Individual Driver Characteristics Research Articles

베이지안 다수준 모형을 활용한 고령운전자 사고의 식별

This study examines various influential factors aimed at identifying crashes involving senior drivers. We consider variables reflecting the individual characteristics of drivers, crash types, roadway conditions, and environments, along with latent effects related to space and time, as potential identifiers of senior driver-related crashes in Daejeon. To this end, we employ Bayesian multilevel logistic regression models to unveil the random influences arising from spatial dependency and temporal autocorrelation after accounting for the fixed effects by explanatory variables. The result reveals that the overall annual trend, violations, and intersections appear to be positively associated with the log odds of the probability. Conversely, snow and icy roadway surface conditions, along with several crash types, are estimated to lower the log odds. The latent spatial effects dominate overall residual fluctuation, suggesting the benefits of our adopted model. We argue that our approach lays the foundations for designing safety policies for senior drivers.

Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data

Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety.

Factors driving changes in water conservation function from a geospatial perspective: case study of Jilin Province

Jilin Province in China borders other countries in Northeast Asia and is a complex ecosystem of transboundary rivers, varying terrains, and climates, which provides vital freshwater resources to the neighboring countries. Water conservation is one of the ecosystem service functions and is of great significance to maintaining sustainable development of water resources and ecological security. In this study, we analyzed the water conservation function and its variations in Jilin Province from 2000 to 2020 using the InVEST model. Furthermore, we examined the dominant factors governing spatial distribution of the changes in water conservation function over the past two decades using geographical detectors. In addition, a multi-scale geographic weighted regression model was adopted to investigate spatial differences and characteristics of individual drivers across different spatial scales. The study revealed a number of findings. First, between 2000 and 2020, average water conservation in Jilin Province increased by 46.12% overall from 87.14 mm/m2 to 127.28 mm/m2. Second, compared with land use change, climate change had a greater influence on water conservation. However, in some areas, the opposite was true, and the combined impact of both factors was more substantial on water conservation than when each acted independently. Third, except for temperature, the correlation between each driving factor and the change in water conservation function exhibited spatial variations, and the characteristics of each factor also varied across different spatial scales. In the study site, variations in water conservation were positively correlated with forest and grass coverage and negatively correlated with cultivated land and urban-rural and industrial-mining land. This study provides new insights for exploring the driving factors of changes in water conservation function and imparts a more appropriate basis for government agencies to make decisions about ecological and environmental protection and optimization of ecosystem management.

Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments

With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers’ responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers’ demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human–machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers’ responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers’ characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers’ awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.

Influence of occupational risk factors for road traffic crashes among professional drivers: systematic review

ABSTRACT This systematic review focuses on factors associated with occupational road crashes among professional drivers of commercial vehicles, e.g. trucks, buses and taxis. PubMed, MEDLINE and Google Scholar were searched, and quality assessment followed guidelines developed by the British Sociological Association medical sociological group. Sixty-six cross-sectional studies and one cohort study were included, of which 18, 45 and 3 studies were categorised as high-, moderate- and low-quality studies, respectively. Twenty-seven significant risk factors for road crashes were divided into six domains: (1) organisational, (2) individual driver characteristics, (3) qualifications, (4) driver conditions, (5) driving behaviour, and (6) external. The most frequently investigated factors were age (individual driver characteristics domain), sleepiness and substance use (driver conditions), driving duration and time of driving (organisational). More recently studied risk factors were management support (organisational), years of experience (qualifications), health parameters (driver conditions), vehicle speed and distracting activities, e.g. use of phone or navigation tools while driving (driving behaviour). The review identified overall risk factor domains and occupation-specific risk factors suitable for targeting and prioritising organisational preventive safety efforts. To obtain a more in-depth understanding of the antecedents of road crashes, future prospective studies are encouraged that evaluate preventive strategies for occupational road crashes.

Graph method for driving behavior optimization based on “SAF-ECO” description of behavior characteristics

Considering the fact that driving behavior data possesses characteristics of strong real-time, poor stability, and continuous change, this study proposes the Individual Driving Behavior Graph Construction Method (DBGCM), which visually presents the time trajectory of driving behavior to explore safety-ecological (SAF-ECO) characteristics of individual drivers. The results can be applied in the analysis of driving safety ecology and as a reference for driving behavior optimization. This study is based on the micro-driving behavior data collected by the on-board diagnostic devices (OBD), which can create a graph on individual driver behavior characteristics via nodes and time axis as its elements. Additionally, the method of Longest Common Subsequence (LCSS) is proposed to identify the similarity among different driving behavior graphs. The data results of taxi drivers under different SAF-ECO levels lead to the conclusion that the driving behavior characteristics graph analysis is consistent with the SAF-ECO classification. The similarity of graphs among “safe and non-eco” drivers is higher than that within other categories. Finally, the research discusses in detail the data requirements, method verification, and future applications. The reasonable coupling characteristic description of “SAF-ECO” driving behavior is conducive to the enhancement of drivers’ self-management ability, driving education, and customization for drivers.

Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis

Driving behavior is one of the most critical factors in traffic accidents. Accurate vehicle acceleration prediction approaches can promote the development of Advanced Driving Assistance Systems (ADAS) and improve traffic safety. However, few prediction models consider the characteristics of individual drivers, which may overlook the potential heterogeneity of driving behavior. In this study, a vehicle acceleration prediction model based on machine learning methods and driving behavior analysis is proposed. First, the driving behavior data are preprocessed, and the relative distance, relative speed, and acceleration of the subject vehicle are selected as feature variables to describe the driving behavior. Then, a finite Mixture of Hidden Markov Model (MHMM) is used to divide the driving behavior semantics. The model can divide heterogeneous data into different behavioral semantic fragments within different time lengths. Next, the similarity of different behavioral semantic fragments is evaluated using the Kolmogorov–Smirnov test. In total, 10 homogenous drivers are classified as the first group, and the remaining 20 drivers are classified as the second group. Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) are used to predict the vehicle acceleration for both groups. The prediction results show that the proposed method in this study can significantly improve the prediction accuracy of vehicle acceleration.

Research on risky driving behavior evaluation model based on CIDAS real data

This paper constructs an evaluation system that reflects dangerous driving behavior. The evaluation system has a three-layer structure model of “Evaluation Index-Performance Mode-Driving behavior score.” Verification of the feasibility of the model based on the relationship between the driver and the cause of the accident based on behavioral characteristics. First, the driving return survey data and accident form information of the real traffic accident cases of China In-Depth Accident Study (CIDAS) database are counted, and the character variables are converted into digital variables. Then, a three-tier structure of the dangerous driving behavior evaluation system is built, and the correlation between the driver and the cause of the accident is conducted to verify the feasibility of the model. The research shows that the individual characteristics of drivers with dangerous driving behavior are closely related to the cause of accidents, and the evaluation system constructed in this paper can quantify and describe this relationship effectively.

INFORMATION APPROACH TO DETERMINING THE TRAFFIC ROUTE BY VEHICLES DRIVERS IN CITIES

Purpose. Modeling the transport network, streamlining the development and planning of traffic flows, which is possible through the introduction of information technology, leads to better control of the transport system and transport operations. The study is aimed at gaining new knowledge on determining traffic routes for vehicle drivers in cities. Currently discovered patterns of drivers choosing traffic routes are offered for all drivers of vehicles, regardless of their individual characteristics. Methodology. When determining the patterns of drivers choosing the routes of vehicles in cities, it is proposed to conduct differentially depending on the individual characteristics of drivers, which are determined by the type of nervous system. Based on the analysis, factors were identified that affect the choice of traffic route for drivers. In order to fix the identified factors, a survey was conducted using a specially designed questionnaire. To take into account the individual characteristics of drivers using a typological questionnaire, the type of nervous system was determined. To determine the patterns of choice of traffic routes for drivers of vehicles, methods of regression analysis were used. Findings. Based on the data obtained from a survey of vehicle drivers, a model was developed for changing the correspondence share implemented by the drivers with the sanguine type of nervous system on the alternative traffic routes. A statistical evaluation of the resulting model indicates the admissibility of its use for predicting the parameters of traffic flows in urban sustainable development projects. Originality. For the first time, the authors conducted a study of the patterns of choice of traffic routes for drivers of vehicles, taking into account the individual characteristics of drivers, which are determined by the type of nervous system. Practical value. Based on the results obtained, it is possible to model the transport network, rationalize the development and plan traffic flows, which is possible through the introduction of information technologies. Modeling the traffic routes of vehicles makes it possible to analyze the throughput of highways and intersections. The introduction of information technology leads to better control of the transport system and transport operations.

