Student of Infrastructure & Environmental Systems (INES) Ph.D. Program, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223-0001, USA.
Professor of Civil & Environmental Engineering Department, Director of IDEAS Center, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223-0001, USA.
Accid Anal Prev. 2018 Nov;120:101-113. doi: 10.1016/j.aap.2018.08.006. Epub 2018 Aug 10.
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.
本研究论文的重点是提取与网络内、交通、信号、人口统计学和土地利用特征相关的预测变量，按区域类型进行分类，并研究它们对闯红灯违规事故数量的影响。本研究使用了北卡罗来纳州夏洛特市的数据进行分析。在中央商务区（CBD）、城市和郊区三个不同区域类型中，分别选择了三组信号灯交叉口。每组包含六十个信号灯交叉口（共计 180 个信号灯交叉口）。从 2010 年 1 月到 2014 年 12 月，在每个选定信号灯交叉口附近发生的闯红灯违规事故数量被视为因变量，用于为每个区域类型开发事故预测模型。基于所有 180 个信号灯交叉口开发的事故预测模型与各区域类型的模型进行了比较。在三个不同区域中，有不同的预测变量在 95%置信水平下被认为是显著的。研究发现，在 CBD 区域交通量较大的信号灯交叉口进行人工或使用闯红灯摄像头（RLC）执法；在交通量较大、全红清空时间较长、水平混合非住宅和开放空间/娱乐类型土地利用密度较高的城市区域信号灯交叉口；在交通量较大、主要入口限速、次要入口车道数、全红清空时间以及周围有水平混合非住宅和零售类型土地利用的郊区信号灯交叉口进行执法，将有助于减少闯红灯违规事故的数量。
