Driver Stress Research Articles

The position and role on patent citation network of the parking lot guidance system

ABSTRACT During the peak period of urban traffic, about one-third of vehicles are looking for parking spaces, resulting in air and noise pollution, and even traffic accidents due to emotional stress of drivers; one of the reasons is the lack of guidance information for vacant parking spaces, which leads to inefficient parking behavior. The “Guide System” of smart parking can guide drivers to quickly enter the parking space to improve parking efficiency, which can greatly reduce urban air and noise pollution, and even reduce traffic accidents and casualties. This study discusses the technological development of smart parking “navigation system”, through the social network analysis formed by patent citation, and then analyzes and discusses the technology status and role of individual companies in the industry. This research uses two analysis indicators of technology overlap and network centrality and uses patent citation networks to analyze the technology status of individual companies. Technology overlap is composed of technology knowledge status (TKS) and technology knowledge reliability (TKR), and social network centrality. This research uses the navigation system of smart parking to explore whether this model can locate the technology role and location of individual companies. Companies with special industries/equipment/unique technologies are scattered in special fields.

Predicting Driver Stress Levels with a Sensor-Equipped Steering Wheel and a Quality-Aware Heart Rate Measurement Algorithm.

Unobtrusive monitoring of driver mental states has been regarded as an important element in improving the safety of existing transportation systems. While many solutions exist relying on camera-based systems for e.g., drowsiness detection, these can be sensitive to varying lighting conditions and to driver facial accessories, such as eye/sunglasses. In this work, we evaluate the use of physiological signals derived from sensors embedded directly into the steering wheel. In particular, we are interested in monitoring driver stress levels. To achieve this goal, we first propose a modulation spectral signal representation to reliably extract electrocardiogram (ECG) signals from the steering wheel sensors, thus allowing for heart rate and heart rate variability features to be computed. When input to a simple logistic regression classifier, we show that up to 72% accuracy can be achieved when discriminating between stressful and non-stressful driving conditions. In particular, the proposed modulation spectral signal representation allows for direct quality assessment of the obtained heart rate information, thus can provide additional intelligence to autonomous driver monitoring systems.

Driving Behaviour in Depressed Patients vs Healthy Controls

Abstract Introduction Depression is characterized by mental, emotional and executive dysfunction, and it may have an impact on driving behaviour. The aim of the study was to evaluate driving performance in depression. Methods Participants (39 depressed patients vs 30 controls) completed questionnaires and scales on demographics, driving experience and habits, mental and physical health. They also completed two specific questionnaires on driving behaviour and attitude: the Driver Behaviour Questionnaire (DBQ) with three sub-scales (driving errors, traffic violations and attention lapses) and the Driver Stress Inventory (DSI) which assesses drivers' vulnerability to stress reactions while driving and consists of five sub-scales (aggression, dislike of driving, hazard monitoring, thrill seeking and proneness to fatigue). Results All driving sub-scales did not differ between cases and controls. For both the total population of participants and for depressed patients separately aggression and hazard monitoring (HM) were found to be positively correlated to fatigue severity scale total score (FSS), HM was correlated with male sex, and proneness to fatigue (PF) was negatively correlated with the total score of STOP-BANG, a questionnaire for probable sleep apnea. Only among depressed patients there was a positive correlation of PF with the total score of Athens Insomnia Scale (AIS), of traffic violations with AIS and body mass index, and of both dislike of driving and HM with the anxiety sub-scale of the Hospital Anxiety and Depression Scale. Only when all subjects were considered together, age was negatively correlated to thrill seeking and traffic violations. Conclusions While there are no significant differences between depressed patients and controls, there are parameters which influence driving behaviour and attitude similarly for both groups (especially fatigue and sex) and specific parameters only for depressed patients (anxiety, insomnia symptoms and BMI). Key messages Driving behaviour and attitude of depressed patients and healthy controls are influenced by specific parameters. Depression per se and its consequences, such as sleep disorders and anxiety, affect driving performance and attitude.

