Investigating Violation Behavior at Intersections using Intelligent Transportation Systems: A Feasibility Analysis on Vehicle/Bicycle-to-Infrastructure Communications as a Potential Countermeasure

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Virginia Tech

The focus of this dissertation is on safety improvement at intersections and presenting how Vehicle/Bicycle-to-Infrastructure Communications can be a potential countermeasure for crashes resulting from drivers' and cyclists' violations at intersections. The characteristics (e.g., acceleration capabilities, etc.) of transportation modes affect the violation behavior. Therefore, the first building block is to identify the users' transportation mode. Consequently, having the mode information, the second building block is to predict whether or not the user is going to violate. This step focuses on two different modes (i.e., driver violation prediction and cyclist violation prediction). Warnings can then be issued for users in potential danger to react or for the infrastructure and vehicles so they can take appropriate actions to avoid or mitigate crashes.

A smartphone application was developed to collect sensor data used to conduct the transportation mode recognition task. Driver violation prediction task at signalized intersections was conducted using observational and simulator data. Also, a naturalistic cycling experiment was designed for cyclist violation prediction task. Subsequently, cyclist violation behavior was investigated at both signalized and stop-controlled intersections. To build the prediction models in all the aforementioned tasks, various Artificial Intelligence techniques were adopted. K-fold Cross-Validation as well as Out-of-Bag error was used for model selection and validation.

Transportation mode recognition models contributed to high classification accuracies (e.g., up to 98%). Thus, data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. Driver violation (i.e., red light running) prediction models were resulted in high accuracies (i.e., up to 99.9%). Time to intersection (TTI), distance to intersection (DTI), the required deceleration parameter (RDP), and velocity at the onset of a yellow light were among the most important factors in violation prediction. Based on logistic regression analysis, movement type and presence of other users were found as significant factors affecting the probability of red light violations by cyclists at signalized intersections. Also, presence of other road users and age were the significant factors affecting violations at stop-controlled intersections. In case of stop-controlled intersections, violation prediction models resulted in error rates of 0 to 10 percent depending on how far from the intersection the prediction task is conducted.

Intersection safety, driver/cyclist violation prediction, transportation mode recognition, Machine learning