Risk Factors Contributing to Taxi Involved Crashes: A Case Study in Xi’an, China

Zhao, Yongqiang; Zhang, Jun; He, Xin

doi:10.3311/pptr.7742

Cited by 18 publications

(8 citation statements)

References 23 publications

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“…In Britain, 31% of fatal crashes and 26% of serious injury crashes involve occupational drivers [18]. A survey in Xi'an, China revealed that the average number of taxi crashes per month was 8.500 over the period of 2006 to 2011 [4]. Our previous study also showed that 47.34% of the occupational bus, taxi, lorry, company car and shuttle drivers in Xining, China had caused crashes at some point [19].…”

Section: Conclusion and Discussionmentioning

confidence: 97%

“…Many studies have been conducted to examine the significant effect of exposure to hazardous working conditions on taxi drivers' health and to identify potential risk factors contributing to road crashes, such as drivers' age, job experience [4], license, type of employment [5], and physical and mental health [3,6,7]. Generally, taxi drivers are regularly obliged to work long hours into the late night or early morning and are therefore vulnerable to fatigue or sleepiness while driving [3,4,8,9]. Moreover, occupational illness [10], irregular shifts [3], and income dissatisfaction [11] have also been significantly correlated with taxi drivers' risk of serious crashes.…”

Section: Introductionmentioning

confidence: 99%

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Predicting personal injury crash risk through working conditions, job strain, and risky driving behaviors among taxi drivers

Zhou

Wang

2018

Eur. Transp. Res. Rev.

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Introduction: Taxis play an important role among public transport modes in China, but there has been very little in-depth research regarding taxi drivers' crash risk. Thus, this study aimed to develop a quantitative method to predict taxi drivers' crash risk and identify contributory factors. Methods: Nine hundred fourty-eight professional taxi drivers in Xi'an, China completed an anonymous, structured face-to-face questionnaire reporting their demographic information, work-related stress, daily risky driving behaviors, and crash experience within the 3 years prior to the survey. A Negative-Binomial regression model was used to predict the risk of personal injury collisions for taxi drivers. Results: Drivers' 7 risky driving behaviors (e.g., disregarding red lights, speeding, aggressive driving, driving while sleepy or fatigued, etc.) were significantly and positively related to the risk of personal injury collisions, while driver's parking at will to pick up/drop off passengers was not a significant predictor of such risk for taxi drivers. Furthermore, driver's sociodemographic characteristics and level of occupational workload were not found to be significantly correlated with the personal injury crash risk. Conclusions: Risk traits appear to peak among male taxi drivers who drive more hours per day, pay high management fees, and frequently engage in risky behaviors while driving. These findings provide implications to design potentially useful policy initiatives as well as targeted safety promotion programs to prevent road crashes involving professional taxi drivers.

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Section: Conclusion and Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Predicting personal injury crash risk through working conditions, job strain, and risky driving behaviors among taxi drivers

Zhou

Wang

2018

Eur. Transp. Res. Rev.

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“…Taxis have recently taken on a considerable share of the transport market in China, because of their comfort, convenience and flexibility. Previous studies have focused on the safety records of taxi drivers [46][47][48], and on atmospheric pollutants in taxi cabins. For the taxi, just as for the private car, ventilation settings have been shown to have a significant impact on in-vehicle mass and number concentrations [39,49].…”

Section: Taximentioning

confidence: 99%

Exposure to particulate matter and life loss from traveling by four different common commuting modes

Zheng¹,

Qiu²

2020

Preprint

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Objective Traffic-related air pollution (TRAP) has an adverse effect on commuters. The primary aim of this article was to make a comparison of PM exposure among four common transportation modes, analyze various influencing factors for specific modes, and evaluate public health effects caused by commuting exposure. Method: The experimental database was integrated for the four modes’ 336 trips and 13 volunteers’ heart-rate data. Basic descriptive statistics were used to compare PM exposure levels, and multivariable linear regression model and Spearman correlation analysis were adopted to explore major influences on pollution concentration among the different modes. In human health evaluations, the inhalation dose model and life table method were employed to obtain inhaled dose and years of life expectancy loss (YLE). Results The results indicated that higher PM exposure was found in cycling (average PM10, PM2.5 and PM1.0 of 114.35, 72.37 and 56.51 µg/m3, respectively), followed by bus (116.29, 67.60 and 51.12 µg/m3 for the same pollutants, respectively), and then taxi (97.61, 58.87 and 45.11 µg/m3), and the lowest concentrations appeared in subways (55.86, 46.20 and 40.20 µg/m3). Background pollution concentration and relative humidity were the staple determining factors on PM exposure according to the analysis outcomes. The greatest YLE loss was found for cycling, and could result in per capita 5.51–6.43 months of life expectancy loss for the 15–64 age group. Conclusions Given the higher average PM concentration for cycling, substituting other modes (like subway) for cycling could reduce adverse health effects during severely polluted periods, before the completion of local dedicated infrastructure projects that might reduce bicyclists’ exposure. PM2.5 levels in taxi cabins were decreased greatly when the vehicles were powered by CNG or methanol as compared to traditional fuels, indicating that implementing energy structure strategies such as vigorously promoting clean energy could effectively reduce pollution emissions and residents’ exposure.

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“…Zhao et al (2015) noticed that for Xi'an city the taxi drivers with 3 to 5 years experience are at greater risk for crashes than other groups. The results of the present study corroborate with those found in Xi'an city.…”

Section: Fig 2 Steps Acquisition and Transmission Of Informationmentioning

confidence: 99%

Using Qualitative Study and GIS to Explore Road Accident Black Areas in Algeria

Oulha¹,

Boumediene²,

Amara

et al. 2016

Period. Polytech. Transp. Eng.

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Risk Factors Contributing to Taxi Involved Crashes: A Case Study in Xi’an, China

Abstract: Using the 612 valid samples deriving from the original 6259 crash reports in Xi'an from 2006 to 2011, comparative statistics and systematic

Cited by 18 publications

References 23 publications

Predicting personal injury crash risk through working conditions, job strain, and risky driving behaviors among taxi drivers

Predicting personal injury crash risk through working conditions, job strain, and risky driving behaviors among taxi drivers

Exposure to particulate matter and life loss from traveling by four different common commuting modes

Using Qualitative Study and GIS to Explore Road Accident Black Areas in Algeria

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