An Enhanced Weight-based Topological Map Matching Algorithm for Intricate Urban Road Network

Map-matching (MM) algorithms integrate positioning data with spatial road network data to identify the correct link on which a vehicle is travelling and determine the location of a vehicle on a link. There are four classes of MM algorithms, including geometric, topological, probabilistic and advanced. The topological map-matching (tMM) algorithms are relatively simple, easy and quick. Due to considering information of heading, proximity, link connectivity and turn-restriction weights, weight- based tMM algorithms are most robust and widely used tMM algorithms. As is known to all, a metropolis usually has intricate road network. And the urban road density has various performances in different parts of a metropolis’ urban area, which makes the weight scores used in tMM algorithm different. As a result, it can affect the accuracy of matched results. In this paper, the authors develop an enhanced weight-based tMM algorithm considering the urban road density. This algorithm was verified using actual taxi global positioning system (GPS) data collected in the urban area of Harbin, China, about 600 positioning points and a 1:80,000 scale digital map of Harbin. The results show that this enhanced weight-based tMM algorithm outperforms the base algorithm and has potential to support many applications of Intelligent Transport System (ITS) service.

• Record URL:
  • https://doi.org/10.1016/j.sbspro.2013.08.189
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S187704281302315X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770428
• Supplemental Notes:
  • © 2013 Haiqiang Yang et al.
• Authors:
  • Yang, Haiqiang
  • Cheng, Shaowu
  • Jiang, Huifu
  • An, Shi
• Conference:
  • 13th COTA International Conference of Transportation Professionals (CICTP 2013)
  • Location: Shenzhen , China
  • Date: 2013-8-13 to 2013-8-16
• Publication Date: 2013-11-6

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: pp 1670-1678
• Serial:
  • Procedia - Social and Behavioral Sciences
  • Volume: 96
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0428
  • Serial URL: http://www.sciencedirect.com/science/journal/18770428/53
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Data collection; Global Positioning System; Highway capacity; Intelligent transportation systems; Mapping; Urban areas
• Geographic Terms: Harbin (China)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01505963
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 28 2014 5:03PM