Research on visual differences of exits of different grades of tunnels based on machine learning

Abstract

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.

Graphical abstract

Keywords

Traffic safety / Tunnel exit / Machine learning model / Fixation characteristics / Scanning characteristics