Modeling and mitigating fatigue-related accident risk of taxi drivers

Effect of Fitness-To-Drive and Metacognition on Road Traffic Injury Among Older Taxi Drivers: Hierarchical Modeling

Age-related cognitive and somatic motor skills changes have been linked to impaired driving abilities. Taxi drivers play an important role in providing public transportation services and security. This study aimed to examine the level of fitness-to-drive (FTD) and identify the predictors of self-reported traffic injury among the older taxi drivers. Taxi drivers 60 years and older in Bangkok and the metropolitan area were enrolled. Hierarchical regression models were carried out to examine the effects of demographics, FTD, and metacognition towards self-reported road traffic injury. Totally, 46.1% of the respondents were classified as at-risk drivers. Drivers with alcohol consumption and low risk perception toward road safety were more likely to experience road traffic injury. Regular assessment of physical and psychometric capacity among older taxi drivers could provide another empirical basis to improve public safety transport.

A comparative analysis of risk factors in taxi-related crashes using XGBoost and SHAP

Taxis play a crucial role in urban public transportation, but the traffic safety situation of taxi drivers is far from optimistic, especially considering the introduction of ride-hailing services into the taxi industry. This study conducted a comparative analysis of risk factors in crashes between traditional taxi drivers and ride-hailing taxi drivers in China, including their demographic characteristics, working conditions, and risky driving behaviors. The data was collected from 2,039 traditional taxi drivers and 2,182 ride-hailing taxi drivers via self-reported questionnaires. Four XGBoost models were established, taking into account different types of taxi drivers and crash types. All models showed acceptable performance, and SHAP explainer was used to analyze the model results. The results showed that for both taxi drivers, risk factors related to risky driving behaviors are more important in predicting property damage (PD) crashes, while risk factors related to working conditions are more important in predicting person injury (PI) crashes. However, the relative importance of each risk factor varied depending on the type of crashes and the type of taxi drivers involved. Furthermore, the results also validated certain interactions among the risk factors, indicating that the combination of certain factors generated a greater impact on crashes compared to individual factors alone. These findings can provide valuable insights for formulating appropriate measures to enhance road safety for taxi driver.

Can job stress, health status and risky driving behaviours predict the crash risk level of taxi drivers? New evidence from China

Despite statistics indicating that China has the world's largest taxi industry, there exists limited research about the relationship between workplace health hazards and taxi driver occupational crashes. In this paper, a cross-sectional survey of taxi drivers in four typical Chinese cities was conducted, and data on their self-reported job stress, health status, and daily risky driving behaviours, together with crash involvement experience in the two years before the survey was collected. Three hypotheses were then developed, and they were verified via multivariate analysis of variance (MANOVA) that the seriousness of drivers' health problems and the frequency of their daily risky driving behaviours could be the accurate predictor of their crash risk of taxi drivers. These factors were subsequently substituted in a bivariate negative binomial (BNB) distribution model to determine the joint rate of at-fault taxi drivers' involvement in property-damage-only (PDO) and personal-injury (PI) crashes. The results offer some useful advice for policy development to decrease and prevent professional taxi drivers from causing severe traffic crashes.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

Research gaps and needs for preventing worker fatigue in the transportation and utilities industries

BACKGROUND

The transportation and utilities industries include establishments engaged in the movement of passengers and freight, or the provision of public power, water, and other services. Along with the warehousing industry, they make up the US National Occupational Research Agenda's Transportation, Warehousing and Utilities (TWU) industry sector. In 2018 the sector composed 5% of the US workforce, with approximately 8 million workers. TWU workers experienced 19% of all fatalities among U.S. workers in 2018 and 7% of total occupational injuries and illnesses.

METHODS

Around-the-clock operations, heavy workloads, long and irregular shifts, complicated schedules, and time pressures characterize work across the US TWU sector. However, there are considerable differences in worker priorities and concerns between TWU industries. Major areas of concern within the sector include disparities in work schedules; required training for employee fatigue awareness and prevention; physical and mental job demands; and safety culture.

