Predicting Travel Time on Freeway Corridors: Machine Learning Approach
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2020-09-01
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Corporate Contributors:University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
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Edition:Final
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Abstract:The purpose of this project is to develop a systematic approach to predicting freeway travel time. An advanced machine learning-based approach (i.e. XGBoost model) is employed to predict freeway travel time. The prediction methodology can assist the decision makers in planning, designing, operating, and managing a more efficient highway system. Specific objectives are to: (1) develop the travel time prediction model using an advanced, efficient and accurate machine learning-based approach; (2) select a real-world freeway corridor to examine the developed prediction model; and (3) evaluate the prediction results of the developed model.
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