Exploring the impacts of speed variances on safety performance of urban elevated expressways using GPS data

Speed variation on urban expressways has been frequently noted as a key factor associated with high crash risk. However, it was often difficult to capture the safety impact of speed variance with spaced sensor measurements. As an alternative, this paper aims to leverage the use of the floating car data (FCD) to capture the speed variance in a morning rush hour on urban elevated expressways and examine its effect on safety. A semi-automatic filtering process was introduced to distinguish taxi GPS data points on the elevated expressways from the ones on the surface roads under the expressways. Subsequently, the standard deviation of the cross-sectional speed mean (SDCSM) and the cross-section speed standard deviation (MCSSD) were derived to capture the spatial and temporal speed variances, respectively. Together with other explanatory variables, both hierarchical and non-hierarchical Poisson-gamma measurement error models were developed to model the crash frequencies of the expressways. The modeling results showed that the hierarchical model performed better and both SDCSM and MCSSD were found to be positively related to the crash occurrence. This secures the need for addressing the impact of speed variation when modeling crashes occurred on the elevated expressways.