Modeling and mitigating fatigue-related accident risk of taxi drivers

Li, Musen Kingsley; Yu, Jiayi Joey; Ma, Liang; Zhang, Wei

doi:10.1016/j.aap.2018.11.001

Cited by 42 publications

(20 citation statements)

References 70 publications

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“…e results from Question A1 revealed that fatigue and drowsiness were found to be the most important risk factors for the driver. is is consistent with the results of [41] which considered fatigue as one of the most important factors that can cause an accident. In general, fatigue reduces the speed of reaction and increases the number of errors in decision making.…”

Section: Taxi Driver Road and Road Users And In-vehicle Systemssupporting

confidence: 92%

Evaluating Safety Issues for Taxi Transport Management

Aghabayk

Rejali

Samerei

et al. 2021

Journal of Advanced Transportation

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Taxi drivers face many problems every day including safety issues. The tendency to quickly transport passengers to their destinations for more income has resulted in dangerous driving behaviors leading to traffic violations. So, taxi drivers need appropriate support and training programs to improve safety and reduce the risk of crashes. Implementing different support and safety training programs requires an effective management system. There is a dearth of research on the safety issues of taxis from the perspective of taxi organization managers. This study aims to evaluate the safety issues of taxi transport management through a case study of the Tehran Taxi Organization. A questionnaire survey was conducted with 22 regional managers and 20 transportation specialists of the Tehran Taxi Organization. Issues related to taxi drivers, roads and road users, vehicles, and management systems were evaluated in the questionnaire. Participants determined the relevance level and priority ranking of each question. The level of agreement was then tested using the Kendall concordance test. According to the results, the use of GPS was selected as the best in-vehicle monitoring system that can be used to evaluate drivers in the fleet. Participants believed that passengers’ loading and unloading had the most risk for taxi users. The start-inhibit technology to detect open doors was unanimously evaluated as an efficient technology for taxi safety. With respect to educating taxi users, starting education in schools had the most relevance and priority. Recommendations for increasing the safety of taxis include the use of GPS in taxis to monitor and evaluate drivers, receiving crash reports from police and submitting monthly safety assessment reports, flexibility in drivers’ working hours’ schedule, providing training on drivers fatigue management, and evaluating drivers’ health.

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Section: Taxi Driver Road and Road Users And In-vehicle Systemssupporting

confidence: 92%

Evaluating Safety Issues for Taxi Transport Management

Aghabayk

Rejali

Samerei

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…A number of studies have shown that somnolence/ fatigue is one of the important risk factors affecting the driving behaviors of professional drivers and leads to traffic accidents [31][32][33] . Accordingly, FDA, FAA and FMCSA in US announced that pilots, air-traffic controllers, and truck and bus drivers are barred from taking varenicline for smoking cessation treatment and took these precautions to protect the public and mass transport [16][17][18] .…”

Section: Discussionmentioning

confidence: 99%

Safety of varenicline as an aid to smoking cessation in professional drivers and its impact on driving behaviors: An observational cohort study of taxi drivers in Beijing

et al. 2020

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INTRODUCTION Varenicline is an effective smoking cessation medicine. However, the possible adverse neuropsychiatric events reported by Food and Drug Administration for varenicline may cause safety problems for professional drivers. We aimed to investigate its safety and impacts on driving behaviors among taxi drivers in Beijing, China. METHODS An observational cohort study was conducted in a smoking cessation clinic in Beijing, China, between September 2017 and April 2018. Smokers with varenicline for smoking cessation were included and categorized into taxi-driver smokers (n=103) and non-taxi-driver smokers (n=119). All participants received varenicline up to 12 weeks and five standardized counseling sessions. Treatmentrelated adverse events (AEs) were collected in all participants and their impacts on driving behaviors were assessed in taxi-driver smokers. Multiple logistic regression analysis was used to examine potential risk factors for vareniclinerelated somnolence/fatigue. RESULTS The incidence of most treatment-related AEs was similar between taxidriver smokers and non-taxi-driver smokers, but treatment-related somnolence/ fatigue was more frequently reported in taxi-driver smokers (18.4% vs 6.7%; p=0.008). Most taxi-driver smokers (87.4%) reported that treatment-related AEs did not affect their driving behaviors, and no traffic accident was reported during treatment. CONCLUSIONS Varenicline appears to be a well-tolerated smoking cessation aid for Beijing taxi drivers and has less impact on driving behaviors. However, taxidriver smokers were more likely to report somnolence/fatigue during varenicline treatment and physicians should pay more attention to this occupational population.

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“…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…”

Section: Literaturesmentioning

confidence: 99%

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

Lee

Kim

Park

et al. 2020

IJERPH

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Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

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Modeling and mitigating fatigue-related accident risk of taxi drivers

Cited by 42 publications

References 70 publications

Evaluating Safety Issues for Taxi Transport Management

Evaluating Safety Issues for Taxi Transport Management

Safety of varenicline as an aid to smoking cessation in professional drivers and its impact on driving behaviors: An observational cohort study of taxi drivers in Beijing

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

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