Modeling the risk of wrong-way driving at the exit ramp terminals of partial cloverleaf interchanges

Abstract

Introduction: Partial cloverleaf (parclo) interchanges with closely spaced parallel entrance and exit ramps are more prone to wrong-way driving (WWD) compared to other interchange types. In this study, a logistic regression model was developed to predict the risk of WWD at the exit ramp terminals of parclo interchanges. Method: The logistic regression model was developed using Firth’s penalized likelihood techniques based on the predictor variables such as exit ramp geometric design features, wrong-way related traffic control devices, area type, and traffic volume. Results: According to the model, the significant predictors of WWD at parclo exit ramp terminals include corner radius from crossroad to entrance ramp, type of median on crossroad, width of median on two-way ramp, channelizing island, distance to the nearest access point, “Keep Right” sign, wrong-way arrow, intersection signalization, and traffic volume at the exit and entrance ramps. This model was used to conduct network screening for all the exit ramp terminals of parclo interchanges in Alabama and Georgia to identify high-risk locations in these two states. Seven high-risk locations were monitored by video cameras for 48-hours to observe the occurrences of WWD incidents. Results suggest that two locations in Alabama and two locations in Georgia experienced multiple WWD incidents within 48-hours of a typical weekend. Conclusion: The observation of WWD incidents at high-risk locations demonstrates strong evidence that the model could identify the exit ramp terminals with high risk of WWD. Practical Applications: Transportation agencies can use this model to assess the risk of WWD at the exit ramp terminals within their jurisdictions and identify the high-risk locations for countermeasures implementation.