Analysis of factors influencing expressway speeding behavior in China

Liang, Zijun; Xiao, Yitian

doi:10.1371/journal.pone.0238359

Cited by 7 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Behavioral experience had a signifcant positive impact on the intention of fatigue-driving behavior, and it also had a signifcant positive impact on fatigue-driving behavior. Liang and Xiao [29] conducted an analysis of the infuencing factors of speeding behavior on Chinese highways, introducing punishment avoidance to correct TPB. In the process of model building, the mutual infuence relationship between punishment avoidance and TPB factors was considered, especially the direct impact of punishment avoidance on speeding behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Risky Driving Behavior of Young Truck Drivers: Improved Theory of Planned Behavior Based on Risk Perception Factor

Liang,

Zhan,

Deng

et al. 2024

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

In response to the issue of young truck drivers’ weaker perception of potential risks, which makes them more prone to engaging in risky driving behaviors, the direct influence of risk perception on behavior was innovatively considered. An improved theory of planned behavior (TPB) model was developed and a study on risky driving behavior among young truck drivers was conducted. Valid questionnaire data from 330 young truck drivers in China were collected, and the improved TPB model was validated and analyzed through structural equation modeling. The results indicate that the improved TPB model can effectively explain the risky driving behavior among young truck drivers. Specifically, attitudes toward behavior, subjective norms, and perceived behavioral control have significant positive effects on behavioral intention, while behavioral intention and perceived behavioral control have significant positive effects on behavior. In addition, risk perception has a significant negative effect on behavioral intention and behavior. Furthermore, a comparison with the traditional TPB model reveals that the improved TPB model performs better in terms of fit and explanatory power. Fit indices CMIN/DF, RMSEA, and AGFI were optimized by 16%, 18%, and 1.5%, respectively, and there was a 5% increase in explanatory power for behavior variance, validating the rationality and effectiveness of the improved TPB model. This provides decision support for the development of intervention measures for risky driving behavior among young truck drivers in the future.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Research on Risky Driving Behavior of Young Truck Drivers: Improved Theory of Planned Behavior Based on Risk Perception Factor

Liang,

Zhan,

Deng

et al. 2024

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

show abstract

“…This inclination may be associated with the impulsive and adventurous traits often attributed to young males. Available studies show that men report significantly more risk-driving behaviors than women and that anger, impulsivity, and risk driving are common among young male drivers [50]. However, when distraction characteristics are considered as a mediating factor, the impact of gender on event outcomes in this driving cluster takes on a contrasting pattern.…”

Section: Demographic Characteristicsmentioning

confidence: 99%

Analysis of factors affecting crash under risk scenarios based on driver homogenous clustering

Zheng,

Li,

Ding

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.

show abstract

“…(1) Number of trips (NT) Number of trips for a user is defined as the total number of trips in the study period, which can reflect the travel frequency of the expressway users. In this paper, we divide NT values into five groups (0, 5): 'NT 1', (5,10]: 'NT 2', (10,20]: 'NT 3', (20,50]: 'NT 4', (50, +∞): 'NT 5'.…”

Section: Features Of Expressway Usersmentioning

confidence: 99%

“…We divide UNT values into four groups. (0, 5): 'UNT 1', (5,10]: 'UNT 2', (10,15]: 'UNT 3', (15, +∞): 'UNT 4'.…”

Section: Features Of Expressway Usersmentioning

confidence: 99%

See 1 more Smart Citation

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

Cao¹,

Chen²,

Jia

et al. 2023

Sustainability

View full text Add to dashboard Cite

Understanding equity and travelers’ behavior plays a key role in creating suitable strategies to promote the development of the expressway. Especially, finding clusters of expressway users could help managers provide targeted policies in order to enhance service quality. However, it is challenging to identify expressway travel behaviors, such as traffic flow distribution and users’ classification. Electronic toll collection (ETC) has been widely applied to improve expressway management, because it can record the origin–destination information of users. This paper proposes a framework to analyze the equity and travel behavior of expressway users with a large amount of ETC data. In the first stage, the Gini coefficient is adopted to analyze expressway equity. In the second stage, 12 kinds of indicators are extracted, including number of trips, car type, mean distance, etc. In the third stage, kmeans algorithm is adopted to cluster the users, based on the introduced indicators. Finally, we analyze the traffic flow distribution of each group by constructing a traffic flow network. The results show that the Gini coefficient is 0.4193, which demonstrates evident inequity in the expressway service. Moreover, statistical analysis shows that expressway flow is complicated and 70.77% of travelers do not make repeat trips. It is demonstrated that expressway users can be divided into six groups, and the flow networks of cluster 2 and cluster 3 are connected more closely and evenly than other clusters are.

show abstract

Analysis of factors influencing expressway speeding behavior in China

Cited by 7 publications

References 21 publications

Research on Risky Driving Behavior of Young Truck Drivers: Improved Theory of Planned Behavior Based on Risk Perception Factor

Research on Risky Driving Behavior of Young Truck Drivers: Improved Theory of Planned Behavior Based on Risk Perception Factor

Analysis of factors affecting crash under risk scenarios based on driver homogenous clustering

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

Contact Info

Product

Resources

About