Driver Behaviour Research Articles

Delay models for Signalised Intersections with Vehicle Actuated Controlled system in Heterogeneous Traffic Conditions

Delay is a critical parameter used to assess the performance of signalised intersections. Non-deterministic arrival and departure rates of vehicles at intersectionseffect the determination of delay. Furthermore, driver behaviour, vehicle characteristics, and type of signal control system at intersectionwere also influencing parameters. Previous literature has offered analytical and empirical models to estimate the average delay using hourly traffic flow data and a fixed cycle time. In India, major signalised intersections on urban arterial roads are controlled with vehicle actuated controlled system. In the present study, Simpson’sone third rule was applied to estimate the overall delay by using arrival rate and departure rate of vehicles in everycycle. In addition, the current studydevelopedregression-based delay model and modified the Indo-HCM model by changing incremental delay term in themodels. Thisensuredaccurateestimation of the delays at signalised intersections in both the VAC and fixed signal control systems. The proposed modelswere statistically significant, with R2values of 0.89 and 0.51, respectively.

A cross-sectional study on the prevalence of haemorrhoids among lorry drivers in Klang valley, Malaysia

Background: Haemorrhoid is often considered as one of the effects of prolonged sitting especially on a surface with high temperature which mainly occur among lorry drivers who are often exposed to this condition due to the engine located relatively under the driver’s seat. This study aims to assess the prevalence of haemorrhoids among lorry drivers in Klang Valley, Malaysia. A cross sectional study was done by using consecutive sampling. Respondents that fulfill the inclusion and exclusion criteria were interviewed with validated questionnaires and data were analyzed using SPSS. Materials and Methods: A​ cross-sectional study was conducted in Klang Valley, Malaysia. The study was focused on the companies, shops with lorry drivers and lorries that had stop at the rest and services area along the highway in Klang Valley. The method of sampling used was a consecutive sampling with sample size of 70 respondents. All Malaysians who are currently working as a lorry driver regardless of type of company were selected as respondents. Data was collected through an assisted face to face interview and spread google form using a questionnaire from Truck Driver Behaviour and Perceptions Study (1991) by Accident Research Centre of MONASH University.

Mental health and driving behaviour of students and alumni of a university in the United Arab Emirates: a cross-sectional study.

The United Arab Emirates has set the goal to reduce traffic-related deaths to 3 per 100 000 people by 2021. To do this, authorities must assess the factors related to risky driving behaviour. To verify if there are any correlations between driving behaviour and certain variables, including years of driving experience, daily hours of sleep, general markers of mental health, and symptoms of attention deficit hyperactivity disorder (ADHD). Two hundred and seventy-five participants responded to a survey made up of the Manchester driver behaviour questionnaire, the general health questionnaire, the adult ADHD self-report scale, and 2 additional questions. Spearman's coefficient was calculated for correlations between these variables, at statistical significance level P < 0.05. Years of driving experience and hours of sleep had no correlations with driving performance. Markers of general mental health had a weak correlation with risky driving behaviour, and symptoms of ADHD had moderate correlation with risky driving behaviour. Policymakers and public health officials should screen for ADHD during driver licensing examination.

How (where) Does Music Background Hamper Driver Behaviour?

The current study examined the effects of music on Hazard Perception - a skill that serves as a proxy for safe driving. There is contradictory evidence whether or not music engagement leads to decremented driver performance and compromises traffic safety. In the study, 36 participants performed a standard Video-Based Hazard Perception Test under three aural conditions: Road and Traffic Sounds (RS); RS + Driver-Preferred Music; RS + Alternative Music. The results show no effect of aural backgrounds (including music) on the situation awareness portion of the driving task. Music background might affect later stages of the driving task such as response selection and/or response execution (mitigation). The investigation of human factors related to vehicular control should include how (where) music might trigger failures in perception and/or behaviour.

