Evaluation of Hot-Spot Identification Methods for Municipal Roads

Estimating crash prediction models and applying the Empirical Bayesian approach in identifying hot spots for roads under municipal jurisdiction is often challenging due to the lack of traffic count data. This study presents five hot spot identification (HSID) methods in which AADT information is not required (i.e. crash frequency (CF), equivalent property damage only (EPDO), relative severity index (RSI), excess predicted average crash frequency using method of moments (MOM) and cross sectional analysis (CSA)), to identify hot spots for road segments under municipal jurisdiction in Connecticut. The segments were categorized into eleven homogenous groups based on the roadway geometric characteristics. The five HSID methods were applied to all segments in each roadway group separately and across the entire State for a systemic analysis. Four quantitative tests (i.e. site consistency test (SCT), method consistency test (MCT), total rank difference test (TRDT) and total score test (TST)) were used to compare the performance of the five HSID methods. The results indicate that the MOM outperforms others in identifying hot spots for urban one-way arterials, urban one-way local roads, urban two-lane two-way local roads, urban multilane two-way arterials, and urban multilane two-way collectors; the CF outperforms others for rural arterials and collectors, rural local roads, urban one-way collectors, urban two-lane two-way arterials, urban two-lane two-way collectors and urban multilane two-way local roads, and the CSA performs best in all of the five HSID methods in identifying and ranking the roadway hot spots for all roadway groups together.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANB10 Standing Committee on Transportation Safety Management.
• Authors:
  • Wang, Kai
  • Zhao, Shanshan
  • Ivan, John N
  • Ahmed, Ishraq
  • Jackson, Eric
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: 4p

Subject/Index Terms

• TRT Terms: Cities and towns; Crash rates; Crash severity; High risk locations; Mathematical prediction; Rural highways; Urban highways
• Geographic Terms: Connecticut
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01664138
• Record Type: Publication
• Report/Paper Numbers: 18-05210
• Files: TRIS, TRB, ATRI
• Created Date: Mar 23 2018 5:14PM