Evidence map›Paper›PMID 40199973›Full record

ArticleScientific reports2025

A study on the correlation between MBTI dimensions and driving behavior characteristics.

Liwei Bai, Tao Wang, Jianyao Tu, Bozhezi Peng, Zhuoyu Wang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Liwei BaiSchool of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Tao WangSchool of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. wangtao127@sjtu.edu.cn.
Jianyao TuSchool of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Bozhezi PengSchool of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Zhuoyu WangSchool of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Funding

Science and Technology Commission of Shanghai Municipality 23DZ1202400
6 · The paper itself

Abstract

With the rapid global increase in motor vehicle usage, road traffic injuries have emerged as the leading cause of injury-related deaths worldwide. Within the complex traffic system, although factors such as vehicle performance and road conditions significantly influence driving safety, the driver's personality traits remain a critical determinant of traffic accidents. Consequently, exploring the intricate relationship between driving behavior patterns and personality traits is essential for understanding the underlying causes of traffic injuries and developing effective intervention strategies. Grounded in the theoretical framework of the Myers-Briggs Type Indicator (MBTI), this study systematically examines the interaction between personality traits and driving behavior. Through an empirical analysis of driving behavior data, this research makes several notable contributions. First, it introduces the "Six Driving Behavioral Facets," a multidimensional framework for analyzing the relationship between personality traits and driving behavior. Second, the study employs the "inverse chi-square test" to uncover latent patterns in otherwise non-significant results. Using the K-modes clustering algorithm, this study identified significant imbalances in the distribution of MBTI personality dimensions across eight clusters, particularly in the Thinking-Feeling (T-F) dimension. For example, in Cluster 1, Thinking (T) individuals accounted for 10.84% of the total population, compared to 15.09% for Feeling (F) individuals, while in Cluster 5, T individuals represented 17.48%, compared to 10.53% for F individuals. Such pronounced differences in personality distributions across clusters highlight the relevance of MBTI traits in shaping driving behavior patterns. These findings provide theoretical support for personalized traffic management strategies and the optimization of autonomous driving systems.

Indexed as

Accidents, TrafficAutomobile DrivingPersonalityAdultFemaleHumansMaleMiddle AgedYoung AdultCluster analysisDriving behaviorMachine learningMBTIPersonality theoryTransportation engineering

Identifiers

PMID40199973
PMCPMC11978940

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.