Influence of on-network, traffic, signal, demographic, and land use characteristics by area type on red light violation crashes.

The focus of this research paper is on extraction of predictor variables pertaining to on-network, traffic, signal, demographic, and land use characteristics, by area type, and examining their influence on the number of red light violation crashes. Data for the city of Charlotte, North Carolina was extracted and used for analysis. Three different sets of signalized intersections were selected in the three different area types - Central Business District (CBD), urban, and suburban areas. Each set is comprised of sixty signalized intersections (total 180 signalized intersections). The number of red light violation crashes from January 2010 to December 2014, within the vicinity of each selected signalized intersection, was considered as the dependent variable to develop crash estimation models 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 three 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), at signalized intersections with high traffic volume in the CBD area; at signalized intersections with high traffic volume, high all-red clearance time, near high density of horizontal mixed non-residential and open space/recreational type land uses in urban area; at signalized intersections with high traffic volume, speed limit on the major approach, the number of lanes on the minor approach, and all-red clearance time and areas surrounded with horizontal mixed non-residential and retail type land use in suburban areas, would lead to a reduction in the number of red light violation crashes.