The implications of situation and route familiarity for driver-pedestrian interaction at uncontrolled mid-block crosswalks

Most routine daily trips take place along the same route, a fact that previous studies have not investigated together with the repeated situation of conflicting with other road users. Consequently, our study addresses driver behaviour by separating the driving experience into three categories: (i) drivers unfamiliar with the route, (ii) those route-familiar, and (iii) situation-familiar drivers. The specific case of driver-pedestrian interaction at uncontrolled mid-block crosswalks is investigated. A multi-level factorial experiment including (i) crosswalk design (linear sidewalk and curb extension), (ii) driver familiarity, and (iii) pedestrian time gap acceptance (4, 6, and 8 s) was conducted using a driving simulator. Fifty-two participants were divided into four groups and stratified by age, gender, and driving experience. The minimum instantaneous time to collision, post-encroachment time, maximum car deceleration, and maximum car speed were all used as surrogate safety measures (SSM).Route-familiarity led to higher speed, while situation-familiarity positively affected driving behaviour making drivers more inclined to decrease their speed at circa 100 m before a crosswalk. The curb extension layout enhanced pedestrian safety and mitigated any adverse effects due to familiarity, with a particularly relevant impact on SSM at low accepted time gaps for pedestrians. Situation- and route-familiarity treatment protocols lead to different behaviours among drivers, indicating a clear need to account for these two familiarity levels in experiments on safety-related countermeasures.

Moral disengagement mechanisms in interactions of human drivers with autonomous vehicles: Validation of a new scale and relevance with personality, driving style and attitudes

The introduction of autonomous vehicles (AVs) in the road transportation systems raises questions with respect to their interactions with human drivers’, especially during the early stages. Issues such as unfamiliarity or false assumptions regarding the timid and safe behaviour of AVs could potentially result in undesirable human driver behaviours, for instance “testing” AVs or being aggressive towards them. Among other factors, morality has been determined as a source of aggressive driving behaviour. Following previous approaches on moral disengagement, the current paper argues that moral standards during interactions of human drivers with AVs could potentially blur, leading to the disengagement of self-regulation mechanisms of moral behaviour. The study investigates the impact of moral disengagement on the intention of human drivers to be aggressive towards AVs. To that end, an online survey was conducted including a newly developed survey of moral disengagement, adapted to the context of AVs. Moreover, measures of personality, driving style, attitudes towards sharing the road with AVs and perceived threats were collected. A confirmatory factor analysis provided support for the concept of moral disengagement in the context of AVs. Moreover, relationships between personality, driving style and attitudes towards sharing the road with AVs were found, via a structural equation modelling approach (SEM). The results could have implications in the future driver training and education programmes, as it might be necessary to not only focus on driving skills but also on the development of procedural skills that will improve the understanding of AVs’ capabilities and ensure safer interactions. Efforts on improving attitudes towards AVs may also be necessary for improving human driver behaviour.

An approach to classify distraction driver detection system by using mining techniques

According to the motor vehicle safety division, over the past 5-10 years, usage of motor vehicles has rapidly increased, in that specifical usage of cars has grown tremendously. The major contribution of this paper is a systematic evaluation of the scholarly literature on driver distraction detection techniques. Our driver distraction detection framework offers a systematic overview of evaluated methodologies for detecting driver attention. So, we need to develop a model that classifies each driver's behaviour and determines its corresponding class name. To overcome this dispute, we have attained an appreciable number of deep learning algorithms on the dataset like convolutional neural network (CNN) and VGG16 to detect what the driver is doing in the car as given in the driver images. This process can be done by predicting the likelihood of the driver's actions in each picture. Of all models, we distinguished that the VGG16 Algorithm has conquered CNN with a loss of 0.298 and an Accuracy of 91.7%.

Temporal Analysis Based Driver Drowsiness Detection System Using Deep Learning Approaches

With the development of technology, artificial intelligence comes into our lives more and also comes up as a solution to many problems. Recently, deep learning approaches have been bringing fast and highly accurate solutions to problems. In this work, within the scope of Advanced Driver Assistance Systems (ADAS), deep learning based driver drowsiness detection system is proposed. First, face regions of drivers are detected using SSD MobileNet object detection method. The aim is to detect the eye, mouth and head positions of the drivers from this face region and to make a situation estimation with the combinations of these detected objects which are “normal”, “drowsy” and “danger”. The proposed approach examines the driver's behaviour over a certain period of time for making a decision, rather than a one-time eye closure or yawning decision. The detected eye, mouth and head positions are monitored and recorded over a period of time. Finally, these merged patterns are classified with Convolutional Neural Networks (CNN). Experimental results show that the performance of proposed novel CNN approach outperforms existing approaches in literature.