Effects of safety pattern, cabin ergonomics, and sleep on work-related stress and burnout of city and transit bus drivers in Lahore, Pakistan

The health and working environment of bus drivers is compromised in low-middle-income countries like Pakistan which leads to burnout and excessive Road Traffic Crashes. Hence, this study delves into factors affecting their safe operations from health and work environment perspectives and measures their associated stress and Burnout level. In a study of four hundred and ninety-nine (499), 86% city and 14% transit bus drivers are surveyed through a questionnaire. Stress is estimated for city and transit bus drivers, using the Effort/Reward Imbalance Model (ERI) of Siegrist, and burnout is calculated using the Copenhagen Burnout Inventory (CBI). For the determination of important determinants, descriptive and regression analyses are conducted. Findings show that stress has emerged as a negative factor for the physical and psychological health of city and transit bus drivers. Results based on bus drivers’ responses suggest that organisational awareness and emphasis on health and safety levels can significantly reduce driver stress and burnout. Practitioner Summary: This study explores burnout and work-related stress of bus drivers in Lahore (Pakistan). City and transit bus drivers were interviewed through a questionnaire, containing three sections, using different subjective ratings based upon their past reliability. Results indicate that stress in bus drivers emerged as a physical and psychological health-damaging factor.

MODELING THE DRIVER'S REACTION TIME TAKING INTO ACCOUNT THE INFLUENCE OF CONFLICT SITUATIONS ON CITY STREETS

The driver's reaction time is the most important characteristic when driving. The driver's reaction time is the period of time from the moment the stimulus appears in the driver's field of vision until the start of the action (application of the brake pedal, accelerator, steering wheel). The response time to stimuli depends on: the driver's age; the state of his health; mental state; alcohol or drug consumption; if at the moment he is in the vehicle alone or with a passenger, etc. The functional state of the driver is a complex of characteristics of the functions and qualities of the operator, which directly or indirectly characterizes the performance of labor activity. This definition draws a line between the state of a person and the state of his individual physiological and psychophysiological functions. One of these indicators is the stress index. Heart rate is a stable and accurate indicator of functional status. The experiment involved drivers with 10-16 years of experience. The driver's cardiogram was recorded using a portable Holter and analyzed in the CardioSens CS system. According to the degree of danger, conflict situations are divided into three types: light, medium and critical. The degree of danger of a conflict situation is determined by the appearance of critical accelerations. A racelogic device was used to register the acceleration parameters. The obtained dependences of the change in the functional state of the driver and the reaction time in accordance with the number of conflict situations while driving indicate significant fluctuations in these indicators. Change in reaction time from 0.7 sec. up to 1.5 sec. associated with significant psychophysio-logical stress on the driver, the number of conflict situations of various types: the appearance of a pedestrian, dangerous maneuvering of other vehicles, etc. At the same time, the driver's stress may increase by more than 2.5 times. The article discusses an approach to modeling the driver's reaction time, taking into account the influence of conflict situations on city streets. Experimental studies of the regularities of changes in the reaction time, the stress index of drivers in traffic on city streets have been carried out. Two factor models have been built, taking into account the mutual influence of factors of movement time in urban conditions and the number of conflict situations.

Taxation within the Transport Sector: A Ride-hailer and Sharing Economy Perspective

This study explored the implications of taxes on ride-hailing services in a developing economy setting and its effect on the activities of drivers who patronize such services. Using thematic analysis, the study analyzed interview responses from key participants in the ride-hailing services such as the drivers, service operators, and vehicle owners based on the sharing economy perspective. The results indicate that (1) in a broader sense the contribution of the ride-hailing service is characterized with some features such as savings in money and time, ease of stress for both drivers and passengers, and also ease of movement; (2) taxes are needful to ensure sustainable governance but these taxes ought to be equitably charged to all actors within the economy and (3) the Vehicle Income Tax is a cause of financial distress to Uber drivers due to its increasing nature. More importantly, the tax has the potential of eroding profits which makes drivers worse off. This new but genre system of transportation is gaining roots and far advancing within the Ghanaian landscape. Thus, policymakers and industry players must focus on its role within the economy and the benefits therefrom. Equally, there must be more policy initiatives to prevent overburdening the final consumer of ride-hailing services with high taxes and prices. It also suggested that subsequent studies focus on the use of a different methodology within a different or the same setting.

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness.

