The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China

Exploring driving anger-caused impairment of takeover performance among professional taxi drivers during partially automated driving

Partially automated systems are expected to reduce road crashes related to human error, even amongst professional drivers. Consequently, the applications of these systems into the taxi industry would potentially improve transportation safety. However, taxi drivers are prone to experiencing driving anger, which may subsequently affect their takeover performance. In this research, we explored how driving anger emotion affects taxi drivers' driving performance in various takeover scenarios, namely Mandatory Automation-Initiated transition (MAIT), Mandatory Driver-Initiated transition (MDIT), and Optional Driver-Initiated transition (ODIT). Forty-seven taxi drivers participated in this 2·3 mixed design simulator experiment (between-subjects: anger vs. calmness; within-subjects: MAIT vs. MDIT vs. ODIT). Compared to calmness, driving anger emotion led to a narrower field of attention (e.g., smaller standard deviations of horizontal fixation points position) and worse hazard perception (e.g., longer saccade latency, smaller amplitude of skin conductance responses), which resulted in longer takeover time and inferior vehicle control stability (e.g., higher standard deviations of lateral position) in MAIT and MDIT scenarios. Angry taxi drivers were more likely to deactivate vehicle automation and take over the vehicle in a more aggressive manner (e.g., higher maximal resulting acceleration, refusing to yield to other road users) in ODIT scenarios. The findings will contribute to addressing the safety concerns related to driving anger among professional taxi drivers and promote the widespread acceptance and integration of partially automated systems within the taxi industry.

Distracted on duty: A theory-based exploration of influences leading to mobile phone distracted riding among food delivery workers

Using mobile phones while riding is a form of distracted riding that significantly elevates crash risk. Regrettably, the factors contributing to mobile phone use while riding (MPUWR) among food delivery riders remain under-researched. Addressing this literature gap, the current study employs the Job Demands-Resources (JD-R) model and various socio-economic factors to examine the determinants of MPUWR. The research incorporates data from 558 delivery workers in Hanoi and Ho Chi Minh City, Vietnam. The study utilizes two analytical methods to empirically test the hypotheses, considering non-linear relationships between variables: Partial Least Square Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). The results reveal mixed impacts of factors connected to job resources. Although social support appears to deter MPUWR, work autonomy and rewards seemingly encourage it. Furthermore, a predisposition towards risk-taking behaviour significantly impacts the frequency of mobile phone usage among delivery riders. Interestingly, riders with higher incomes and those who have previously been fined by the police exhibit more frequent mobile phone use. The findings of this study present valuable insights into the crucial factors to be addressed when designing interventions aimed at reducing phone use among food delivery riders.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability

OBJECTIVE

This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks.

BACKGROUND

Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages.

METHOD

This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance.

RESULTS

Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values.

CONCLUSION

HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs.

APPLICATION

The established models can be used in realistic driving scenarios.

Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?

INTRODUCTION

Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved.

METHOD

In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country.

RESULTS

The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.

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.

Fatigue in taxi drivers and its relationship with traffic accident history and experiences: a cross-sectional study in the north of Iran

BACKGROUND

The monotonous nature of work, long driving duration, and working overload hours cause frequent fatigue in taxi drivers. A high prevalence of fatigue is associated with traffic accidents. However, the risk factors associated with taxi driver fatigue are unclear. Therefore, the present study aims to determine the rate of fatigue in taxi drivers and its relationship to their traffic accident experience.

METHODS

In this descriptive-analytical study, 400 taxi drivers in the city of Rasht were registered in Taxi association selected through random sampling and entered into the study based on inclusion criteria. Data was collected through a researcher-made questionnaire reliable and valid by two medical students. The statistical analysis used ordinal data and a Poisson regression model with SPSS software version 21, with a significance level set at 5%.

RESULTS

The driver fatigue self-reported was directly and significantly related to alcohol consumption (OR = 3.43, 95% CI 1.01-11.62) and had a significant and inverse relationship with smoking (OR = 0.50, 95% CI 0.32-0.76), being married (OR = 0.08, 95% CI 0.01-0.40) and driving experience there was (OR = 0.96, 95% CI 0.94-0.98). Drivers' sense of quality of life (QOL) was directly and significantly related to smoking (IRR = 1.43, 95% CI 1.28-1.59), education level under diploma (IRR = 2.41, 95% CI 1.43-4.06) diploma (IRR = 2.06, 95% CI 1.21-3.48) and bachelor (IRR = 2.42, 95% CI 1.36-4.29) and there was a significant and inverse relationship with age (IRR = 0.98, 95%CI 0.98-0.99). There was a significant relationship between the number of traffic accidents in the past year with the level of bachelor's degree (IRR = 3.10, 95% CI 1.43-6.76) and driving experience (IRR = 1.03, 95% CI 1.02-1.04 and inverse relationship between the number of traffic accidents in the past year and the QOL sense (IRR = 0.96, 95% CI 0.93-0.99) and the working hours (IRR = 0.96, 95% CI 0.94-0.99).

