An Approach to Geocoding and Modeling Crashes in Developing World Cities

Road safety research and policy in developing world cities are often hampered by poor quality crash data. One of the biggest challenges is the lack of geographic coordinates for crash location, which makes it difficult to accurately map crashes, analyze them, and identify black spots. Without a way to accurately map crashes, there is only limited use for the existing crash databases in cities like Mexico City or Guadalajara (Mexico), and building crash prediction models can be extremely difficult. In this paper, the authors propose a way to address this issue by using a combination of mapping software, programming, and open source data from Open Street Map. They use the city of Guadalajara as an example, and they illustrate how, by using these tools, it is possible to automatically geocode crashes from an existing database using street name information and create an accurate crash map. In addition, the authors show how the information and basic principles from the mapping process can also be used to automatically generate a dataset for a crash prediction model, which can also be estimated automatically. This solution is also applicable to other cities in the developing world, as a way to analyze crashes in the absence of georeferenced crash data.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 12p
  • Monograph Title: Proceedings of the 25th World Road Congress - Seoul 2015: Roads and Mobility - Creating New Value from Transport

Subject/Index Terms

Filing Info

  • Accession Number: 01679910
  • Record Type: Publication
  • ISBN: 9782840604235
  • Report/Paper Numbers: 0691
  • Files: TRIS
  • Created Date: Aug 4 2018 5:07PM