Predicting Travel Time on Freeway Corridors: Machine Learning Approach

US Transportation Collection2020PDFTech ReportFan, Wei (David)Chen, ZhengUniversity of North Carolina at Charlotte. Department of Civil and Environmental EngineeringUniversity of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and EducationUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramUnited States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyUniversity of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and EducationUnited StatesAlgorithmsForecastingFreewaysMachine learningTravel timeValidation2019 Project 01FinalThe 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.120