CONCLUSION

Legislators and policymakers should pay more attention to fatigue in single and inexperienced taxi drivers. Regarding the QOL, pay attention to drivers with high education and older. To reduce the number of crashes, pay more attention to drivers with a bachelor's degree and less driving experience and improve the feeling of QOL.

Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

Development and evaluation of a scale to measure nurses' unsafe driving behaviour while commuting

Driving is the most prevalent form of commuting for most workers but is also perhaps the most hazardous mode of travel with unsafe driving contributing significantly to road traffic accidents. Despite nurses having been reported as being at higher risk of commuter-related accidents over the last three decades, little is known about unsafe driving behaviours among nurses while commuting, which is unique from other driving routines. Additionally, the lack of appropriate tools to measure such behaviours is apparent. This study aims i) to identify unsafe driving behaviours among nurses while commuting and ii) to develop a scale to assess nurses' unsafe commuting driving behaviours. The study employed a multiphase and multimethod approach to develop the scale, which was subject to stringent validation and evaluation. Themes were specified via the Nominal Group Technique (NGT). Six themes were identified namely: i) violations and reckless driving, ii) negative emotions, iii) drowsy driving iv) mind wandering, v) error and vi) carelessness. Content and face validity were sought through expert review. A total of 442 nurses' data were collected across multisite hospitals for evaluation. Exploratory factor analysis (EFA) resulted in recovered structure and was confirmed through Confirmatory Factor Analysis (CFA) with structural equation analyses being conducted to test predictive validity. All constructs met adequate validity and reliability. Nurses' unsafe driving behaviours while commuting were identified with a novel scale to assess them being both developed and validated. The resulting MyUDWC scale is a suitable tool for measuring nurses' unsafe driving behaviours while commuting.

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.

The road safety and risky behavior analysis of delivery vehicle drivers in China

The delivery industry has seen dramatic growth in demand and scale in China. Due to the stock limitations and delivery time restrictions, the couriers may commit traffic violations while delivering, resulting in a pessimistic road safety situation. This study aims to reveal critical factors that influence delivery vehicle crash risks. A cross-sectional structured questionnaire survey is conducted to collect demographic attributes, workload, work emotions, risky driving behavior, and road crash involvement data among 824 couriers in three developed regions of China. The collected data is then analyzed through an established path model to identify the contributing factors of delivery road crash risks and risky behaviors. The road crash risk level (RCRL) indicator is defined by taking into consideration both frequency and severity. While the risky behaviors are defined by both their frequency and correlations to crash risks. The results indicate that 1) Beijing-Tianjin Urban Agglomeration has the highest road crash frequency and RCRL; 2) distracted driving and wrong-lane-use are among the top three risky behaviors for both Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration. For Beijing-Tianjin Urban Agglomeration, distracted driving, aggressive driving, and lack of protection are the top three risky behaviors; 3) time demand and personal efforts are important factors contributing to the cognitive workload of couriers; 4) objective workload can affect the cognitive workload and both workloads influence drivers' emotions (anxiety and anger); 5) the objective, cognitive workload, drivers' emotions influence the RCRL through their impacts on risky behavior but in different paths for three agglomerations. The findings highlight the importance of developing targeted countermeasures to reduce the delivery workers' workload, improve their performance on roads, and mitigate severe crash risks.

Safety and security perceptions in informal transport: the case of Tunisia

This study aims to evaluate the perception of safety and security in informal transport in Tunisia. This research employs a questionnaire-based survey of passengers and drivers of informal transport in Tunisia. Interviews were conducted with 215 informal transport Tunisian drivers and 255 Tunisian passengers, all above 18 years of age. Analyses have been made by employing several multivariate statistical analyses. Results showed that personal attributes of informal transport drivers influence their decision to violate traffic laws and to display risky driving behaviors. It was also found that driver skills, personality, education, experience, quality of the car, and information about the road congestion are the main factors influencing safety and security in informal transport. Non-compliance with the law by informal drivers leads to aberrant habits and behaviors as well as to a driving style beyond recognized standards. Informal drivers, police and local authorities are the parties involved in the low safety and security aspects of informal transport. It can be concluded that this study underlines that improving safety and security in informal transport cannot be achieved without improving awareness of the role of traffic laws and the need for their enforcement.

