, (iii) analyzing impacts of temporal travel restrictions, (iv) impacts of variable tolling, (iv) tracking time-dependent freight flows. For example, the effect of congestion on long distance freight travel can be captured. It may take four to five hours for freight to traverse the State in both peak and off-peak periods. DTA can provide better information on peak spreading and freight routing in anticipation of congestion.

Contact: Frederick Ducca

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