Modeling the Risk of Wrong-Way Driving at Freeway Exit Ramp Terminals

Abstract

Wrong-way driving (WWD) crashes are a critical safety issue on freeways. Although these crashes are rare in nature, they often result in severe injuries and/or fatalities. Typically, exit ramp terminals are the initial point of entry for most wrong-way drivers on freeways. Therefore, it is important for the transportation agencies to identify the exit ramp terminals with high risk of WWD and adopt a systemic safety approach to reduce the probability of their occurrence proactively before a crash happens. However, the rare nature of WWD crashes and the difficulty in identifying the actual entry points make it hard to assess the risk of WWD at a particular exit ramp terminal. To overcome this issue, in this study, logistic regression models have been calibrated for predicting the risk of WWD at the exit ramp terminals of full diamond and partial cloverleaf (parclo) interchanges. The geometric design features, usage of traffic control devices (TCDs), traffic volume, and area type were used as the potential predictors of WWD. To evaluate the performance of the calibrated models, they were used as a network screening tool to rank the exit ramp terminals of full diamond and parclo interchanges in Alabama from high to low risk of WWD and the occurrences of WWD events was observed over a 48-hour period using video cameras. The observation of WWD incidents at high-risk locations demonstrates strong evidence that the models calibrated in this study are capable of identifying the exit ramp terminals with high risk of WWD. Transportation agencies can use these models to assess the risk of WWD at the exit ramp terminals within their jurisdictions and identify the high-risk locations for countermeasures implementation.