Applying systems thinking to improve the safety of work-related drivers: A systematic review of the literature

INTRODUCTION

Light vehicles (<4.5 tons) driven for work purposes represent a significant proportion of the registered motor vehicles on our roads. Drivers of these vehicles have significant exposure to the dangers of the road transport environment. To optimize safety for these workers, it is critical to understand the factors contributing to risk of being involved in an incident. This information can then be used to inform the review and revision of existing risk controls and the development of targeted prevention activities.

METHOD

The aim of the study was to undertake a systematic review of the literature to identify the factors associated with work-related driving incidents. The factors identified in the review were represented within an adapted version of Rasmussen's risk management framework (Rasmussen, 1997). Fifty studies were analyzed following data screening and review of full text. The highest proportion of risk factors were categorized at the lower levels of the system, including the 'Drivers and Other Road Users' level (n = 20, 44.4%) and the 'Equipment, Environment, and Meteorological Surroundings' level (n = 19, 42.2%). There were no risk factors identified at the 'Regulatory and Government Bodies' levels of the framework, confirming the narrow investigative scope of past research and the need to acknowledge a broader range of factors within and across higher levels of the system.

CONCLUSIONS

The findings of this study inform the direction of future research and design of targeted prevention activities capable of creating system change for the safety of work-related drivers.

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.

Driving Fine and its Relationship with Dangerous Driving Behaviour Among Heavy Vehicle Drivers

Context

There is a significant difference between actual and existing statistics of traffic fines; since some invisible fines and most of the visible traffic violations cannot be recorded by traffic officers. Therefore, dealing with driving fines and road fatalities is considered an important issue in social and public management worldwide.

Aims

Explore the factors associated with unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers.

Settings and Design

The present cross-sectional study was conducted in Iran, from February 2019 to September 2020.

Methods and Material

This study used the driver behavior questionnaire (DBQ), demographic and driving characteristics, the number of fines, and structural equation modeling. Also, in this study 320 professional drivers participated.

Statistical Analysis Used

This article used structural equation modeling for Statistical analysis.

Results

The results of structural equation modeling analysis indicated that the data fit well with the theoretical model proposed in this study. The number of fines was directly predicted by both demographic and driving characteristics and risky driving behaviors. A significant relationship was observed between, driving hours, driving experience, and smoking, respectively, with a mistake, slip, and risky violation. There was a negative correlation between education and all four sub-scales of risky driving behaviors.

Conclusions

In order to reduce traffic fines, training courses on increasing attention and precision in drivers' observations and judgments are useful. The courses can decrease traffic violations by trying to change beliefs, attitudes, and social norms. It is therefore helpful to understand the ways to change the drivers' attitudes.

Nudges-Based Design Method for Adaptive HMI to Improve Driving Safety

This study introduces a new operational tool based on the AEIOU observational framework to support the design of adaptive human machine interfaces (HMIs) that aim to modify people’s behavior and support people’s choices, to improve safety using emotional regulation techniques, through the management of environmental characteristics (e.g., temperature and illumination), according to an approach based on the nudging concept within a design thinking process. The proposed approach focuses on research in the field of behavioral psychology that has studied the correlations between human emotions and driving behavior, pushing towards the elicitation of those emotions judged to be most suitable for safe driving. The main objective is to support the ideation of scenarios and/or design features for adaptive HMIs to implement a nudging strategy to increase driving safety. At the end, the results from a collaborative workshop, organized as a case study to collect concept ideas in the context of sports cars, will be shown and evaluated to highlight the validity of the proposed methodology, but also the limitations due to the requirement of prototypes to evaluate the actual effectiveness of the presented nudging strategies.

Assessing the Australian occupational driver behavior questionnaire in U.S. taxi drivers: Different country, different occupation and different worker population

BACKGROUND

Promoting safe driver behaviors is an important aspect of road safety. To better understand road safety behaviors, there is a role for practical instruments that can validly measure typical road safety behaviors among occupational drivers. The Occupational Driver Behavior Questionnaire (ODBQ) was developed to assess road safety behaviors among home health nurses in Australia.

METHODS

We administered a cross-sectional survey to a sample of taxi drivers in two U.S. metropolitan areas. The survey included Newnam's ODBQ-12 and a study-specific 15-item version (ODBQ-15) assessing 4 different road safety behaviors with 3 more items added and motor-vehicle crashes in the past year. Logistic regression analyses examined the association of the road safety behaviors with motor vehicle crashes. A series of confirmatory factor analysis (CFA) models assessed the construct validity of the ODBQ-12 and ODBQ-15.