Understanding of traffic signs by drivers in the city of Manila, Philippines

The use of traffic signs is one of the most reliable control devices used to guide the safe and orderly movement of traffic and pedestrians. Traffic signs are necessary to give information for routes, directions, and warnings for drivers. These should be clear to convey the intended message for road users to understand. Drivers tend to ignore the road signs while authorities opt not to enforce them. As a result, majority of the Filipino drivers are not disciplined and traffic signs become of less importance. Moreover, a good number of Filipino motorists have not gone proper training resulting to the lack of knowledge regarding the various road signs. Therefore, driver understanding of some selected traffic signs was assessed through a survey. The study aims to determine the individual characteristics of drivers in understanding traffic signs. The study of the role of drivers’ characteristics in understanding traffic signs in Manila is of great importance to prevent rising accidents in the area. The survey was conducted among 535 Manila city drivers. The results indicated that the drivers had a poor level of understanding of the meaning of traffic signs. The overall understanding of level, measured in terms of percentage correct responses, was 76.25%. The drivers’ familiarity to traffic signs mainly depends on its abundance in the location where the respondent usually passes through and the simplicity of its design wherein the road user can easily determine its meaning. The study further establishes that socio-economic background and driving characteristics have a significant influence on driver’s understanding of traffic signs such as the educational attainment and mode of driving training.

Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction

In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the near future, the technique to forecast dangerous situations and automatically adjust the speed to prevent a collision can be implemented to a real vehicle. We have attempted to realize the technique to predict the future positions of adjacent vehicles. Several previous studies have investigated similar approaches; however, these studies ignored the individual characteristics of drivers and changes in driving conditions, even though the prediction performance largely depends on these characteristics. The proposed method allows estimating the operation characteristics of each driver and applying the estimated results to obtain the trajectory prediction. Then, the collision risk is evaluated based on such prediction. A novel advanced adaptive cruise control, proposed in this paper, adjusts its speed and distance from adjacent vehicles accordingly to minimize the collision risk in advance. In evaluation using real traffic data, the proposed method detected lane changes with 99.2% and achieved trajectory prediction error of 0.065 m, on average. In addition, it was demonstrated that almost 35% of the collision risk can be decreased by applying the proposed method compared to that of human drivers.

Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles.

The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.

Driver profiling – Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation

This paper focusses on the role of driver individuality in the field of cognitive driver behavior modeling for the prospective safety impact assessment of advanced driver assistance systems (ADAS) and automated driving functions. Virtual traffic simulation requires valid models for the environment, the vehicle and the driver. Especially modeling human driver behavior is a major challenge, which in recent years has already led to the development of various driver models for the purpose of virtual simulation. Modeling human behavior in traffic with a precise representation of human cognition, capability and individuality, are crucial demands, which require thorough investigation and understanding of the human driver. Current driver behavior models often leave aside the aspect of driver individuality and lack the consideration of differences in driving behavior between different drivers. To take into account all the aspects from complex human cognitive processes to individual differences in action implementation, the Stochastic Cognitive Model (SCM) was developed. The SCM is based on five subcomponents: gaze control, information acquisition, mental model, action manager and situation manager (=decision making process) and action implementation. The aim of the present study is to provide a basis for establishing a solid logic for the integration of driver individuality into the current structure of the SCM by creating a new submodule that takes into account several behavior affecting driver characteristics. This subcomponent controls the stochastic variance in several driver behavior parameters, such as velocity or comfort longitudinal acceleration. In a representative driving simulator study with 43 participants, driver behavior on the highway was investigated and thoroughly analyzed. Information about several relevant driver characteristics and personality traits of the participants was collected and a logical hierarchical model was set up to cluster several dependent and independent variables into four layers: independent manifest driver variables, such as age or gender (Level 1), latent driver personality factors, such as thrill seeking or anxiety (Level 2), driver behavior dimensions, such as dynamics and law conformity (Level 3), and various dependent driver behavior parameters, such as velocity, acceleration or speed limit violation (Level 4). Multiple linear regression analyses were run to find the individual driver characteristics and personality traits, by which most of the stochastic variance in the measured driver behavior parameters can be explained. Subsequently, a principal component analysis (PCA) was run to test, if the previously clustered driver behavior parameters were loading on the presumed behavioral dimensions on the third level of the model to identify significant components of driver behavior, such as dynamics or law conformity. Results of the present study show significant correlations between driver characteristics and driver behavior parameters. According to the results of the PCA, variability in driver behavior can be explained to a great extent by three largely independent components: (1) Speed and cruise control, (2) Dynamics and (3) Driver performance. With the consideration of driver individuality in driver behavior models for the agent-based traffic simulation, validity of the results from prospective safety impact assessment analyses of automated driving functions can be enhanced. Beyond that, the findings of the current study can be used as a solid basis for the development of adaptive functions in the field of vehicle automation, considering the different driving skills and preferences of drivers with different individual profiles.