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

The impact of courteous and discourteous drivers on physiological stress

Prior studies into road safety have concentrated largely on studying unsafe forms of driving behaviour such as aggressive, stressed, and risky driving. Little attention has been given to ‘positive’ driving behaviour, such as how pro-social driving may help to promote cooperation with other road users and decrease incidences of aggressive and stressful driving. This study aimed to compare the impact of courteous and discourteous driving on the immediate physical health of other drivers (blood pressure, heart rate, and markers of stress) whilst controlling for other recognized factors responsible for driver stress such as road infrastructure (e.g. roadworks, traffic lights, freeways) and driving maneuvers (e.g. merging, tail-gaiting, navigating roundabouts). Using actors in a deception study, a naturalistic driving scenario was created in a lab-based simulation. All participants (n = 10; 39 ± 14.5 years) drove the same route in a simulator and engaged with the same driving behaviours and other virtual road users on two separate occasions separated by 7 days. The difference between conditions was whether the participant interacted with other drivers who displayed: a) courteous or b) discourteous behaviours. Blood pressure, heart rate variability and salivary hormone concentrations (cortisol and alpha amylase) were measured before and immediately after each simulated drive. After interacting with the discourteous drivers, participants experienced significantly higher mean arterial blood pressure, systolic and diastolic pressure, central systolic and diastolic pressure, and heart rate, and lower heart rate variability (indicative of acute stress) compared to interacting with the courteous drivers. Conversely, these markers of stress were reduced after interacting with the courteous drivers. The results support courtesy on the road to provide short-term benefits for the recipient of the action, while also increasing road safety more generally.

Design and Implementation of Smart Car Parking System using LabView

Abstract: Due to rapid proliferation in the number of vehicles on the road, finding a vacant car parking space is becoming challenging and ubiquitous in every major city, resulting traffic problems are bound to exist. Increase in traffic causes the number of accidents thatcause serious bodily harm to the road users, the pollution caused by the large amount of CO2 released by the vehicles, and the continuous stress of drivers who must drive in often narrow and very busy roads and who must look for a long time to find a space to park. Thus, tosolve the parking problem, lot of research is being done on smart parking management system mechanisms. The existing system gives us the information about the empty slots availability but does not give information about the exact location of parking slot available in sucha big area. This paper proposes an efficient, cost effective smart car Parking System on wireless Sensor Networks (WSN) technology using LabView that can easily locate and secure a vacant parking space at any car park deemed convenient to them. The parking slots are continuously monitored, and data is continuously updated in the display board. Index Terms: parking system, smart parking, IR sensor

Dynamic Vehicle Routing with Parking Probability under Connected Environment

In downtown areas of large cities, it is very challenging for drivers to find available parking spots, even when they are provided with information on parking availability and location information. To overcome this challenge, this paper develops a dynamic vehicle routing system to search for the optimal routes for connected vehicles to find parking spots successfully and to minimize total trip time, including driving time and walking time. The system predicts the probability of each parking lot having available parking spots based on the existing available number of spots and the vehicle arrival and departure rates collected by connected vehicles. This probability is integrated in the search for vehicle routes to minimize total travel and walking times. Numerical experiments indicate that the proposed system can reduce the cruising time spent searching for available parking spots, and the total trip time can be reduced by up to 24%. In addition, the system can decrease the number of re-routing decisions, which reduces the stress of drivers on the road. A sensitivity analysis of the parking probability is also conducted. Some future work based on the proposed system is proposed in the conclusion to this paper.

Work-related stress in cycle rickshaw taxi drivers from Mexico City's Historic Downtown.

In Mexico, neither the pathological profile nor the health/illness conditions of the cycle rickshaw taxi drivers have been studied. Therefore, the factors that influence the physical and mental health of this labor group are unknown. To describe the characteristics of working and health/illness conditions that influence the stress level of the cycle rickshaw taxi drivers from Mexico City’s Historic Downtown. 90% (n = 110) of the workers participated in a cross-sectional study of a correlational type, in which a survey on working conditions and health was applied. Stress was analyzed using the Aro Symptomatic Scale, and vital signs, somatometry and a blood test were measured to analyze lipids and glucose. 16% of drivers were assessed with a high level of stress. No association was found with conditions related to physical and behavioral health, such as hypertension, dyslipidemia, diabetes, overweight, smoking and alcohol consumption. Therefore, the perception of violence risk (odds ratio [OR]: 1.19), the risks associated with the vehicle (OR: 1.18), having had a work accident (OR:38.2) and musculoskeletal complaints —mainly, in the elbow (OR: 1.20) and wrists (OR: 1.11)— are the factors that most influence the level of stress. The main cause of stress in drivers is related to the risks of their activity and musculoskeletal conditions, so interventions and action protocols are suggested to help preventing and face the risks of workers.

