Driver Behavior

The detection of fatigue in drivers has been studied by different researchers and academics. Through this review, it was intended to analyze and summarize the studies that investigate the detection of fatigue in drivers and its effects on the performance of the driving task, as well as the consequent effect of drowsiness and the persistence of drivers in continuing to drive tired. This review was based on relevant articles published in Scopus. The studies included in this review were carried out with humans at the controls of vehicles, and the fatigue of drivers was evaluated. Eight articles were included in the systematic review.
Some studies were carried out in a real road driving context and the most used methods to assess fatigue were EEG, ECG and sEMG.
Rest and recovery breaks had some positive effects, particularly on the perception of discomfort and sleepiness. The characteristics of the participants and the duration of the experiments seem to be the variable that most influences the results. Future research should focus on improving experimental protocols and instrumentation, so that the results represent the real conditions of performance of the driving task, trying to get drivers to take breaks while driving vehicles, when driving for long and uninterrupted periods of time.
Relevance for the industry: the introduction of non-invasive data collection equipment that returns to the user/driver of the vehicle the results they need to remain alert, while performing the driving task, seems to be effective in producing the alert, but inefficient in order to the driver takes a break during his journey.
The results will be useful to identify the need for future research, which will result in a decrease in the number of road and work accidents, with victims, leading to the adoption of good practices in the measures to be adopted to improve road safety, with breaks during driving will provide an improvement in the state of alertness essential for the performance of the driving task.
Keywords — Driver, Fatigue, Road Safety, Sleepiness/drowsiness.

Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable mod...

This study aims to examines the contribution of personality and driver behavior on road accident among bus driver in Indonesia. The study adopts a survey method to elicit responses from a sample of 400 bus driver who were selected as a Respondent. The Data collecting using the Big Five Personality questionnaires, Driver Behavior questionnaires and Road Accident Inventory. The data collected were analysis confirmatory factor analysis and Structural Equation Model (SEM). The SEM results indicated (a) Bus Driver behavior is violent and error have contribution on road accident in Indonesia 8 (b) the big personality factor had significant influences on the latent mediator, path coefficients from-0,50 (conscientiousness) and-0.74 (openness) to-0.75 (ektraversion). As, predicted the effect of conscientiousness, openness and ektraversion were negative and the influences of Neuroticism and agreeableness were positive (path coefficient = 0.35 and 0.81). As expected by the laten mediator variable for road accident risk was relatively strongly and significantly predicted by the latent mediator variable (path coefficient = 0.13). The five personality factor accounted the 38% of the unique in aberrant driving behavior. The driving behavior have direct influences 55% of the variances in road accident among bus driver in Indonesia. The factor of driver behavior error and lapses have strong affect to road accident The implication this study show that there is need for an intervention program in order to reduce the prevalence of accident involvement due to personality factors. The latter should be focused on managing driving behavior.

The use of driver behavior has been considered a complex way to solve road safety complications. Car drivers are usually involved in various risky driving factors which lead to accidents where people are fatally or seriously injured. The present study aims to dissect and rank the significant driver behavior factors related to road safety by applying an integrated multi-criteria decision-making (MCDM) model, which is structured as a hierarchy with at least one 5 5 (or bigger) pairwise comparison matrix (PCM). A real-world, complex decision-making problem was selected to evaluate the possible application of the proposed model (driver behavior preferences related to road safety problems). The application of the analytic hierarchy process (AHP) alone, by precluding layman participants, might cause a loss of reliable information in the case of the decision-making systems with big PCMs. Evading this tricky issue, we used the Best Worst Method (BWM) to make the layman's evaluator task easier and timesaving. Therefore, the AHP-BWM model was found to be a suitable integration to evaluate risky driver behavior factors within a designed three-level hierarchical structure. The model results found the most significant driver behavior factors that influence road safety for each level, based on evaluator responses on the driver behavior questionnaire (DBQ). Moreover, the output vector of weights in the integrated model is more consistent, with results for 5 5 PCMs or bigger. The proposed AHP-BWM model can be used for PCMs with scientific data organized by traditional means.

Spanish:
La investigación accidentológica a través del DBQ ha mostrado una tendencia creciente en el mundo entero durante los últimos cinco años. No obstante, en poblacio-nes hispanohablantes, como es el caso de la colombiana, no se cuenta con adaptaciones o investigaciones previas con este instrumento. Este trabajo presenta los resultados de la aplicación de la traducción de la escala reducida del DBQ en una muestra de 487 conductores de la ciudad de Bogotá, Colombia. En él se analizan los factores y componentes de la prueba, y se presentan los resultados y análisis estadísticos. Se hace una discusión teórica a partir de los resultados sobre las causas atribuibles a la accidentalidad de conductores y se emiten recomendaciones para la construcción e implement-ación de programas de intervención. Esta investigación pro-vee evidencia empírica acerca de las causas de accidentes en la ciudad de Bogotá a través del estudio de las violaciones de tránsito y los errores de conducción.

