Seasonal crash prediction model for urban signalized intersections: Wisconsin southeast region

Highly aggregated data is conventionally used in transportation safety omitting seasonal variations and leading to important loss of information. States in the northern region of the United States experience significant weather variations with snowfall and ice events. Crash occurrence is the highest during the winter compared to other seasons. Therefore, seasonal safety performance models would enable better safety analysis and decision-making. A new modeling approach is proposed which takes into account seasonal crash prediction. A case study is provided with crashes, operational, and weather data of urban signalized intersections from the southeast region in Wisconsin. Four seasons were considered: winter, spring, summer, and fall.

The modeling approach consisted of the Multivariate Negative Multinomial to account for seasonal variations and severity classification. Functional forms of predictor variables were optimized. Measures of log-likelihood, Overdispersion, Cumulative Residual (CURE) plots, and Akaike Information Criterion (AIC) showed adequate model prediction accuracy.

Crash estimates were the highest during the winter and the lowest during the spring seasons. Estimates remained below or near the annual average for the summer and fall seasons. Model performance was evaluated and results showed that seasonal model prediction and observed crash rate variations as percentage of annual estimates were similar.

Seasonal estimates have a significant contribution in safety analysis in regions where snow and ice conditions are regularly experienced. Identifying locations that experience significantly higher number of crashes during the winter can contribute to target snow and ice-related crashes and evaluate the effectiveness of deicing materials, equipment, and practices.