Boundary Crash Data Assignment in Zonal Safety Analysis: an Interactive Approach based on Bayesian Spatial Model

Boundary effect refers to the issue of ambiguous allocation of boundary crashes leading to unreliable and inaccurate estimation in zonal safety analysis. Research problem arises 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 related to multiple sources of risk factors. In this study, the authors proposed a novel iterative boundary crash aggregation approach to assign the boundary crashes, according to the ratio of model-based expected crash number in adjacent zones. To evaluate the proposed method, a case study on a dataset of 738 Traffic Analysis Zones (TAZs) from the county of Hillsborough in Florida was carried out. 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 than the three traditional methods found in recent literature (i.e., half and half ratio method, one to one ratio method, and exposure ratio method). Results revealed several factors which were sensitive to the boundary effect, 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.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ80 Standing Committee on Statistical Methods.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Zhai, Xiaoqi
  • Huang, Helai
  • Gao, Mingyun
  • Dong, Ni
  • Sze, N N
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 5p

Subject/Index Terms

• TRT Terms: Crash risk forecasting; Data analysis; Spatial analysis; Traffic analysis zones
• Uncontrolled Terms: Bayesian models
• Geographic Terms: Hillsborough County (Florida)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01697886
• Record Type: Publication
• Report/Paper Numbers: 19-01065
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM