Factors Influencing Drivers’ Tolerance for Large Vehicle Proportions

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.

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Publication Details

Journal
PROMET - Traffic&Transportation
Published
2025-09-25
DOI
https://doi.org/10.7307/ptt.v37i5.952
Primary Topic
Safety Warnings and Signage
Type
article
Field-Weighted Citation Impact
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article

Factors Influencing Drivers’ Tolerance for Large Vehicle Proportions

Jianxiao MA, Hanbin Wang, Wenting Tang, Chenyang Yang
PROMET - Traffic&Transportation
Safety Warnings and Signage
article

Factors Influencing Drivers’ Tolerance for Large Vehicle Proportions

Jianxiao MA, Hanbin Wang, Wenting Tang, Chenyang Yang
article en

Abstract

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.

PROMET - Traffic&TransportationVol. 37(5)
Nanjing Forestry University (CN)
Openalex Percentile: Top 70%
Safety Warnings and Signage
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