Development of a Dynamic Traffic Assignment Model for Northern Nevada
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Development of a Dynamic Traffic Assignment Model for Northern Nevada

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    The objective of this research is to build and calibrate a DTA model for Northern Nevada (RenoSparks Area) based on the network profile and travel demand information updated to date. The critical procedures include development of consistent and readily adaptable DTA model, model validation, and calibration based on observed field data. The DTA software package used to develop the DTA model in this project is NeXTA/DTALite. Major findings: (1) Capabilities and benefits of DTA. (1a) DTA is mesoscopic in nature, providing a connection between regional travel demand forecasting and micro-simulation models. It is one step further from the planning level travel forecasting towards the operating details of micro-simulation, i.e., DTA analyzes large networks as a travel demand forecasting tool and provides time-varying traffic network performance (e.g., queue formation, bottleneck identification) but not as much detailed as micro-simulation models. (1b) Comparing with micro-simulation models which normally represent known traffic flow patterns, DTA can both represent current traffic performance and evaluate near-term traffic flow impacts from network changes. It is particularly useful to model a regional level network to forecast traffic flow pattern changes and operational impacts due to incidents such as work zone, special events, and accidents. (2) Requirements for DTA Development and Applications (2a) Geometric data, traffic control data, traffic demand, OD demand data and transit demand are basic requirements for network development. (2b) For model calibration, the fidelity of a DTA model depends on more than link volumes. Typical types of data for calibration strategies can include: travel times, travel speeds, queue information, and transit operations. (2c) Transportation modeling techniques and various levels of efforts are needed depending on the model complexity and data availability. (3) Limitations of DTA Applications. (3a) For long-term planning, DTA may not be able to produce a well-calibrated model because of the lack of future travel demand data and corresponding field data. Instead, travel forecasting models such as TransCAD is a better fit for bottlenecks estimation studies. While DTA can only identify active bottlenecks, travel demand forecasting models can predict all potential bottleneck locations, which is necessary for long-term planning purposes. (3b) The level of precision from DTA models largely depends on data availability. DTA requires a significantly larger amount of data which may not be readily available in most cases. Decision makers need to assess the desired level of precision and the available resources and choose if DTA or conventional travel demand models should be used. (3c) For a more localized network or subarea where detailed traffic operational analysis is desired (e.g., transit service and pedestrian facility, turn pockets design, signal control, freeway reconstruction), micro-simulation is a better tool.
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