Highway Accident Modeling and Forecasting in Winter

Environmental attributes are critical risk factors that have proven to affect collision rates. Associated driving risks can be reduced by better maintenance of roadway infrastructure, enforcement of speed limits or other traffic laws. Given the preventive nature of these policies and regulations, accurate predictions of environmental attributes are needed. Currently, most of road safety prediction models are based on deterministic weather forecasts which are not able to capture changes in the likelihood of collision occurrence. As a result, probabilistic forecast is required to improve decision making, mainly in winter. In this paper, a stochastic approach to modeling highway collisions in winter time is considered which enables better assessment of driving conditions and a more accurate prediction. A logistic regression model with covariates is applied to crash data where environmental characteristics are modeled as a finite state space homogeneous multivariate discrete time Markov chain. After fitting the model, weather prediction as well as the conditional predictive probability distribution of collision occurrence are obtained. As the application, the ability of the proposed model to predict hourly environmental attributes and collision occurrence is examined using real highway crash data. The performance of the developed stochastic model is compared with several existing models in the literature using actual collision data. The results demonstrate that the proposed stochastic model outperforms existing models and it accurately predicts collision occurrence in the presence of stochastically changeable winter weather conditions. As a result, the proposed probabilistic forecast model can be used as a valuable tool in a decision support system.