A hybrid approach for identifying factors affecting driver reaction time using naturalistic driving data

The National Transportation Safety Board (NTSB) estimates that 80% of the deaths and injuries resulting from rear-end collisions could be prevented by the use of advanced collision avoidance systems. While autonomous or higher-level vehicles will be equipped with this technology by default, most of the vehicles on our roadways will lack these advances, so rear-end crashes will dominate accident statistics for many years to come. However, a simple and cost-effective in-vehicle device that uses predictive tools and real-time driver-behavior and roadway data can significantly reduce the likelihood of these crashes. In this paper, we propose a hybrid physics/data-driven approach that can be used in a kinematic-based forward-collision warning system. In particular, we use a hierarchical regularized regression model to estimate driver reaction time based on individual driver characteristics, driving behavior, and surrounding driving conditions. This personalized reaction time is input into the Brill’s one-dimensional car-following model to calculate the critical distance for collision warning. We use the Second Strategic Highway Research Program (SHRP-2)’s Naturalistic Driving Study (NDS) data, the largest and most comprehensive study of its kind, to model driver brake-to-stop response time. The results show that the inclusion of driver characteristics increases model precision in predicting driver reaction times.

ADAS Acceptability Improvement Based on Self-Learning of Individual Driving Characteristics: A Case Study of Lane Change Warning System

Low user acceptance is one of the fundamental problems for popularizing advanced driver assistance systems (ADAS). Systems that are developed for the majority of drivers have to possess stationary characteristics and be conservative for safety reasons. However, the drivers with disparate driving styles possess different risk cognition of lane change behavior; therefore, such systems with stationary characteristics may cause frequent interference to aggressive drivers or may be perceived as a radical system by conservative drivers. An ADAS that adapts to the characteristics of individual drivers during lane change maneuvers will be more effective and more acceptable to drivers. In this study, we developed an adaptive algorithm that learns the characteristics of individual drivers during lane changes and determines the optimal threshold online to adapt to different drivers. Signal detection theory (SDT) was employed and the results of the accuracy, false negative rate, and false positive rate were used to capture the drivers' lane change behavior characteristics. A learning stage and a threshold fluctuation stage were designed in the adaptive algorithm to determine the optimal warning threshold and amended the optimal warning threshold based on changes in the drivers' behaviors. We evaluated the proposed algorithm by conducting the actual vehicle tests with a total of three participants. The offline statistical analysis results of the participants' lane change characteristics were compared with the online results of the warning threshold adjustments from the adaptive algorithm; the comparison results indicated that the adaptive algorithm could effectively capture the drivers' lane change characteristics and determine an appropriate warning threshold. The findings provide an improvement in the performance of the lane change warning (LCW) system and enhance people's acceptance of intelligent systems.

A Study of Feature Extraction for Individual Characteristics of Drivers’ Body Motion

A Study of Feature Extraction for Individual Characteristics of Drivers’ Body Motion

УДОСКОНАЛЕННЯ МЕТОДУ ОБЛІКУ ПОВЕДІНКИ ВОДІЇВ У МОДЕЛЯХ ТРАНСПОРТНОГО ПОТОКУ

The goal of the robots is to obtain the values of coefficient of decision (Kp) and plot a histogram of the probability distribution of these values for the selected type of maneuver. In the article the method of modeling the driver's behavior at non-adjustable intersections in intersecting flows is investigated. Analysis and processing of video recording of the motion of intersecting streams at the quadrilateral crossroads of the city of Kharkov, chosen by street road network (SRN), is performed. An improved method of "boundary intervals" is proposed as a "sliding boundary interval" for modeling the driver's behavior. As a result of processing the video recording of the flow of streams at unregulated intersections, a histogram of the probability distribution of the values of the decisiveness coefficient for crossing flows was constructed [11, 12]. With the help of such histograms, the Kp values for each car in the stream can be specified using any known random number generator. This method will take into account both the individual characteristics of drivers and the impact on their behavior of objective factors, the proposed method of recording drivers in modeling the movement of traffic flows will increase the accuracy of modeling, which in turn will allow us to refine a number of results that we obtain by modeling, for example, the calculation of the throughput of lanes. The proposed method of simulating the driver's behavior in the models of traffic flows makes it possible to approximate the accuracy of simulation to the real variety of behavior of drivers, and, consequently, to increase the adequacy of models.