RESULTS

We pooled survey data from 497 Houston drivers and 500 Los Angeles drivers to assess study aims. CFA models examining the 12-item and the 15-item ODBQ versions had good model fit (Comparative Fit Index > 0.95, Tucker Lewis Index ≥ 0.95, root mean square error of approximation < 0.06, standardized root mean square residual ≤ 0.05). The ODBQ's road safety behaviors were significantly associated (p < 0.001) with crashes while working (ORs 0.51-0.75) and not working (ORs 0.57-0.84).

CONCLUSIONS

The ODBQ-12 and ODBQ-15 were both significantly associated with motor vehicle crashes among taxicab drivers in two large U.S. metropolitan areas. Researchers studying occupational drivers who transport passengers may want to consider using the ODBQ-15. The 3 additional items are meaningful to this workforce and are priority areas for international road safety efforts.

Analysis of risky driving behaviors among bus drivers in China: The role of enterprise management, external environment and attitudes towards traffic safety

Several studies have shown that enterprise management (e.g. culture, salary) and external environment (e.g. traffic congestion) predict risky driving behaviors and accident involvement. However, this process has not been systematically investigated in bus drivers. The present study uses structural equation model to assess whether enterprise management and external environment are associated with risky self-reported driving behaviors both directly and indirectly, through the effects of attitudes towards traffic safety in a large sample of bus drivers. Three hundred and thirty-one bus drivers (mean age = 39.5, SD = 5.6 years) completed a structured and anonymous questionnaire measuring enterprise management, external environment, attitudes toward traffic safety, and self-reported risky driving behaviors (i.e., speeding, fatigue driving, running the light) in the last 6 months. Structural equation modeling analysis revealed that enterprise management, and external environment were associated with risky driving behaviors both directly and indirectly. In particular both of them were directly correlated with bus drivers' attitudes toward traffic safety which, in turn, were related to the five types of self-reported risky driving behaviors. The present findings suggest that measures related to the impact factors could be carried out to reduce the probabilities of the risky driving behaviors among bus drivers, such as improving the salary level of bus drivers, setting up bus lanes and priority signals to alleviate road congestion, optimizing shift schedules, implementing effective safety education, etc. These findings can provide the empirical basis for evidence-based road safety interventions in the context of public transport.

Operational factor analysis of the aggressive taxi speeders using random parameters Bayesian LASSO modeling approach

Partial taxi speeders are observed with both high speeding frequency and severity (range). They thereby can be viewed as aggressive speeders whose behaviors may result in more hazards than others. Among the factors contributing to taxi speeding, the operational factors are proven to be deterministic. However, previous studies mainly investigate the operational factors of taxi speeding frequency, which fail to comprehensively unveil the impact of factors on speeders, especially for aggressive speeders. This study intends to disclose the operational factors affecting the aggressive taxi speeders with the random parameters Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach. Taxi speeding behaviors and several operational factors are extracted from taxi GPS trajectory data in Chengdu, China. Based on the hourly speeding frequency and average speeding severity of each speeder, the fuzzy C-means clustering algorithm is employed to categorize taxi speeders into three cohorts: restrained speeder (RS), moderate speeder (MS), and belligerent speeder (BS). Compared to RS, MS and BS are treated as the aggressive taxi speeders. Several binary logistic models are developed with RS as the reference category. The random parameters Bayesian binary logistic LASSO model that captures the unobserved heterogeneity and tackles the multicollinearity is found to be the best fit model to identify the significant operational factors. The results indicate that aggressive taxi speeders are linked to longer daily driving distance and cruise distance, shorter delivery time, higher hourly income, driving at night, and driving on low-speed limit roads. However, intensive lane-changes and sufficient daily naps do not contribute to aggressive taxi speeders. Moreover, BS is more sensitive to the operational factors than MS. This study stresses the necessity of implementing speeder classification in taxi driver management and conceiving countermeasures considering the operational factors which are significantly associated with the aggressive taxi speeders.

Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

In previous research, the effects of commercial vehicle proportions (CVP) on overall crash propensity have been found to be significant, but the results have been varied in terms of the effect direction. In addition, the mediating or moderating effects of roadway attributes on the CVP-vs-safety relationships, have not been investigated. In addressing this gap in the literature, this study integrates databases on crashes, traffic, and inventory for Hong Kong road segments spanning 2014-2017. The classes of commercial vehicles considered are public buses, taxi, and light-, medium- and heavy-goods vehicles. Random-parameter Tobit models were estimated using the crash rates. The results suggest that the CVP of each class show credible effects on the crash rates, for the various crash severity levels. The results also suggest that the interaction between CVP and roadway attributes is credible enough to mediate the effect of CVP on crash rates, and the magnitude and direction of such mediation varies across the vehicle classes, crash severity levels, and roadway attribute type in four ways. First, the increasing effect of taxi proportion on slight-injury crash rate is magnified at road segments with high intersection density. Second, the increasing effect of light-goods vehicle proportion on slight-injury crash rate is magnified at road segments with on-street parking. Third, the association between the medium- and heavy-goods vehicle proportion and killed/severe injury (KSI) crash rate, is moderated by the roadway width (number of traffic lanes). Finally, a higher proportion of medium- and heavy-goods vehicles generally contributes to increased KSI crash rate at road segments with high intersection density. Overall, the findings of this research are expected not only to help guide commercial vehicle enforcement strategy, licensing policy, and lane control measures, but also to review existing urban roadway designs to enhance safety.

Commuter PM exposure and estimated life-expectancy loss across multiple transportation modes in Xi'an, China

Commuters are reportedly exposed to severe traffic-related air pollution (TRAP) during their commuting trips. This study was designed and implemented to (1) compare particulate matter (PM) exposure across four common transportation modes; (2) examine and analyze various determining factors; and (3) estimate public health effects caused by commuting exposure to PM. All analyses and calculations were based on the experimental data collected from 13 volunteers, including heart-rate data on 336 commuting trips in four travel modes in Xi'an China. The results indicate highest PM exposure associated with cycling (average PM₁₀, PM_2.5 and PM_1.0 of 114.35, 72.37 and 56.51 μg/m³, respectively), followed by riding transit buses (116.29, 67.60 and 51.12 μg/m³ for the same pollutants, respectively), then taking a taxi (97.61, 58.87 and 45.11 μg/m³), and the lowest exposure onboard subways (55.86, 46.20 and 40.20 μg/m³). A multivariable linear regression model was used to examine major influences on PM concentration variations, with results corroborating significant PM variance across commuting modes, which is also affected by background pollution concentration and relative humidity. Further, years of life expectancy (YLE) loss were estimated using an inhalation dose model together with the life table method: cycling commuters experienced the greatest YLE loss (5.51-6.43 months per capita for the studied age group). During severe pollution periods, substituting other modes (like subway) for cycling could effectively avoid acute exposure. PM_2.5 levels in taxi cabins powered by CNG or methanol were comparatively lower, indicating that implementing alternative energy strategies could effectively lower traffic emissions and population exposure.

Associating ridesourcing with road safety outcomes: Insights from Austin, Texas

Improving road safety and setting targets for reducing traffic-related crashes and deaths are highlighted as part of the United Nations sustainable development goals and worldwide vision zero efforts. The advent of transportation network companies and ridesourcing expands mobility options in cities and may impact road safety outcomes. We analyze the effects of ridesourcing use on road crashes, injuries, fatalities, and driving while intoxicated (DWI) offenses in Travis County, Texas. Our approach leverages real-time ridesourcing volume to explain variation in road safety outcomes. Spatial panel data models with fixed-effects are deployed to examine whether the use of ridesourcing is significantly associated with road crashes and other safety metrics. Our results suggest that for a 10% increase in ridesourcing trips, we expect a 0.12% decrease in road crashes, a 0.25% decrease in road injuries, and a 0.36% decrease in DWI offenses in Travis County. On the other hand, ridesourcing use is not significantly associated with road fatalities. This study augments existing work because it moves beyond binary indicators of ridesourcing availability and analyzes crash and ridesourcing trips patterns within an urbanized area rather than their metropolitan-level variation. Contributions include developing a data-rich approach for assessing the impacts of ridesourcing use on the transportation system's safety, which may serve as a template for future analyses for other cities. Our findings provide feedback to policymakers by clarifying associations between ridesourcing use and traffic safety and uncover the potential to achieve safer mobility systems with transportation network companies.

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.

Effects of state-led suburbanization on traffic crash density in China: Evidence from the Chengdu City Proper

Road crashes have become a leading cause of death in China. Although enormous efforts have been exerted to determine the factors that affect individual crash incidents, neighborhood-level crash incidence in Chinese cities has not been sufficiently analyzed. This study fills this gap by quantifying the effects of built environment factors on neighborhood-level automobile-involved crash density (NACD) in urban China and identifying its mediators and mediating effects. In American suburbs, urban sprawl is widely recognized to render neighborhoods unsafe for residence, thus leading to a high crash incidence. This study compares the characteristics of built environments between inner-city neighborhoods and the new neighborhoods that have been developed through China's state-led suburbanization since 2008 to reveal how this suburbanization provides a safer neighborhood environment. A structural equation model is used to examine the relationships among suburbanization, built environment factors, and NACD in the city proper of Chengdu, the largest metropolis in southwest China. Thus, this study contributes new empirical evidence to the debates over urban designs that are safest for traffic. Moreover, this study enriches our understanding of different sociospatial consequences between American-style urban sprawl and China's state-led suburbanization.

