Evaluating Spatial-Proximity Structures in Crash Prediction Models at the Level of Traffic Analysis Zones

Zonal crash prediction that considers cross-zonal spatial correlation is a frontline research topic, especially in the context of transportation safety planning. This study presents an evaluation of crash prediction models at the level of traffic analysis zones with four types of spatial-proximity structures: 0–1 first-order adjacency, common-boundary length, geometry-centroid distance, and crash-weighted centroid distance. Bayesian spatial analysis with conditional autoregressive priors was successfully applied, and Hillsborough data were used to compare the model-fitting and predictive performance associated with the proposed models. The results confirmed the extensive existence of cross-zonal spatial correlation in crash occurrence. The best predictive capability, relatively, was associated with the model that considered proximity of neighboring zones by weighing their common-boundary lengths. Furthermore, full consideration of all possible spatial correlations for all zones significantly increased model complexity, which probably resulted in reduced predictive performance.

• Record URL:
  • https://doi.org/10.3141/2432-06
• Summary URL:
  • http://scholar.google.com/scholar_lookup?title=Evaluating+Spatial-Proximity+Structures+in+Crash+Prediction+Models+at+the+Level+of+Traffic+Analysis+Zones&author=N.+Dong&author=H.+Huang&author=P.+Xu&author=Z.+Ding&author=D.+Wang&publication_year=2014
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309295208
• Authors:
  • Dong, Ni
  • Huang, Helai
  • Xu, Pengpeng
  • Ding, Zhuodi
  • Wang, Duo
• Publication Date: 2014

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 46–52
• Monograph Title: Safety Data, Analysis, and Evaluation 2014
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2432
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Bayes' theorem; Crash risk forecasting; Mathematical models; Mathematical prediction; Spatial analysis; Traffic analysis zones; Traffic safety
• Uncontrolled Terms: Spatial proximity
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01519549
• Record Type: Publication
• ISBN: 9780309295208
• Report/Paper Numbers: 14-1595
• Files: TRIS, TRB, ATRI
• Created Date: Mar 26 2014 10:07AM