English:
Accidentological research through the DBQ shows a growing trend in the world over the past five years. However, in Spanish-speaking populations (specially in the Colombian case) there are not previous research experiences with this instrument. This paper presents the results of applying the translation of the reduced scale (28 items) of DBQ to a sample of 487 drivers in the city of Bogotá, Colombia. It examines the factors and components of the test and presents the results and statistical analysis. It is presented a theoretical discussion from the results on the causes of accidents attributed to drivers and makes recommendations for the construction and implementation of intervention programs. This research provides empirical evidence about the causes of accidents in Bogotá through the study of traffic violations and driving errors.

This paper presents the results of an online survey of licensed driver comprehension of the right-turn signal displays with a focus on the flashing yellow arrow (FYA) and also including the circular green and red and red arrow. Recruitment postcards were mailed to a random sample of 9,872 residents in Oregon. The online survey yielded 399 responses. The open-ended responses were coded for comprehension and analyzed. The results suggest that FYA for right turns is well understood by Oregon drivers despite its current novelty (only two locations at the time of the research). Importantly, survey respondents were more likely to recognize the yielding requirement of the permissive movement and associate the yielding with pedestrians with the FYA over the circular green (CG) display. The research also confirmed that the expected driver response to the red arrow display for right turns is not well understood (only 52% of the respondents correctly stated the expected driver response). Binary logistic regression modeling revealed that the driver's age and their educational level were significant factors in comprehension.

The focus of this study is roadway safety in American Indian reservations. We provide new sources of data and policy- relevant findings to address the unusually high rates of roadway fatalities and injuries among American Indians. Qualitative methods were used to generate and analyze data from people with the most direct knowledge of and responsibility for reservation roadway safety. Four case studies were conducted in partnership with the tribal governments of the Red Lake, Fond du Lac, Leech Lake, and Mille Lacs reservations; these data sources include fieldwork (90 days), interviews (n=102), focus groups (n=8), and short surveys (n=220). These data are triangulated with data from FHWA’s 2016 nationwide survey of tribes and states (n=196). Key findings from this extensive data analysis are: 1. Pedestrian safety is a critical, yet under-recognized issue on reservations. This is unequivocal across all data sources and differentiates reservations from rural areas in general. 2. Reservation road engineering and repair are very high priorities according to both tribe and state governments. 3. Reckless driving is a multi-faceted concern, including not only impaired driving but also cell phone distraction and speeding. 4. Education and enforcement to increase seatbelt and car seat use are named as high priorities in the national survey. 5. Tribes need better cooperation with local, state, and federal agencies. Priorities include addressing data quality and sharing issues better inter-jurisdictional cooperation for infrastructure and enforcement. The study concludes with recommendations to improve roadway safety in reservations and for further research.

Road safety is a global issue; as well same issue is prevailing in Sri Lankan society too. Ignorance of road sign is a one of major issue causing for road crashes. Ignorance of road sign occurs due to lack of awareness of drivers on signs and communication errors of road signs. In this research, it describe about road sign ignorance of Sri Lankan drivers.
Ignorance of road signs caused due to lack of awareness and communication issues of drivers. This can be change with raising drivers’ awareness, behavior, attitude and perception on road sign. As a media and communication student this research is important to study of driving lifestyle, behavior, communication disturbances, noises and issues in a society.

Traffic control (TC) is a challenging problem in today’s modern society. This is due to several factors including the huge number of vehicles, the high dynamics of the system, and the nonlinear behavior exhibited by the different components of the system. Poor traffic management inflicts considerable cost due to the high rate of accidents, time losses, and negative impact on the economy as well as the environment. In this paper, we develop a traffic control system based on the Bayesian interpretation of probability that is adaptive to the high dynamics and non-stationarity of the road network. In order to simulate the traffic non-stationarity, we extend the Green Light District (GLD) vehicle traffic simulator. The change in road conditions is modeled by varying vehicle spawning probability distributions. We also implement the acceleration and lane changing models in GLD based on the Intelligent Driver Model (IDM).
Index Terms—reinforcement learning, traffic control, traffic simulation, multi agent systems, driver behavior

Learning to drive can be a daunting experience.  Getting a licence is a rite of passage,  freedom, and coming of age, glorified for decades in cinema movies .

However, the reality of learning to drive can be difficult – the price of obtaining a driving licence can be a burden for many with driving lessons being a significant cost factor. With EDT being compulsory from April 2011, learner drivers must take 12 one-hour compulsory driving lessons with an ADI.

