Boundary crash data assignment in zonal safety analysis: An iterative approach based on data augmentation and Bayesian spatial model

Boundary effect refers to the issue of ambiguous allocation of crashes occurred on or near the boundaries of neighboring zones in zonal safety analysis. It results in bias estimates for associate measure between crash occurrence and possible zonal factors. It is a fundamental problem to compensate for the boundary effect and enhance the model predictive performance. Compared to conventional approaches, it might be more reasonable to assign the boundary crashes according to the crash predisposing agents, since the crash occurrence is generally correlated to multiple sources of risk factors. In this study, the authors proposed a novel iterative aggregation approach to assign the boundary crashes, according to the ratio of model-based expected crash number in adjacent zones. To verify the proposed method, a case study using a dataset of 738 Traffic Analysis Zones (TAZs) from the county of Hillsborough in Florida was conducted. Using Bayesian spatial models (BSMs), the proposed approach demonstrated the capability in reasonably compensating for the boundary effect with better model estimation and predictive performance, as compared to three conventional approaches (i.e., half and half ratio method, one to one ratio method, and exposure ratio method). Results revealed that several factors including the number of intersections, road segment length with 35 mph speed limit, road segment length with 65 mph speed limit and median household income, were sensitive to the boundary effect.

• Record URL:
  • https://doi.org/10.1016/j.aap.2018.09.010
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457518306249
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhai, Xiaoqi
  • Huang, Helai
  • 0000-0003-2334-4124
  • Gao, Mingyun
  • Dong, Ni
  • Sze, N N
  • 0000-0002-2597-8107
• Publication Date: 2018-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 231-237
• Serial:
  • Accident Analysis & Prevention
  • Volume: 121
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash data; Traffic analysis zones; Traffic crashes; Traffic safety
• Uncontrolled Terms: Boundary effects; Zonal analysis
• Geographic Terms: Hillsborough County (Florida)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01684311
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 26 2018 10:34AM