More Than Just "Stressful"? Testing the Mediating Role of Fatigue on the Relationship Between Job Stress and Occupational Crashes of Long-Haul Truck Drivers.

IntroductionRecent evidence consistently highlights the adverse work environment of long-haul professional drivers, whose task structure typically involves the performance of extensive shifts, driving under stressful working conditions. In this regard, job stress and fatigue – that are highly prevalent in this workforce – seem to play a crucial role in explaining this group’s negative traffic safety outcomes. The aim of this study was to assess whether work-related fatigue is a mechanism that mediates the relationship between job stress, health indicators and occupational traffic crashes of long-haul truck drivers (LHTD).MethodsThe data used in this study were collected from 521 Spanish long-haul truck drivers (97% males) from all 17 regions of Spain, with a mean age of 47 years.ResultsUtilizing structural equation models (SEM), it was found that work-traffic crashes of long-haul truck drivers could be explained through work-related fatigue that exerts a full mediation between job stress (job strain), health-related factors and traffic crashes suffered during the previous two years.DiscussionOverall, the findings of this research support that a) stressful working conditions and health issues of drivers have significant effects on traffic crashes, and b) fatigue is a mechanism relating stress-related factors and work-traffic crashes of long-haul drivers. This study highlights the need of stress- and fatigue-management policies and interventions, in order to reduce the crash risk of long-haul truck drivers.

ESTABLISHMENT OF A UNIFORM ROUTE GUIDANCE SYSTEM IN UKRAINE TAKING INTO ACCOUNT THE WORLD’S BEST PRACTICES TO IMPROVE ROAD SAFETY

Route guidance tools have a significant impact on road safety on public roads, as they carry important information on route guidance: provide drivers with information on the location on a specified road, regularly confirm the planned direction of movement, timely inform road users about the change of specified route direction and indicate the distance to the final object. This is very important information for the driver and it is difficult to overestimate it in road safety. Timely informing of drivers on the route direction will allow them carrying out adjustments in advance and feel confident on the road. Ensuring the completeness of route guidance information prevents emergency braking of vehicles in the area of traffic junctions and violation of the maneuvering rules to make a turn / turnaround in the right direction, and such maneuvers lead to road accidents. The establishment of a uniform route guidance system will reduce the road accident level and the cost of transportation due to excessive mileage of vehicles in the case of wrong route and help the drivers freely and easily get to their destination. The main need for information is felt by the drivers of vehicles whose actions directly impact the road safety. This, in turn, determines the basic principles and requirements for the composition, placement and choice of information tools. During the trip, the drivers need the information that would allow them navigating freely on the roads when passing the selected route that will relieve the stress of drivers and reduce the probability of road accidents, as well as increase the capacity of roads. It helps the drivers to adjust the previously selected route taking into account the real traffic conditions during the trip thereby minimizing the time consumption. To navigate the roads during movement, the drivers need the information about streets, objects and directions. Such information is provided by information signs, with which the road network must be equipped in sufficient quantity. The information provision should differ depending on the settlements, public roads and traffic conditions. A comprehensive solution of the issue of quality information provision to the road users can be achieved only by establishing a uniform route guidance system for road users which will take into account the needs for information of different categories of road users.

Scanning the Issue

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.

WITHDRAWN: Assessment of stress and safety practice level of heavy commercial passenger vehicle drivers using multi-grade fuzzy

Abstract Drivers’ Stress and Safety Practices (SSP) are very important for transportation organizations to save people’s lives, reduce fines and keep insurance premiums low. The purpose of this article is to evaluate the SSP level of heavy commercial passenger vehicle drivers in a transport organization using Multi-Grade Fuzzy (MGF) approach. The overall SSP index was calculated as 5.51 which indicate that the case transport organisation’s practice level is ‘moderately good practices’ and has scope for improvement. The stress and safety practice attributes are classified based on Importance-Performance Analysis (IPA). The results found that the weaker attributes of the case transport organisation were work timing, pragmatic trip scheduling, work-life balance and degree of freedom at work, which the organization has to improve. Using this framework, the any transport organisation can better evaluate their drivers’ SSP level.

