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• W1528111389 abstract "In order to automate the travel demand forecasting process in urban transportation planning, a number of commercial computer based travel demand forecasting models have been developed, which have provided transportation planners with powerful and flexible tools in modeling a traffic network for planning or traffic impact studies. It is commonly recognized that none of the existing travel demand forecasting software is perfectly suited for all application network scenarios and traffic conditions. A particular model, which is strong in one application scenario, may be weak in a different application scenario. This report intends to present a comparative study of two widely used computer based travel demand forecasting models: QRS II vs. EMME/2. The comparative study is designed to identify main features and differences of the two models, with an attempt to provide some useful information to practitioners. The comparative description of basic features of two models in this report includes model structure, network development, data input, network modification, parameter calibration, and modeling output. In the comparison of advanced features, the calculate function in QRS II and macro language in EMME/2 are presented. A real-world small urban network, south Missouri City Network, is used to support the comparison effort. The study has found that both QRS II and EMME/2 models are reliable to model real-world networks. However, QRS II is relatively easy to use for inexperienced users because of its comprehensive default parameters, calculation formulas, procedures and the embedded four-step travel demand forecasting process. On the other hand, EMME/2 provides more powerful and flexible modules for users to perform more complex tasks." @default.
• W1528111389 created "2016-06-24" @default.
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• W1528111389 date "2000-10-01" @default.
• W1528111389 modified "2023-09-25" @default.
• W1528111389 title "Travel demand forecasting models: a comparison of EMME/2 and QRS II using a real-world network" @default.
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