Tunnels are vital in connecting crucial transportation hubs as transportation infrastructure evolves. Variations in tunnel design standards and driving conditions across different levels directly impact driver visual perception and traffic safety. This study employs a Gaussian hybrid clustering machine learning model to explore driver gaze patterns in highway tunnels and exits. By utilizing contour coefficients, the optimal number of classification clusters is determined. Analysis of driver visual behavior across tunnel levels, focusing on gaze point distribution, gaze duration, and sweep speed, was conducted. Findings indicate freeway tunnel exits exhibit three distinct fixation point categories aligning with Gaussian distribution, while highway tunnels display four such characteristics. Notably, in both tunnel types, 65% of driver gaze is concentrated on the near area ahead of their lane. Differences emerge in highway tunnels due to oncoming traffic, leading to 13.47% more fixation points and 0.9% increased fixation time in the right lane compared to regular highway tunnel conditions. Moreover, scanning speeds predominantly fall within the 0.25−0.3 range, accounting for 75.47% and 31.14% of the total sweep speed.
Establishing comparison events/crashes is among the key challenges in safety analysis. This study proposes a spatial consideration for predicting scooter crashes using Utah's five years of crash data. It involves creating buffers ranging from 5 to 250 ft from the point of the scooter crash to obtain comparison crashes. The appropriate variables were selected based on the literature and engineering judgment. The Binary Logistic Regression was then applied to determine the appropriate buffer based on the consistency in the direction and magnitude of the impact of predictor variables. Results indicate that three variables, the junction type, lighting condition, and weather condition, are susceptible to changes in the direction of impact. Moreover, the study findings reveal that as the buffer distance increases, the magnitude of the impact of the variables decreases. Based on the results, a buffer of less than 50 ft is deemed appropriate for various analyses due to consistency in direction and the magnitude of impact. Further, the study findings show that intersections, dark-lighted conditions, summer season, and right-turning movements are more likely to be associated with scooter crashes. These findings can be crucial to transportation agencies and practitioners in improving the safety of scooter riders.
