Demand Structure Analysis for Urban Traffic Using Automatic License Plate Recognition Data

Traffic demand is the basis for urban traffic planning and management. Most studies focus on exploring the temporal-spatial pattern of traffic demand. Few studies have investigated traffic demand structure with field data, such as the ratio of commuting demand. The availability of automatic license plate recognition (ALPR) data provides the opportunity to study the travel behavior of individual vehicles. A method was proposed to investigate the traffic demand structure of urban road networks using ALPR data. First, the temporal and spatial features of individual vehicles were extracted. A clustering technique was then used to identify the commuting vehicles. The commuting and non-commuting traffic demand of the road network were distinguished with the proposed method. Then the time-varying patterns of demand structure within a day combined with MFD and day-to-day patterns were analyzed. The proposed method would help conduct active, effective traffic management and control.

• Record URL:
  • https://doi.org/10.1061/9780784482292.441
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482292
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Hong, Rongrong
  • Zhou, Dong
  • An, Chengchuan
  • Rao, Wenming
  • Xia, Jingxin
• Conference:
  • 19th COTA International Conference of Transportation Professionals
  • Location: Nanjing , China
  • Date: 2019-7-6 to 2019-7-8
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

• TRT Terms: Automatic license plate readers; Cluster analysis; Detection and identification technologies; License plates; Traffic data; Travel demand
• Uncontrolled Terms: License plate recognition
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01715421
• Record Type: Publication
• ISBN: 9780784482292
• Files: TRIS, ASCE
• Created Date: Aug 30 2019 1:01PM