Zong Tian
Published: 2014
Total Pages: 45
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The objective of this research is to build and calibrate a DTA model for Northern Nevada (Reno Sparks 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