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

Abstract

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.

Psychosocial Work Factors, Job Stress and Strain at the Wheel: Validation of the Copenhagen Psychosocial Questionnaire (COPSOQ) in Professional Drivers

“…Hege et al (2019) and Sturm et al (2019) have also shown how adverse working conditions have been associated with adverse consequences in different spheres, such as physical and mental health, job performance, and occupational safety outcomes. In the particular case of transport workers, frequent conditions such as time pressure, difficult weather conditions, environmental overstimulation, and shift-work increase even more the risk of (e.g., performing risky road behaviors and suffering severe crashes involving injured or fatal victims Ba et al, 2018; Gómez et al, 2018).…”

Measuring job stress in transportation workers: psychometric properties, convergent validity and reliability of the ERI and JCQ among professional drivers

“…Further details on the scoring of this version of the ERI are presented in Table 5 . Job strain has been empirically associated with health outcomes of workers belonging to different industries [ 20 , 29 ] and, more specifically, its predictive value for both health problems [ 19 , 21 ] and safety records [ 44 – 46 ] has also been assessed among professional drivers.…”

Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data

“…Considered Factors Hagl and Kouabenan [16] Staubach et al [17] Needs for advanced driver assistance system (ADAS) Li et al [18] Working hours/Time spent on personal affairs/Fatigue level Meng et al [15] Fatigue level Wang et al [9] Level of job satisfaction Wang et al [9] Ba et al [19] Frequency of safety education by the company Vahedi et al [20] Working hours per week/Level of living satisfaction/Age Ishimaru et al [21] Drinking frequency Bulduk et al [22] Usual fatigue level/Age Lim et al [23] Amount of sleeping time…”