Field Data Based Data Fusion Methodologies to Estimate Dynamic Origin-Destination Demand Matrices from Multiple Sensing and Tracking Technologies

Modern technologies can use various types of sensors to collect traffic data; these include global positioning system (GPS), blue tooth, video, automatic vehicle identification (AVI), plate scanning, etc. Based on the characteristic of data collected by sensors, sensors can be categorized as follows. (a) Counting sensors: these sensors can count vehicles on a single lane or a set of lanes in the network. (b) Image/video sensors: these sensors can take images or videos of moving flows. (c) Vehicle-ID sensors: these sensors can be used to identify vehicle IDs in the network. Those sensors/technologies can get variant traffic data including link counts, intersection turning movements, flows and travel time on links and partial paths. This research seeks to propose a Bayesian method and tries to synthesize these multiple sources of data together to estimate dynamic origin-destination (O-D) demand, thereby filling a key gap in the current dynamic O-D demand estimation literature.

• Record URL:
  http://www.purdue.edu/discoverypark/nextrans/assets/docs/109PUY2.1_Summary%20and%20Final%20Technical%20Report.pdf
• 
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  NEXTRANS

  Purdue University
  3000 Kent Avenue
  Lafayette, IN  United States  47906-1075

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Zhu, Senlai
  • Guo, Yuntao
  • Zheng, Hong
  • Peeta, Srinivas
  • Ramadurai, Gitakrishnan
  • Wang, Jian
• Publication Date: 2016-7-31

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 65p

Subject/Index Terms

• TRT Terms: Bayes' theorem; Data fusion; Methodology; Origin and destination; Sensors; Traffic counts; Traffic flow; Travel demand; Travel time; Turning traffic
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01646094
• Record Type: Publication
• Report/Paper Numbers: NEXTRANS Project No. 109PUY2.1
• Contract Numbers: DTRT12-G-UTC05
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Sep 15 2017 10:39AM