RESULTS

Strategies for fatigue mitigation are critical to reduce the prevalence of injuries, safety-critical events, and crashes in TWU workers. Further research on the incidence and characterization of fatigue among TWU workers will guide the development of effective mitigation strategies. The influence of work scheduling on missed sleep opportunities and disrupted circadian rhythms should be determined. Evaluation of fatigue mitigation strategies can lead to the adoption of the most effective ones for each TWU industry.

CONCLUSION

Implementation of effective strategies is critical for the health, safety, wellbeing, and productivity of workers in the TWU sector.

Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations

Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the "pedestrian priority" policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.

Age, gender, personality, burnout, job characteristics and job content as predictors of driver fatigue

Objectives. Several studies have shown that one of the most common causes of collision is driver fatigue since fatigue causes drowsiness while driving and this decreases the driver's ability to maneuver the vehicle and increases the probability of their nodding off and falling asleep at the wheel. This may be due to a variety of personal reasons and specific factors connected to working conditions. In the present work we therefore intend to develop a predictive model for fatigue in professional drivers using the following indicators: age, gender, personality, burnout, characteristics and job content. Method. The participants were 516 professional drivers from different transport sectors, obtained through non-probabilistic sampling. SPSS version 25.0 was used for data analysis. Results. The predictive capacity of a number of variables that affect drivers by causing fatigue is determined. Fatigue can be predicted through certain variables, with the best predictor being exhaustion (48.8%). Conclusions. This research contributes to a greater knowledge of the factors that produce fatigue in professional drivers. It highlights the importance of designing interventions to reduce the incidence of fatigue, resulting in greater well-being for the driver and a lower incidence of collisions.

Spanish Adaptation of the Groningen Sleep Quality Scale (GSQS-8)

Professional drivers are a group susceptible to sleep problems or incorrect rest patterns resulting from work stress that causes alterations in biological stress markers, such as cortisol, or in cardiovascular parameters that show a state of physiological hyper-activation. The current research objective was to adapt and validate the Groningen Sleep Scale (Meijman et al., 1990) in a Spanish population. We analysed its internal structure, reliability and evidence of validity. The participants in this study were 372 drivers (93.4% men, 6.6% women), with a mean age of 40.9 (SD = 10.54), obtained through non-probabilistic sampling. The SPSS 23.0 and AMOS (5.0) programs were used. With the confirmatory factor analysis (CFA) of the AMOS program (5.0), the indicators NFI = 0.902; TLI = 0.844; CFI = 0.913; RMSEA = 0.129 were obtained and showed an acceptable adjustment of the unifactorial model with 8 items.  Adequate reliability (0.90) and appropriate evidence of validity with TDS-38, MBI-GS, Irritation, MP-9, DII, and Trans-18. We can conclude that the Groningen Sleep Scale (GSQS-8) is a reliable and valid instrument, suitable in the Spanish language for evaluating the sleep quality of professional drivers.

DF-8: a specific scale for assessing work fatigue in professional drivers

The study objective is to create a scale specifically for measuring driver fatigue and to analyze the scale's psychometric properties. The participants were 518 Spanish drivers. We carried out an exploratory factor analysis (EFA) and the first subsample obtained a single-item solution (eight items). We then performed a confirmatory factor analysis (CFA) with a second subsample. The results were root mean square error of approximation (rmsea) = 0.05, comparative fit index (CFI) = 0.94 and Tucker-Lewis index (TLI) = 0.92, which corroborates the previous results and maintains the same number of elements. The resulting dimension shows good reliability. The scale scores were then related to several external correlates and other scales, and showed good convergence and criteria validity. The results indicate that the scale for assessing work fatigue specifically in professional drivers - driver fatigue (DF-8) - is a reliable and valid instrument.