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.

The Complex Interrelationship of Work-Related Factors Underlying Risky Driving Behavior of Food Delivery Riders in Athens, Greece

Background

In this study, the association of work and demographic characteristics with different traffic offenses committed by food delivery riders in Greece was examined. Previous research has identified various factors related to risky driving however, there is a need for exploring the complex interrelationship of work-related factors underlying risky driving behavior.

Materials and Methods

A 2-items uestionnaire was used exploring delivery riders demographic characteristics, terms of employment, issues of concern during work and type of traffic offenses committed. In total, uestionnaires were analyzed using logistic regression in order to identify characteristics independently associated with serious traffic offenses, namely, red-light running and helmet non-use.

Results

The analysis showed that: (i) typical health and safety measures had no effect on serious traffic offenses, (ii) young age was related to both offenses however (iii) different sets of work conditions were associated with reports of red-light running (i.e. low work experience, use of personal vehicle for work, and payment by hour) and helmet non-use respectively (i.e. intense work pace, high tip income per day and low concern about vehicle condition).

Conclusion

The above findings provide evidence that serious traffic offenses are manifestations of underlying conflict experienced by the riders between safety and various performance criteria. Each one of the two offenses is related to different rider profiles aiming to satisfy different goals, namely, those mainly trying to maximize profit non-helmet users and those, mostly inexperienced ones, trying to cope with work pressure red light runners. Potential regulatory measures to alleviate risky practices are discussed.

Resilient trade-offs between safety and profitability: perspectives of sharp-end drivers in the Beijing taxi service system

Background. Trade-offs are common behaviors of resilient systems, when the systems adapt to changing situations to meet multiple goals. Objective. In the context of the Beijing taxi service system (BTSS), this work investigates the sharp-end taxi drivers' trade-offs between work safety and business profitability, demonstrates their resilience in balancing these two goals and identifies factors that contribute to the trade-offs. Methods. An empirical framework incorporating questionnaire surveys, semi-structured interviews, field observation, data screening and categorization was adopted. Data were collected from a random sample of 70 taxi drivers. Results. In the drivers' decisions we found a slight bias in favor of profitability rather than safety (regardless of their finances), and a high level of resilience that the drivers had developed in making strategies for the trade-offs. Trip distance, possibility of traffic congestion, redundant consumption, weather conditions, road features and real-time broadcast information were identified as determinants of the drivers' decision-making. Conclusion. The findings inform BTSS organizational layers and regulators about the sharp-end drivers' needs for productive safety, and provide an evidence base for making more definitive recommendations about support provision and resource re-allocation in an effective and proactive manner.

Ranking contributors to traffic crashes on mountainous freeways from an incomplete dataset: A sequential approach of multivariate imputation by chained equations and random forest classifier

The estimation of the effect of contributors to crash injury severity and the prediction of crash injury severity outcomes suffer often from biases related to missing data in crash datasets that contain incomplete records. As both estimation and prediction would greatly improve if the missing values were recovered, this study proposes a sequential approach to handle incomplete crash datasets and rank contributors to the injury severity of crashes on mountainous freeways in China. The sequential approach consists of two parts: (i) multivariate imputation by chained equations imputes the missing values of independent variables; (ii) a random forest classifier analyses the correlation between the dependent and the independent variables. The first part considers different imputation methods in light of the independent variables being either binary, categorical or continuous, whereas the second part classifies the correlations according to the random forest classifier. The proposed method was applied to the case-study about mountainous freeways in China and compared to the analysis of the raw dataset to evaluate its effectiveness, and the results illustrate that the method improves significantly the classification accuracy when compared with existing methods. Moreover, the classifier ranked the contributors to the injury severity of traffic crashes on mountainous freeways: in order of importance vehicle type, crash type, road longitudinal gradient, crash cause, curve radius, and deflection angles. Interestingly, a lower importance was found for environmental factors.

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.

