01627591

Component

Locations where red light violations and related crashes happen and geospatial factors that influence the same vary by area type as well as demographic, socio-economic and land use characteristics within the vicinity of a signalized intersection. Extracting and examining the relationship between such geospatial predictor variables and crashes due to red light violation helps proactively identify where they could potentially happen and who may be involved in these crashes. Such findings are vital to identify, prioritize and allocate limited available transportation funds. Therefore, the focus of this research paper is on extraction of geospatial predictor variables pertaining to demographic, socio-economic and land use characteristics by area type and examining their influence on crashes due to red light violation. Data for the city of Charlotte, North Carolina was extracted and used for analysis. Three different sets of signalized intersections were selected in three different area types - Central Business District (CBD), urban and suburban areas. Each set is comprised of sixty signalized intersections (total 180 signalized intersections). Crashes due to red light violation from January 2010 to December 2014 within the vicinity of each selected signalized intersection was considered as the dependent variable to develop three crash estimation models, one for each area type. The crash estimation models by area type were compared with the crash estimation model developed considering all the 180 signalized intersections together. Different predictor variables were found to be significant at a 95% confidence level in different areas. Log-link model with Negative Binomial distribution was observed to best fit the data used in this research. Findings indicate that enforcement, either manually or using red light running cameras (RLCs), near public / private institutions and offices in the CBD area, densely populated areas in the urban area and near large lots of residential units in the suburban area would lead to a reduction in the number of crashes due to red light violation. Likewise, enforcing traffic laws in and around Hawaiian community in the urban area and Asian community in the suburban area would yield maximum safety benefits associated with reduction in crashes due to red light violation.

This paper was sponsored by TRB committee ANB40 Standing Committee on Traffic Law Enforcement.

01618707

17-05051

English

0000-0002-9112-8370

16p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Countermeasures; Demographics; Land use; Red light running; Socioeconomic factors; Traffic crashes

Geospatial analysis

Charlotte (North Carolina)

Data and Information Technology; Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2017 Paper #17-05051

TRIS, TRB, ATRI

Dec 8 2016 11:57AM