A Dynamic Hybrid Choice Model to Quantify Stress in a Simulated Driving Environment

In this article, a dynamic hybrid choice model of the driver stress arising from cognitive workload is developed and applied to a driving simulator experiment conducted with students at the American University of Beirut. A latent or unobserved variable quantifying the state stress over time is integrated with a discrete choice model of red-light violations. The driver state stress is induced by additional cognitive workload and situational factors in an urban driving context and is dependent on a time-invariant agent effect or individual trait. Driving performance (e.g., speed and acceleration) and physiological measures (e.g., heart rate) are used as indicators of the underlying state stress. The driver state stress is found to be significantly affected by road events or situations (e.g., encountering pedestrians, trucks, and traffic light), the level of cognitive workload, the individual propensity for stress, and the mere driving task (e.g., maintaining control of the car). Moreover, results show that there is a pattern of regulatory driving behavior in response to the increasing stress. The developed model is a mathematical conceptualization of the transactional model of the driver stress and can be integrated within in-vehicle systems to detect/predict the driver state and enhance safety and well-being.

Driver stress detection via multimodal fusion using attention-based CNN-LSTM

Stress has been identified as one of major contributing factors in car crashes due to its negative impact on driving performance. It is in urgent need that the stress levels of drivers can be detected in real time with high accuracy so that intervening or navigating measures can be taken in time to mitigate the situation. Existing driver stress detection models mainly rely on traditional machine learning techniques to fuse multimodal data. However, due to the non-linear correlations among modalities, it is still challenging for traditional multimodal fusion methods to handle the real-time influx of complex multimodal and high dimensional data, and report drivers’ stress levels accurately. To solve this issue, a framework of driver stress detection through multimodal fusion using attention based deep learning techniques is proposed in this paper. Specifically, an attention based convolutional neural networks (CNN) and long short-term memory (LSTM) model is proposed to fuse non-invasive data, including eye data, vehicle data, and environmental data. Then, the proposed model can automatically extract features separately from each modality and give different levels of attention to features from different modalities through self-attention mechanism. To verify the validity of the proposed method, extensive experiments have been carried out on our dataset collected using an advanced driving simulator. Experimental results demonstrate that the performance of the proposed method on driver stress detection outperforms the state-of-the-art models with an average accuracy of 95.5%.

XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity

Stress is considered by many studies to affect traffic safety, and many researchers have attempted to monitor the dynamics of driving stress. Previous research has relied excessively on the positive effects of psychological indicators to improve the accuracy of stress monitoring models. However, psychological data collection sensors have not been widely used in conventional vehicles, which makes it impossible to apply the results of that research to actual driving tasks on a daily basis, even if the accuracy is high. This study designs a real driving task to extract data and proposes a driver's driving stress monitoring model based on driving behaviour, driving environment, and route familiarity. The driving behaviour is described by the speed and acceleration of the vehicle, and the driving environment is quantified by a dilated residual networks (DRN) model thazt divides the video image from the full region into subregions according to the distribution of the driver's attention. Based on the psychological data and driver stress inventory (DSI) results, the study used a K-means 3D cluster analysis to obtain the evaluation method of driving stress and constructed an extreme gradient boosting (XGBoost) model to monitor driving stress. Comparisons of performance with other models show that the XGBoost model significantly outperforms the other three mainstream machine learning algorithms and exceeds most traditional models without the use of psychological data. The model's performance indicators, accuracy, sensitivity, and precision, reached 91.18%-93.25%, 84.13%-89.37%, and 90.25%-91.34%, respectively. The study also summarises the ranking of effects of different scene elements on driving stress for each visual field. The results could make it possible to apply stress monitoring on a large scale to real driving situations, providing urban designers with advice on how to reduce driver stress and directing their attention to those visual areas and visual scene elements that have a higher impact on driving stress and need improvement.

Exploring the Relationships between Work Stress, Psychological Stress and Driver Stress-A Case Study of Chinese Drivers

Exploring the Relationships between Work Stress, Psychological Stress and Driver Stress-A Case Study of Chinese Drivers