Condiciones laborales, salud y calidad de vida en conductores

Objetivo: Conocer las condiciones laborales, salud y calidad de vida de los trabajadores que se dedican al rubro de la conducción. Materiales y Métodos: Se realizó revisión integrativa en las bases de datos Medline/PUBMED, BVS, WEB OF SCIENCE, SCOPUS, referente a artículos publicados desde el año 2010 a 2019, en relación a la temática de salud, condiciones laborales y calidad de vida de trabajadores que se desempeñan en el área de la conducción, siguiendo los lineamientos planteados según Ganong. Resultados: 29 estudios cumplieron con los criterios seleccionados, los cuales fueron realizados en población trabajadora del ámbito de locomoción colectiva como buses, taxis, motocicletas y en el área de transportes, como camiones. Discusión: La realidad que experimentan estos trabajadores crea consciencia y abre un espacio para implementar políticas de salud que busquen su bienestar, y a la vez es una puerta de entrada a crear conocimiento sobre la necesidad de estudiar a este grupo poblacional. Conclusiones: La calidad de vida y las condiciones laborales de estos conductores, en su mayoría es deficiente y esto los afecta de manera negativa en distintos ámbitos. Por otra parte, la salud de este grupo de trabajadores se ve perjudicada por las condiciones en el trabajo que muchos de ellos puedan tener, así mismo la mayoría carece de buenas prácticas de salud, teniendo hábitos poco saludables, que se relacionan directamente con la labor que realizan. Como citar este artículo: Sepúlveda Guerra Elena Belén,  Valenzuela Suazo Sandra Verónica, Rodríguez Campo Varinia Alejandra. Condiciones laborales, salud y calidad de vida en conductores. Revista Cuidarte. 2020; 11(2): e1083. http://dx.doi.org/10.15649/cuidarte.1083

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.

Comparative risk factor analyses on bi-level injury severity of taxi and private car crashes in Seoul, South Korea

Objectives: Taxis, one of the main transportation modes that occupy the roadways in Seoul, are semipublic transportation modes for transporting passengers safely and promptly. Considering that one fifth of passenger vehicles on the roads in Seoul are taxis and the crash rate of taxis is double the exposure to traffic, it is important to identify risk factors of taxis from that of private cars. In this paper, crash causes and characteristics in both taxi crashes and private car crashes are investigated to identify the risk factors in accordance with the injury severity.Methods: An eight-year light-vehicle crash dataset was utilized, in which injury levels were defined as severe vs. non-severe. Three binary logit models that estimate the severity of crashes, the injury severity for at-fault drivers, and the injury severity for victims were modeled for taxi crashes and private car crashes. Independent variables were extracted and included in the models to evaluate the odds ratio of each predictor variable.Results: The results indicated that violation of traffic signals and signs was the highest contributor among all violation types for taxi crashes and parties involved (at-fault driver and victims), while driving on the wrong side of the road resulted in the highest increase in the odds ratio for private cars. Head-on collision and nighttime driving increased the likelihood of severe injury risk for all models, while age was the most prominent factor for the injury level of victims. Use of seatbelts had a major impact on the at-fault drivers, especially for taxis.Conclusions: This study identified the risk factors that affect the crash- and party-related severity level when casualties involved taxis and private cars. By employing both crash- and party-level models, the study not only identifies the risk factors among taxis and private car crashes but also provides a comprehensive picture of the injury profile of all vehicular occupants, which helps to devise safety measures that enhance the safety and reduce the injury severity for parties involved in crashes.

Phenomenology of being a safe taxi driver

BACKGROUND

In developing countries, a great number of people typically utilize public transportation such as Taxis. Therefore, taxi drivers have an important role in residents' well-being and safety. The aim of this study was to describe the subjective experiences and insights on safe driving among taxi drivers in Zanjan, Iran.

METHOD

In this qualitative study, 17 taxi drivers (23-54 years) were purposefully selected to participate in 11 semi-structured interviews, as well as one focus group (n = 6). The data was then analyzed based on a combination of Braun's and Colaizzi's methods.

RESULT

By analysis of the qualitative data, four main themes emerged from expressed experiences by taxi drivers, including 1) traffic chaos, 2) social prestige, 3) economic pressure, and 3) job satisfaction.

CONCLUSION

The emerging concepts of the present study imply a threefold structure of existence, which introduced by Heidegger, as taxi drivers' (the self) feelings and experiences of being under pressure of traffic chaos (the world), accompanied by low social prestige, economic pressure and job dissatisfaction (being in). Thus, strategic planning for traffic safety should consider taxi drivers' needs to feel a friendly world, as well as, to find themselves in a respectful and supportive environment.

Significance of attitudes, passion and cultural factors in driver's speeding behavior in Oman: application of theory of planned behavior

Traffic accidents and related fatalities have become a major public health problem in the world. This study aims to identify the significant factors that play an important role in the speeding behavior of drivers. A comprehensive questionnaire was designed and conducted with the students and employees of the University of Nizwa. The questionnaire items were designed considering the theoretical background of the theory of planned behavior (TPB) and local socio-cultural aspects of the driving environment. A total of 303 usable samples were obtained. The collected data were analyzed using factor analysis and structural equation modelling technique. The extracted factors of speeding passion and culture, speeding attitudes, subjective norms and perceived behavioral control are significant determinants of drivers speeding behavior in Oman. The driver's speeding attitudes, speeding culture, and passion have positive association with the drivers' speeding intentions and behavior, and driver's perceived behavioral control forms negative relationship with the speeding behavior. This study confirms the application of the TPB in predicting the drivers' speeding behavior in the context of Oman.

Self-reports of workloads and aberrant driving behaviors as predictors of crash rate among taxi drivers: A cross-sectional study in China

Objective: Taxis provide an important mode of public transport in China, but there has been very little in-depth research on the crash involvement propensity of taxi drivers. Thus, this study was conducted to develop a quantitative model for predicting the crash rate of taxi drivers. Methods: A total of 2,391 taxi drivers from 29 companies in 4 Chinese cities completed a structured and anonymous face-to-face questionnaire reporting their demographic information, workload conditions, aberrant driving behaviors, and crash history within the 2 years prior to the survey. Using the self-reported variables, a negative binomial model was implemented to predict taxi drivers' property damage only (PDO) and personal injury (PI) crash rates and identify the factors contributing to this risk. Results: Descriptive analysis of the survey data revealed that the workload of taxi drivers in China is relatively heavy. Seven significant predictors of PDO and PI crash rates were identified, including crossing red lights, dangerous overtaking, honking at a slow driver, failure to use an indicator lamp, driving while fatigued, stopping in forbidden areas to pick up or drop off a passenger, and driving with one hand. Taxi drivers' average off-duty days per week, aggressive driving behaviors, and preventing another driver from merging had significant effects only on PDO crash rate, and sleep problems were found to be significantly correlated with PI crash rate. Conclusions: To improve the safety of taxi drivers, considerable measures should be strictly implemented, ranging from periodic driver training and safety education to workload reduction, with the cooperation of government agencies and taxi companies. The findings of this study contribute to the design of potentially useful policy initiatives as well as targeted safety promotion programs.

Modeling and mitigating fatigue-related accident risk of taxi drivers

Taxi drivers worldwide often have very long driving hours and experience frequent fatigue. These conditions are associated with a high prevalence of fatigue and accidents. However, the key factors that distinguish high/low fatigue-related accident risk (FRAR) taxi drivers are uncertain. By examining a series of potential factors related with fatigue or accident risk as discussed in previous research, the objective was to find out the most important factors that relate to taxi driver's FRAR, and to investigate the association of these factors and taxi driver's FRAR. Modeling methods were applied to questionnaire data collected from Beijing taxi drivers. A 269-sample dataset was analyzed to identify key factors related to FRAR and to fit FRAR prediction models. The model's performance on high-risk driver prediction was then tested using another independently collected 100-sample dataset. High-risk taxi drivers had significantly longer driving hours per working day, lower rest ratios, less driving experience, and were more confident about their fatigue resistance. The FRAR model with only four major measurable predictors achieved a sensitivity of 91.9% and a specificity of 94.6% on predicting labeled data. Adjusting drive-rest habits and self-evaluation pertaining to these predictors is good for high-risk drivers to mitigate their accident risk. It was concluded that taxi drivers' drive-rest habits, experience, and intention for fatigue driving are crucial, and to a large degree determine their FRAR, and the prediction model can satisfactorily identify high-risk taxi drivers.

Aberrant Driving Behaviour, Risk Involvement, and Their Related Factors Among Taxi Drivers

The current study aims to investigate the aberrant driving behaviour and risk involvement of Iranian taxi drivers. The sample comprised 405 Iranian taxi drivers, who were recruited with a cross-sectional design, using a self-completion questionnaire survey during October and November 2016. We contribute to the literature by understanding how and to what extent the socioeconomic, demographic, driving, and aberrant driving behaviours influence risk involvement (accident involvement and traffic tickets). The validated 27-item Driver Behaviour Questionnaire (DBQ) was applied to measure aberrant driving behaviour. The results from valid observations (n = 381) explored a four-factor solution (including errors, ordinary violations, lapses, and aggressive violations) of the DBQ. The results also showed that being a single driver, having a high annual driving mileage, and a high number of daily taxi trips were positively associated with accident involvement. Furthermore, there was a positive correlation between the more ordinary violations and aggressive violations and accident involvement. Establishing better training and qualification mechanisms for taxi drivers could be considered by traffic safety experts in order to reduce ordinary and aggressive violations.