Civil & Environmental Engineering, Center for Transportation Policy Studies, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States.
Accid Anal Prev. 2011 Jan;43(1):439-46. doi: 10.1016/j.aap.2010.09.014. Epub 2010 Oct 30.
The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. The average number of pedestrian crashes per year within 200 feet of each intersection was considered as the dependent variable whereas the demographic characteristics, socio-economic characteristics, land use characteristics, road network characteristics and the number of transit stops were considered as the predictor variables. The Pearson correlation coefficient was used to eliminate predictor variables that were correlated to each other. Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25mile, 0.5mile and 1mile buffer widths to extract data and develop models was also evaluated.
（1）检验行人碰撞与预测变量（如人口和户数、社会经济特征（平均收入和总就业）、土地利用特征、道路网络特征（车道数、限速、中央分隔带的存在，以及行人和车辆的数量）和公共交通系统可达性）之间的非线性关系；（2）通过行人活动水平和与信号交叉口的空间接近程度，开发广义线性行人碰撞估计模型（基于负二项分布以适应数据的过离散），以提取信号交叉口的特定地点数据。本研究使用了北卡罗来纳州夏洛特市 176 个随机选择的信号交叉口的数据，以检验非线性关系并开发行人碰撞估计模型。每个交叉口 200 英尺范围内每年行人碰撞的平均数量被视为因变量，而人口特征、社会经济特征、土地利用特征、道路网络特征和过境站数量被视为预测变量。皮尔逊相关系数用于消除彼此相关的预测变量。然后分别为所有信号交叉口、高行人活动信号交叉口和低行人活动信号交叉口开发模型。还评估了使用 0.25 英里、0.5 英里和 1 英里缓冲区宽度提取数据和开发模型的方法。