The influence of the police on vehicle speed in built-up areas

In many cases, the effects of road accidents have significant impact on human health. This translates directly into society, in both material and emotional terms. The severity of the injury in a road event depends largely on the speed of the vehicles. Excessive vehicle speed is of particular concern in incidents involving vulnerable road users. As confirmed by literature analysis and research, in most cases drivers do not respect the speed limits introduced by the road signs. Existing solutions allow to introduce permanent or temporary speed control. This paper investigates speeding and driver behaviour in built-up areas near schools and pedestriancrossings. The aim of the research described in the paper is to determine the influence of the effectiveness of police actions on speed reduction and improvement of road safety.

Designing a conceptual model for the formation of unsafe antisocial behaviors in motor vehicle drivers: a grounded theory study.

Many accidents caused by vehicles are the result of improper driving behavior. Use the vehicle in an antisocial form has led to a phenomenon called driving violence. Antisocial behavior while driving has a potential risk to other road users. This study aims to explain the conceptual framework of the formation of unsafe antisocial behaviors in the road traffic field. This qualitative grounded theory study was conducted with exploratory methods. 31 participants were included in this study. Purposeful and theoretical sampling was used in this study. The data collection process was semi-structured interviews. Codes, subcategories, and categories were extracted by the inductive process and analyzed by Corbin and Strauss approach. Trustworthiness criteria were used to assure the quality of the results. The data analysis process continued until there were no new concepts. A conceptual model was developed to explain the different relationships between the main categories extracted from the study. 10 main categories with 44 subcategories were extracted. Categories include cultural factors, educational factors, rules, economic factors, psychological factors, infrastructure weakness, poor socialization of individuals, violent driving, reduced social welfare and reduced traffic safety. The people's socialization weakness in society was considered as the core concept in the process of formation of these behaviors. The conceptual model obtained from this study can be used in developing prevention programs and identifying the required interventions Considering the negative consequences caused by this type of behavior, its prevention should be the focus of road traffic policy makers.

Comparison between wrap around screens and a head mounted display on driver muscle and kinematic responses to a pedestrian hazard

As driving performance relies heavily on the interpretation of visual information, driving simulators require a visual display that can effectively communicate the virtual environment to the driver. Most high-fidelity visual displays include an expensive system of high-definition projectors and wraparound screens. To reduce the overall cost of a driving simulator while preserving the generalizability of results to naturalistic driving, head mounted displays (HMD) are being considered as a substitute visual cueing system. Recent innovations to virtual reality technologies are encouraging, however, differences between HMDs and more traditional visual displays have not been explored for all types of driving measures. In particular, while existing literature provides insight into the validity of HMDs as a substitute for higher fidelity visual displays in tests of driver behaviour and performance, there is a gap in the literature regarding differences in physiological responses. In the current study, upper body muscle activation and joint angle ranges were compared between an Oculus™ Rift Development Kit 2 HMD and a system of wrap around screens. Twenty-one participants each completed two simulated drives, one per display, in a counterbalanced order. During the simulation, drivers encountered unanticipated pedestrian crossings during which peak surface electromyography, root-mean-square of the surface electromyography signal and joint angles were determined bilaterally on the upper limbs. No significant differences (p ≤ 0.05) were observed between the Oculus™ Rift HMD and the wrap around screens for all dependent variables with the exception of left joint range of motion in female participants, suggesting that the HMD reduced field of view had a minimal effect on driver kinematics and no effect on muscle activation levels. Upper body bracing was observed during the hazard response time segments characterized by significantly increased muscle activity during hazard response time segments and minimal joint movement. Considering the lack of significant kinematic and muscle activation differences between the two visual inputs, HMD technology for hazard response may provide a suitable alternative to wrap around screens for studying kinematic responses during hazardous driving scenarios.

A graph method of description of driving behaviour characteristics under the guidance of navigation prompt message

AbstractTo verify whether a graph is suitable for describing driver behaviour performance under the effects of navigation information, this study applies two types of prompt messages: simple and detailed. The simple messages contain only direction instructions, while the detailed messages contain distance, direction, road and lane instructions. A driving simulation experiment was designed to collect the empirical data. Two vehicle operating indicators (velocity and lateral offset), and two driver manoeuvre indicators (accelerator power and steering wheel angle) were selected, and T-test was used to compare the differences of behavioural performance. Driving behaviour graphs were constructed for the two message conditions; their characteristics and similarities were further analysed. Finally, the results of T-test of behavioural performance and similarity results of the driving behaviour graphs were compared. Results indicated that the two different types of prompt messages were associated with significant differences in driving behaviours, which implies that it is feasible to describe the characteristics of driving behaviours guided by navigation information using such graphs. This study provides a new method for systematically exploring the mechanisms affecting drivers’ response to navigation information, and presents a new perspective for the optimisation of navigation information.

Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means

Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored.This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations.

Intentions and willingness to engage in risky driving behaviour among high school adolescents: evaluating the bstreetsmart road safety programme

ObjectiveTo investigate the impact of a road safety programme on adolescents’ willingness to engage in risky behaviour as probationary drivers, adjusted for covariates of interest.MethodThe bstreetsmart is a road safety...

Analysis of right-turn vehicular driving paths at uncontrolled T-intersections

Turning vehicle maneuvers highly influence vehicular safety at uncontrolled T-intersections due to their complexity and the inattentive behaviour of vehicle drivers. It was observed that the drivers while maneuvering from a minor road to a major road (viz., left-side driving in an Indian context) were distracted from conventional driving paths, which may considerably increase the risk of a crash. The present study examined the impact of various factors on driving path distractions. To fulfil the objective, a Binary Logit Model (BLM) and machine learning techniques, viz. Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) models, were developed by considering driving path distractions as a dependent variable and the remaining set variables as independent variables. The model results revealed that driving path distractions are highly sensitive to the turning vehicle type, maneuvering speed of turning vehicles, running speed of through vehicles on a major road, through traffic volume, vehicle gap, waiting time, and right-turning traffic volume. Further, the study concluded that maneuvering speed, waiting time, and adequate available vehicular gaps significantly impact the behaviour of vehicular drivers when changing lanes. Field engineers could use the results of this study to start taking control measures at uncontrolled T-intersections.

Driver Mental Fatigue Detection Based on Head Posture Using New Modified reLU-BiLSTM Deep Neural Network

Early detection of driver mental fatigue is one of the active areas of research in smart and intelligent vehicles. There are various methods, based on measuring the physiological characteristics of the driver utilising sensors and computer vision, proposed in the literature. In general, driver behaviour is unpredictable that can suddenly change the nature of driving and dynamics under mental fatigue. This results in sudden variations in driver body posture and head movement, with consequent inattentive behaviour that can end in fatal accidents and crashes. In the process of delineating the different driving patterns of driver states while active or influenced by mental fatigue, this paper contributes to advancing direct measurement approaches. In the novel approach proposed in this paper, driver mental fatigue and drowsiness are measured by monitoring driver’s head posture motions using XSENS motion capture system. The experiments were conducted on 15 healthy subjects on a MATHWORKS driver-in-loop (DIL) simulator, interfaced with Unreal Engine 4 studio. A new modified bidirectional long short-term memory deep neural network, based on a rectified linear unit layer, was designed, trained and tested on 3D time-series head angular acceleration data for sequence-to-sequence classification. The results showed that the proposed classifier outperformed state-of-art approaches and conventional machine learning tools, and successfully recognised driver’s active, fatigue and transition states, with overall training accuracy of 99.2%, sensitivity of 97.54%, precision and F1 scores of 97.38% and 97.46%, respectively. The limitations of the current work and directions for future work are also explored.

Drivers’ attitudes to road safety in the South East Asian cities of Jakarta and Hanoi: Socio-economic and demographic characterisation by Multiple Correspondence Analysis

This study investigates drivers’ attitudes towards road safety in Jakarta and Hanoi. A comprehensive analysis of two datasets was achieved by multi-faceted analytical approaches of Multiple Correspondence Analysis (MCA), Cluster analysis (Hierarchical and Two-step) (CA), Principal Component Analysis (PCA) and Multinomial Logistic Regression (MLR). MCA and CA were used to segment the drivers based on their sociodemographic characteristics and driving related information where three clusters were identified from each dataset. There were 60+ attitudinal statements in the datasets, therefore dimension reduction process was conducted using PCA. Five Principal Components were generated from each dataset, four of them common to Jakarta and Hanoi (“Safe driving practices and behaviour”, “Road safety enforcement and education”, “Driver behaviour at signalised junctions”, “Road infrastructure and roadside facilities”), the fifth component being unique to each city (“Road infrastructure design issues” for Hanoi and “Type of motorised vehicles” for Jakarta). Finally, MLR was used to investigate how the level of perception on the identified components varied across sociodemographic clusters in each city. Employed young adults in Jakarta perceive that “Driver behaviour at signalised junctions” directly influences road safety compared to teenage and young adults who are in education. The reverse is true for Hanoi. Employed young drivers in both cities are less likely to recognise the importance of “Road safety enforcement and education” compared to teenage and young drivers in education. The study shows that the perception of road safety among drivers varies based on various factors including their sociodemographic traits and driving experience.