Influence that job characteristics, personality and burnout have on fatigue in professional drivers

Objectives. Professional drivers drive for many hours without rest. This factor, in addition to the characteristics of the job, the vehicle, the environment and the driver, causes driver fatigue. Fatigue is one of the most common risk factors when driving because it causes drowsiness, decreases drivers' attention and may make them fall asleep at the wheel. In this article we propose a predictive model for professional drivers using the following variables: age, number of children, time spent at work, time spent inside the vehicle, personality, job characteristics (JDS), job content (JCQ) and burnout. Method. Participants were 509 professional drivers from various transport sectors recruited by non-probabilistic sampling. SPSS version 25.0 was used for statistical analysis. Results. The predictive capacity of variables that cause driver fatigue was determined. Exhaustion best predicts fatigue positively, while openness to experience best predicts it negatively. Burnout and certain personality characteristics are good predictors, whereas other variables, such as JCQ and JDS, are weak predictors. Conclusions. This study extends our knowledge of the factors that cause fatigue in professional drivers and underlines the importance of designing interventions aimed at reducing the incidence of fatigue, promoting greater driver well-being and lowering the incidence of accidents.

Comparison of Physical, Occupational, and Sociocognitive Characteristics of Corporate and Private Taxi Drivers in Korea

Taxis are a form of public transport which is very closely related to the safety of the public. Although private and corporate taxis have quite different characteristics, there have only been a few studies comparing the characteristics of corporate and private taxis. Moreover, among various characteristics, research was conducted mainly focusing on occupational characteristics. This study was undertaken to compare various physical, occupational, and sociocognitive characteristics of corporate and private taxi drivers. A descriptive cross-sectional study was conducted from 22 August to 11 September 2018. The subjects of this study were 960 corporate and private taxi drivers over 30 years old in Seoul to compare the means and association between private and corporate taxi drivers’ characteristics. In terms of the physical characteristics, corporate taxi drivers’ general physical health status was worse. In terms of the occupational characteristics, corporate taxi drivers had a high working intensity, and the incidence rate of traffic accidents and near misses was also high. This comparison of the characteristics of corporate and private taxis is expected to serve as evidence for developing tailored policies and programs to improve the health of corporate and private taxi drivers.

A data mining approach to deriving safety policy implications for taxi drivers

INTRODUCTION

Traffic safety issues associated with taxis are important because the frequency of taxi crashes is significantly higher than that of other vehicle types. The purpose of this study is to derive safety implications to be used for developing policies to enhance taxi safety based on analyzing intrinsic characteristics underlying the cause of traffic accidents.

METHOD

An in-depth questionnaire survey was conducted to collect a set of useful data representing the intrinsic characteristics. A total of 781 corporate taxi drivers participated in the survey in Korea. The proposed analysis methodology consists of two-stage data mining techniques, including a random forest method, with data that represents the working condition and welfare environment of taxi drivers. In the first stage, the drivers' intrinsic characteristics were derived to classify four types of taxi drivers: unspecified normal, work-life balanced, overstressed, and work-oriented. Next, priority was determined for classifying high-risk taxi drivers based on factors derived from the first analysis.

RESULTS

The derived policies can be categorized into three groups: 'the development of new policies,' 'the improvement of existing policies,' and 'the elimination of negative factors.' Establishing a driving capability evaluation system for elderly drivers, developing mental health management programs for taxi drivers, and inspecting the taxi's internal conditions were proposed as new policies. Improving the driver's wage system, supporting the improvement of rest facilities, and supporting the installation of security devices for protecting taxi drivers are methods for improving existing policies to reinforce the traffic safety of taxi drivers. Last, restricting overtime work for taxi drivers was proposed as a policy to eliminate negative factors for improving taxi traffic safety. Practical Applications: It is expected that by devising effective policies using the policy implications suggested in this study, taxi traffic accidents can be prevented and the quality of life of taxi drivers can be improved.

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

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.

