Factors Influencing Drivers’ Tolerance for Large Vehicle Proportions

https://doi.org/10.7307/ptt.v37i5.952
urban traffic drivers’ tolerance to vehicles ordered logistic regression drivers K-means clustering algorithm

Authors

  • Hanbin WANG College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China
  • Jianxiao MA College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China
  • Wenyun TANG
    tangwy@njfu.edu.cn
    College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China
  • Chenyang YANG College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China
Vol. 37 No. 5 (2025)
Articles

Downloads

To enhance road traffic safety, this study develops a classification model to assess drivers’ tolerance of large vehicle proportions under varying road conditions. It explores how personal and socioeconomic factors influence this tolerance. Six road scenarios were designed with differences in large vehicle proportion, driving time, vehicle types and road types. Behavioural and willingness surveys collected drivers’ demographics and choices. K-means clustering segmented drivers into “low”, “medium” and “high” tolerance groups, accounting for 6.77%, 51.13% and 42.10%, respectively. Based on clustering results, an ordered logistic regression model further analysed factors influencing large vehicle tolerance. Tolerance correlated positively with household vehicle usage, annual household income, driving duration and weekly driving frequency, and negatively with urban GDP, vehicle ownership and peak congestion index, with age showing no significant effect. Additionally, linear trends were observed for urban peak congestion index, urban vehicle ownership, age and driving duration. In contrast, urban GDP, weekly driving frequency, annual household income and household vehicle usage showed curvilinear effects with gradually diminishing rates of change.