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

作　　者：Fangtong Jiao Zhenwei Shi Lingyu Li Wenpin Xu Qing Lan 

机构地区：[1]School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo 255000,Shandong,China [2]Department of Transportation Engineering,Hebei University of Water Resources and Electric Engineering,Cangzhou 061001,Hebei,China [3]Hebei Higher Institute of Transportation Infrastructure Research and Development Center for Digital and Intelligent Technology Application,Cangzhou 061001,Hebei,China

出　　处：《Digital Transportation and Safety》2024年第3期75-81,共7页数字交通与安全（英文）

基　　金：supported by the National Natural Science Foundation of China(52302437);the Cangzhou Science and Technology Plan Project(213101011);the Science and Technology Program Projects of Shandong Provincial Department of Transportation(2024B28);the Doctoral Scientific Research Start-up Foundation of Shandong University of Technology(422049).

摘　　要：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.

关 键 词：Traffic safety Tunnel exit Machine learning model Fixation characteristics Scanning characteristics 

分 类 号：U45[建筑科学—桥梁与隧道工程] TP181[交通运输工程—道路与铁道工程]