Work-related factors, fatigue, risky behaviours and traffic accidents among taxi drivers: a comparative analysis among age groups

The main purpose of this study was to investigate the effect of work-related factors, fatigue, risky behaviours on accident involvement among different age groups of taxi drivers in China. A total of 2391 taxi drivers were selected to complete a self-reported questionnaire about their demographic data and information on working conditions, fatigue, risky behaviours, as well as involvement in traffic accidents between 2014 and 2016. The drivers were divided into three categories according to their age. Then, a set of comparative analyses and three structural equation models were used to analyze the samples of specific age groups. The results indicated that taxi drivers in the younger group rest the least with the most dissatisfaction with income while those in the mid-age group worked the longest time and were charged the most management fee, but the older taxi drivers more frequently engaged in risky behaviours and traffic accidents. Furthermore, two mediating chain processes were confirmed (i.e. 'work-related factors - fatigue - accidents' and 'work-related factors - risky behaviours - accidents') across the three age groups. However, the causes of fatigue, risky behaviours and accidents in different age groups are not exactly the same. These findings suggest that the regulation of the taxi industry should be carefully improved. Incentive policy and education aimed at taxi drivers may also hold promise.

A random parameter logistic model of fatigue-related motorcycle crash involvement in Hanoi, Vietnam

Since motorcycle taxi drivers often work long hours, fatigue would affect their riding abilities, impacting crash risks. However, there is limited understanding about motorcycle taxi drivers' fatigue-related crashes. This study investigates self-reported fatigue-related crashes among motorcycle taxi drivers in Hanoi, Vietnam. Results from a survey showed that approximately 16% of the motorcycle taxi drivers reported fatigue-related crash involvement. It was also found that nearly 37% of all crashes reported by motorcycle taxi drivers were related to fatigue while riding a motorcycle taxi. Results of the heterogeneity-in-means random parameter logistic model suggested that working fulltime, more delivery trips, and overweight conditions were associated with increased likelihoods of fatigue-related crash involvement. Hybrid taxi drivers, who operate as either traditional or ride-hailing taxi drivers at different times, and most ride-hailing taxi drivers had a reduced likelihood of fatigue-related crash involvement when compared to traditional taxi drivers. Overall, this study has revealed a significant issue of fatigue-related crashes among motorcycle taxi drivers. Immediate interventions via publicity or educational campaigns should be considered by authorities to address this important issue. Ride-hailing companies should contribute by sending warnings of excessive riding hours to ride-hailing taxi drivers.

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

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. Treatment-related 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 taxi-driver 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, taxi-driver smokers were more likely to report somnolence/fatigue during varenicline treatment and physicians should pay more attention to this occupational population.

How does financial burden influence the crash rate among taxi drivers? A self-reported questionnaire study in China

Objective: Taxis play an important role in the transportation system of China, but they have a relatively high accident rate. The current study discusses the driver's financial burden in the Chinese context and explores its correlation with working conditions, risky driving behavior, and other characteristics of taxi drivers who are involved in accidents.Method: A total of 2,391 taxi drivers from 29 companies in four Chinese cities were interviewed and then asked to complete a questionnaire concerning their socio-demographic characteristics, working conditions, risky driving behavior, and accident frequency during the previous two years. Given the increase in the management fee (measured in CNY) charged by taxi companies, the drivers were divided into three groups: the "less than 150" group, the" 150 to 180" group and the "over 180" group, where were named Group 1, Group 2 and Group 3, respectively. Finally, the zero-inflated Poisson model was used to investigate the factors that contributed to the accident rate for each group.Result: The significant factors that lead to accidents differed significantly for drivers with different levels of financial burden. First, most of the factors were weakly correlated with the crash rate among Group 1 drivers. Second, many factors related to working conditions and risky driving behavior were significant for drivers in Groups 2 and 3, while working hours and off-duty days were significant only for drivers in Group 3. Third, working hours were negatively correlated with accident rates for drivers in Group 3, and the drivers who suffered from the heaviest financial burden were most affected by fatigue and sleep problems.Conclusion: Financial burden is the root cause behind the propensity of taxi drivers to be involved in accidents. Taxi companies should find ways to reduce drivers' expenses, and new technologies, such as taxi-calling or location and navigation based on mobile applications, should be introduced into the traditional taxi industry.