Driver behaviour and experience around automated freight vehicle platoons

Autonomous vehicles (AVs) are becoming more common and the market size is growing. In the truck industry, the deployment of a platoon of AVs is emerging as a response to existing safety and environmental problems. However, before autonomous technology can be applied to all vehicles in a traffic flow, mixed traffic containing both AVs and non-autonomous vehicles (NVs) is inevitable. In this study, using a driving simulator and the National Aeronautics and Space Administration Task Load Index survey, the behaviour of NVs at highway inflows and outflows was analysed to understand platoon operation management in mixed-traffic conditions. Unlike previous studies, this study considered an environment in which automated freight vehicle platoons occupy a designated lane and examined vehicle driving characteristics and driver psychology as NVs entered and exited the highway. Statistical methods (analysis of variance and multi-variable analysis of variance) were used to analyse differences according to the situation. It was found that, in entry, a platoon had an effect on drivers and the platoon size had a larger effect than inter-platoon spacing. However, among drivers with driving experience above a certain level, the platoon parameters did not have significant effects on driving. The results of this study provide basic information about the operation and management of a platoon of AVs on mixed-traffic highways.

Implementation of a model of awareness-raising for taxi motorcyclists in Benin in relation to helmet use: a quasi-experimental study

BackgroundThis study aims to test the effectiveness of an awareness-raising model designed based on the theory of planned behaviour regarding helmet use for motorcycle taxi drivers.MethodsThis quasi-experimental study took place in the cities of Parakou (intervention group) and Porto Novo (control group). Over a three-month period, a package of awareness-raising activities, based on the theory of planned behaviour, have been implemented in the intervention area. Data relate to knowledge, attitudes and practices regarding helmet use was collected prospectively before the intervention, at the end, and 6 months later. Stata 15 was used for data analysis. Chi-square or Fisher, Student’s or Kruskal-Wallis tests was carried out. The difference-in-difference method was used to determine the specific effect of the awareness activities.ResultsAfter the intervention, there was an improvement in the total score in both groups compared to baseline. The total score increased by 0.2 (0.06–0.3) in the experimental group when the number of sessions attended increased by one (p = 0.005). The difference-in-difference estimator measured among subjects who attended at least one awareness session, controlling for socio-demographic variables, showed a significantly higher difference in the total score of subjects in the experimental group compared to those in the control group both at the end of the interactive sessions and 6 months later.ConclusionThis model improves the helmet-wearing behaviour of motorbike taxi drivers in the experimental area. It could be adapted and applied to other socio-professional groups and other types of users.

Impacts of advanced driver assistance systems on commercial truck driver behaviour performance using naturalistic data

Traffic crashes tend to be more serious when commercial trucks are involved. And an increasing number of commercial vehicles are equipped with advanced driver assistance systems (ADAS) for traffic safety improvement. To this end, this study investigated the impacts of ADAS with respect to forward collision warning (FCW), urban forward collision warning (UFCW), lane departure warning (LDW), headway monitoring and warning (HMW), and speed limit indicator (SLI) on commercial truck drivers' behaviours using naturalistic data. Participants experienced two different test scenarios with two different ADAS warning configurations: Visual and audio alerts in the inactive state and in the active state. After activating alerts, 62%, 35%, 69%, 81% and 73% of drivers received less warnings in terms of FCW, UFCW, LDW, HMW and SLI respectively; the average of FCW, UFCW, LDW, HMW and SLI respectively declined 22%, 13%, 28%, 45% and 15%, but median changed little except for LDW and HMW. Then, the further analysis results by the one-way analysis of variance (ANOVA) method revealed that all warnings except UFCW had positive effect on commercial truck drivers' behaviour performance. The findings provide important references for commercial truck driver training and supervision in the age of advanced technology.