Safety Performance Functions to Assess the Safety Risk of Urban Residential Collector Roads

Previous research shows that various geometric and non-geometric road elements significantly affect collision occurrence and severity on highways and arterial roads; however, little is known about how these elements affect the safety performance of urban residential collector roads. Therefore, this study investigated the impact of these elements on collision occurrence and collision severity for urban residential collector roads. An extensive data collection effort was conducted to synthesize collision records, traffic counts, road geometry, traffic control and other features of residential collector road segments in the city of Edmonton (COE), Alberta, Canada. Negative binomial safety performance functions (SPFs) were developed for total collision occurrence and collision severity using four years of data. The proposed models were estimated using the maximum likelihood estimation technique under a Bayesian context. An outlier test was performed to improve the models’ goodness-of-fit. Scaled Deviance (SD) and the Pearson 2 statistic were used to assess the models’ goodness-of-fit. Results reveal that the exposure covariates (segment length and traffic volume) are highly significant and positively related to the predicted collisions in all of the SPFs. The property damage only (PDO) collision model has the same significant covariates as the total collisions model, indicating that the number of PDO collisions is predominantly higher than other collisions. For predicted total and PDO collisions, there is a statistically significant positive relationship between collisions and access-point density, stop-controlled intersection density, the presence of a horizontal curve, the presence of a licensed premises, the presence of a seniors’ centre and the presence of on-street parking. In contrast, there is a significant negative relationship between the presence of median and predicted total and PDO collisions. For severe (i.e., fatal and injury) collisions, there is a statistically significant positive relationship between collisions and segment length, traffic volume, number of lanes, access-point density, stop-controlled intersection density, bus stop density, the presence of a horizontal curve, the presence of a licensed premises, the presence of a seniors’ centre and the presence of on-street parking. On the contrary, there is a significant negative relationship between predicted severe collisions and the presence of a median, the presence of a centre line and the presence of manned enforcement sites. From a model application perspective, the city authority could use this information to assess the associated safety risk of different geometric and non-geometric road elements on residential collector roads and, hence, prioritize collision prone road segments.