Predicting Travel Time on Freeway Corridors: Machine Learning Approach

Details

• Creators:
  Fan, Wei (David) ; Chen, Zheng
• Corporate Creators:
  University of North Carolina at Charlotte. Department of Civil and Environmental Engineering
• 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
• Subject/TRT Terms:
  Algorithms Forecasting Freeways Machine Learning Travel Time Validation
• Publication/ Report Number:
  2019 Project 01
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final
• Corporate Publisher:
  University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education
• 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.
• Format:
  PDF
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:63f3c1e3bb6c315b79bebc6446c29a547a2ab6de39afa49c11dcabd137b5a7d178d6c39a20214638ffaf4c55eede84fe79959457a36b2afca00a312346279b1d
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/57065/dot_57065_DS1.pdf
• File Type:
  [PDF - 1.60 MB ]