Analysis of Rear-End Conflicts in Urban Networks using Bayesian Networks

Crash analysis and modeling studies have provided insightful information on crash-contributing factors and the methodologies utilized provide evidence that they could also be beneficial for conflict analysis, as traffic conflict data share similar traits with crash data. In this study, a Bayesian network (BN) is estimated to comprehensively analyze rear-end conflict likelihood in an urban network, using disaggregate vehicle-by-vehicle data and the time-to-collision indicator to identify conflicts. The variables imported in the BN include (i) individual driver characteristics (e.g., speed); (ii) traffic operational characteristics (e.g., volume); and (iii) general characteristics (e.g., weather conditions). The inference analyses of the BN conducted to quantify the contributions of the variables affecting rear-end conflict likelihood in the urban network showed that rear-end conflict likelihood could be increased when the involved vehicles are of a different type, when the speed of the following vehicle is higher than the speed of the leading vehicle, when the individual speed is high when the individual headway is small, with a higher coefficient of variation of speed values, when the type of intersection nearest to the measuring point was a priority intersection, when the carriageway was of dual design, and when it was rainy. It was also shown that rear-end conflict likelihood increases during congestion and free-flow traffic. The findings of this study could be further developed to provide a good understanding of contributing factors to possible crashes in the urban network.

EXPERIMENTAL INVESTIGATION OF THE DRIVER'S BEHAVIOR AT UNCONTROLLED INTERSECTIONS IN CROSS TRAFFIC FLOWS

Problem. When modelling traffic flows through intersections of the network of streets, the most difficult task is to model driver's behavior, since it defies precise mathematical description, it depends on almost unlimited number of factors and can significantly change even for the same driver over a fairly short period of time, for example, one trip. Goal. The goal is to obtain the values of Cd and to build the histogram of distribution of these values probabilities for the chosen type of maneuver. Methodology. The method of modeling driver's behavior at uncontrolled intersections in cross traffic flows has been investigated in the paper. The analysis and processing of video recording of cross traffic flows at the four-way intersection chosen on the network of streets of the city of Kharkiv has been performed. An improved method of «boundary intervals» as a «sliding boundary interval» has been proposed for modeling the driver's behavior. Results. As a result of processing the video recording of the flow motion at uncontrolled intersections, the histogram of the distribution of probabilities of the determination coefficient values at cross traffic flows has been built [11, 12]. With the help of such histograms, the Cd values for each car in the flow can be specified using any known random number generator. Originality. The method stated will take into account both the individual characteristics of drivers and the impact of objective factors on their behavior; the proposed method of recording drivers when modeling the movement of traffic flows will increase the accuracy of modeling, which in turn will allow us to specify a number of results that we obtain by modeling, for example, the calculation of the traffic lane capacity. Practical value. The proposed method of simulating the driver's behavior in the models of traffic flows makes it possible to approximate the accuracy of simulation to the real variety of behavior of drivers, and, consequently, to increase the adequacy of models.

Work Environment, Stress, and Driving Anger: A Structural Equation Model for Predicting Traffic Sanctions of Public Transport Drivers.

Public transport is an effective and sustainable alternative to private vehicle usage, also helping to reduce the environmental impact of driving. However, the work environment of public transport operators is full of adverse conditions, which, together with their high mileage, may increase the occurrence of negative safety outcomes such as traffic accidents, often preceded by risky road behaviors enhanced by stress, anger, and difficult operating conditions. The aims of this study were, first, to determine the association between work-related psychosocial factors and individual characteristics of public transport drivers and the rate of traffic sanctions they are subject to; and second, to assess the mediation of driving anger in this relationship. A sample of professional drivers (57.4% city bus, 17.6% taxi, and 25% inter-urban bus male operators) was used for this cross-sectional study, responding to a five-section survey including demographic data and driving-related factors, psychosocial work factors including job stress, driving stress, risk predisposition, and driving anger. The results of this study showed significant associations between work-related factors: measures of stress and self-reported rates of traffic fines. Second, it was found that driving anger mediates the associations between driving stress, risk predisposition, and traffic sanctions; and partially mediates the association between driving experience, hourly intensity, and job stress. This study supports the idea that traffic penalties reported by public transport rates are preceded by work-related, personality, and other individual factors that, when combined with driving anger, enhance the occurrence of road misbehavior that may affect overall road safety.