Mobility and safety are the two greatest priorities within any transportation system. Ideally, traffic flow enhancement and crash reductions could occur simultaneously, although their relationship is likely complex. The impact of traffic congestion and flow on road safety requires more empirical evidence to determine the direction and magnitude of the relationship. The study of this relationship is an ideal application for instrumented vehicles and surrogate safety measures (SSMs). The purpose of this paper is to correlate quantitative measures of congestion and flow derived from smartphone-collected GPS data with collision frequency and severity at the network scale. GPS travel data were collected in Quebec City, Quebec, Canada, and the sample for this study contained data for more than 4,000 drivers and 20,000 trips. The extracted SSMs, the congestion index (CI), average speed ( V), and the coefficient of variation of speed (CVS) were compared with crash data collected over an 11-year period from 2000 to 2010 with the use of Spearman’s correlation coefficient and pairwise Kolmogorov–Smirnov tests. The correlations with crash frequency were weak to moderate. CI was shown to be positively correlated with crash frequency, and the relationship to crash severity was found to be nonmonotonous. Higher congestion levels were related to crashes with major injuries, whereas low congestion levels were related to crashes with minor injuries and fatalities. Surprisingly, V was found to be negatively correlated with crash frequency and had no conclusive statistical relationship to crash severity. CVS was positively correlated with crash frequency and statistically related to increased crash severity. Future work will focus on the development of a network screening model that incorporates these SSMs.
Development and analysis of eco-driving metrics for naturalistic instrumented vehicles
