Addressing the road trauma burden in China: Exploring attitudes, behaviours, risk perceptions and cultural uniqueness

Fleiter, Judy; Watson, Barry C.

doi:10.1016/j.aap.2015.07.005

Cited by 12 publications

(3 citation statements)

References 37 publications

(39 reference statements)

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“…On the other hand, this finding might indicate a more severe problem for misuse among Chinese individuals and might result from the more extreme traffic load conditions in China than in other countries. With the annually increasing number of motor vehicles and the expanding road network, China currently confronts more severe road trauma [42,43], which leads to a major burden for urban commuters [2]. Longer regular commuting times increase the acceptance of and the willingness to use AVs [25].…”

Section: Discussionmentioning

confidence: 99%

The Effect of Acceptability and Personality on the Intention to Use Automated Vehicles among Chinese Samples

Jing

2021

Journal of Advanced Transportation

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The development of automated vehicles (AVs) has attracted increasing attention. Understanding public acceptance of AVs and their intention to use them, which are the primary aims of the present study, are especially important considering that increasingly more AVs will be moving on the road in the coming future. A total of 527 participants voluntarily and validly completed a series of questionnaires, including the automated vehicle acceptability scale (AVAS), Big Five Inventory (BFI), and some sociodemographic variables. The results of an internal consistency, reliability, and confirmatory factor analysis (CFA) confirmed the two-factor (contextual acceptability and impaired driving) structure of the AVAS. The Chinese public generally has a positive attitude towards AVs. In addition, our results indicate the possibility of the misuse of AVs. More importantly, the results reveal that contextual acceptability partially mediated the effect of agreeableness on the willingness to drive and the willingness to own AVs and fully mediated its effect on the willingness to rent AVs, while contextual acceptability and interest in impaired driving fully mediated the effect of the openness on the willingness to drive, own, and rent AVs. Manufacturers and retailers in the automotive vehicle industry should provide their customers with comprehensive information regarding the principles and limitations behind the system and the responsibility and obligations of the drivers to avoid misuse. Moreover, providing more targeted services according to customers’ different personality traits might be a useful sales technique.

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Section: Discussionmentioning

confidence: 99%

The Effect of Acceptability and Personality on the Intention to Use Automated Vehicles among Chinese Samples

Jing

2021

Journal of Advanced Transportation

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“…Different methodologies were used to study pedestrian injury severity. From an anonymous questionnaire conducted in a sample of 239 suspended drivers in China, the results show that pedestrians and drivers face difficulties in following traffic rules when they are distracted and they become more susceptible to crashes ( 16 ). Another study explored how traffic, environmental, and vehicle factors contributed to pedestrian injury severity at signalized and unsignalized intersections in Florida by using the mixed logit model ( 10 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Pedestrian Injury Severity: A Case Study of Using Extreme Gradient Boosting Vs Random Forest in Feature Selection

Misra

Bao

2023

Transportation Research Record: Journal of the Transportation R

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Walking and bicycling are lauded for their negative net carbon impact and for their health benefits. However, national crash statistics suggest that pedestrians are disproportionately harmed in any vehicle–pedestrian conflict situation. Although automated transportation in the future is anticipated to increase overall safety, multiple incidents involving automated vehicles have been reported recently, indicating that the technology needs more training on real-world scenarios and conflicts. This research is motivated by the need for contextual data and related levels of harm in potential conflict scenarios in mixed traffic and we use a national police reported crash dataset, CRSS, to address this need. Our study uses a new gradient boosting algorithm, XGBoost, to identify important features among a host of seemingly significant variables. We compare the performance of XGBoost with the more frequently used random forest method and find that XGBoost is more reliable and robust for handling an unbalanced and sparse dataset like crash data, and the features extracted are more aligned to findings from previous research on the topic. We also compare feature importance between NASS-GES and CRSS—two national crash databases with different sampling strategies but the same objective—and find that sampling strategy influences selection of feature importance. We further use the features extracted using XGBoost in a multiclass logistic regression to quantify the effect of these features on different levels of pedestrian injury. Our findings indicate that speed limit, light conditions, pre-crash movements, and location of pedestrian are important contributors to crash severity, along with driver distraction and impairment.

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“…From 2014 to 2020, the number and severity of e-bike injuries seen in emergency departments dramatically increased [ 11 ]. In China, ERTIs have become a significant problem, with accidents, injuries, and economic losses increasing as a proportion of RTIs [ 12 , 13 , 14 ]. For instance, in 2008, 5.4% of all traffic fatalities in China were caused by e-bike riders, which increased to 7.8% in 2012 and 12.0% in 2016 [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Risky Riding Behavior Characteristics of the Related Road Traffic Injuries of Electric Bicycle Riders

Huang

Song

Xie

et al. 2023

IJERPH

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Electric bicycle (EB) riders, being vulnerable road users (VRUs), are increasingly becoming victims of road traffic injuries (RTIs). This study aimed to determine the current status and epidemiological characteristics of RTIs among EB riders through a questionnaire survey and roadside observations in Shantou to provide a scientific basis for the prevention and control of electric bicycle road traffic injuries (ERTIs). A total of 2412 EB riders were surveyed, and 34,554 cyclists were observed in the study. To analyze the relationship between riding habits and injuries among EB riders, chi-square tests and multi-factor logistic regression models were employed. The findings reveal that the prevalence of ERTIs in Shantou was 4.81%, and the most affected group was children under 16 years old, accounting for 9.84%. Risky behavior was widespread among EB riders, such as the infrequent wearing of safety helmets, carrying people on EBs, riding on sidewalks, and listening to music with headphones while bicycling. Notably, over 90% of those who wore headphones while bicycling engaged in this risky behavior. The logistic regression analysis showed that honking the horn (odds ratio (OR): 2.009, 95% CI: 1.245–3.240), riding in reverse (OR: 4.210, 95% CI: 2.631–6.737), and continuing to ride after a fault was detected (OR: 2.010, 95% CI: 1.188–3.402) all significantly increased the risk of ERTIs (all p < 0.05). Risky riding behavior was significantly less observed at traffic intersections with traffic officers than at those without (all p < 0.001).

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Addressing the road trauma burden in China: Exploring attitudes, behaviours, risk perceptions and cultural uniqueness

Cited by 12 publications

References 37 publications

The Effect of Acceptability and Personality on the Intention to Use Automated Vehicles among Chinese Samples

The Effect of Acceptability and Personality on the Intention to Use Automated Vehicles among Chinese Samples

Modeling Pedestrian Injury Severity: A Case Study of Using Extreme Gradient Boosting Vs Random Forest in Feature Selection

Analysis of Risky Riding Behavior Characteristics of the Related Road Traffic Injuries of Electric Bicycle Riders

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