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Development of Traffic Simulation Laboratory for Design Planning and Traffic Operations (Phase 1)

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  • Edition:
    Final Report 1995
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  • Abstract:
    The key element in improving traffic operations and performing effective real time management is the ability to assess the effectiveness of the various alternatives prior to implementation. Simulation methods have long been recognized as the most effective tool for such an analysis, and various simulators have been developed by different agencies for freeway and arterial networks. While there have been individual tests of each software by various agencies, no comprehensive effort has been made to quantify and evaluate the performance of each model. The major difficulty includes the lack of detailed real data and the time-consuming manual effort to prepare input information for each software. To be sure there is no user-interface developed to data that can generate input files for different simulators with a common set of data. Recent installation of the state-of-the art video detection systems in the 1-394 freeway provides a unique opportunity to develop a comprehensive freeway database that can be used to create various test cases with detailed traffic database that can be used to create various test cases with detailed traffic information. Further, the 1-494 Integrated Corridor Traffic Management project being conducted by MN/DOT will be able to provide valuable corridor traffic data which can be used for evaluating freeway/arterial network models. By evaluating existing traffic simulation models the advantages/disadvantages of each model can be identified. Based on the evaluation results a comprehensive modeling approach for freeway/arterial networks can be developed. The ultimate goal of this research is to develop a traffic simulation laboratory where various roadway design/operational alternatives can be evaluated with traffic simulators under an integrated database-simulation environment. The proposed research, Phase I, will evaluate existing freeway simulation models with detailed traffic data to be collected using machine-vision an loop detection systems.
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    urn:sha256:5da25bfc58be7f9be2f8d0bb90d28a431ec412ed5c02fb72ce60ff40e444f3f6
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    Filetype[PDF - 11.26 MB ]
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