However, it’s not only the cost that causes concern for learners – issues such as the length of time the learning to drive process takes, can be arduous for many learners (minimum of 6 months). Safety issues pose a critical consideration for our learner drivers - 158 people lost their lives on Irish roads in 2017 . On average 13 learner drivers lose their lives on Irish roads annually (RSA) .

Learner drivers obtain a learner’s permit to allow them practice driving while supervised, however, there is a protracted history of generally unregulated driving with relatively low enforcement rates for unaccompanied learner drivers in Ireland (CAS 2009).

The most significant challenge facing this dissertation was consideration of how to interpret, reflect and translate findings into recommendations. As all recommendations require change to existing systems/processes, it is reasonable to consider findings in light of their implications for change management. The recommendations are as much to do with modifying driving culture as with fundamental process change. Unless culture changes alongside process change, it is unlikely that the recommendations cited can be successfully implemented.

Driving provides a terrific sense of freedom, but it’s also potentially the most dangerous action we do daily. Learning to drive isn’t just about passing a test – it’s about learning skills from experience and continually improving and advancing the quality of those skills.

—In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.

With research revealing low road safety campaign efficacy and links between reckless driving behaviors and crash frequency, further investigation into the foundations and composition of driver education and training is required. Through two studies, the current research aimed to develop a measure that utilized the principles of Kelly’s (1955) personal construct theory to a) elicit constructs, or construals, specific to selected elements of reckless driving behaviors, b) pilot a method in which the elicited constructs could be measured by asking participants to choose those they deemed most important, c) group participants based on their constructs, and d) assess between-group differences in self-reported reckless driving behavior. Results suggest that drivers can be categorized based on the constructs they use, and that rates of self-reported past engagement in reckless driving behavior, and willingness to do so in the future, vary systematically between these construal-based driver categories. Continuing research is required to develop and test applications of these findings.

When Elaine Herzberg was killed while crossing a road with her bicycle in Tempe, Arizona, confidence in autonomous vehicle technology was at an all-time high. In their accident report, the National Transportation Safety Board (NTSB) said that the vehicle in autonomous mode had failed to identify Herzberg until 1.2 seconds before impact. NTSB also said the system design did not include consideration for jaywalking pedestrians [1]. The accident created an uproar among the public and made the AV makers rethink testing beta-software on an actual road. Fast forward to 2021; there are announcements about deploying fully autonomous driving systems from big tech and car manufacturers. There is no government regulation or any other regulatory body to verify and validate the safety of these technologies. Since a market launch without safety assurance would not be acceptable by society or lawmakers, much time and resources have been invested into AV's safety assessment in recent years. There are several motivations behind the introduction of AVs, including road safety, driving comfort, energy efficiency, and broader mobility access [2]. According to the National Highway Traffic Safety Administration (NHTSA) report, 94% of serious vehicle crashes in the US are caused by human factors [3]. AVs promise to significantly reduce this public health crisis by removing many of the mistakes human drivers recurrently make. However, many challenges are creating bottlenecks, including the AV's perception system's performance, safety validation, legal and ethical issues, human-machine interaction, etc. Between September 2014 and January 2017, 11 suppliers and manufacturers reported 26 crashes while testing self-driving technology on public roads in California [4]. Before AVs can be deployed at scale, traffic safety issues related to automation need to be adequately addressed to avoid unacceptable situations like the one that happened in Arizona. The Society of Automotive Engineers (SAE) defines six levels of driving automation ranging from level 0 (fully manual) to level 5 (fully autonomous) [5]. "Driving automation" refers to both Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). ADAS encompasses features such as cruise control, adaptive cruise control. These systems support human drivers and enhance safety. Next-generation ADAS (traffic jam chauffeur, automated valet parking) and ADS may ultimately be able to operate a vehicle without human intervention. SAE classified ADAS features as level 0-2 and ADS as level 3-5. Verified testing methods accepted by all stakeholders for ADAS are already in place. The safety validation of next-generation ADAS to ADS (SAE level 3-5) in complex environments calls for newer approaches because of increased Operational Design Domain (ODD) 1 and less scope for human intervention. Due to the high degree of realism required for the measurement, conducting tests in the real road with other traffic users is the highest fidelity form of testing. However, testing AVs' response in unsafe situations in the real world is not feasible without placing the other road users, such as other human-driven vehicles, bicyclists, pedestrians, in danger. Moreover, these tests take a considerable amount of time before achieving a statistical conclusion regarding safety. Repeatability of the tests is also an issue. These factors make depending solely on real-world testing infeasible. Virtual testing can complement this real-world testing. Virtual testing has previously been used to verify and validate SAE level 0-2, and currently, companies like Waymo, Zoox, and Aurora are using virtual environments to train and test vehicle automation of SAE level 4-5. Waymo is using their virtual environment CarCraft to simulate 25000 AVs every day to drive 8 million miles in the virtual world [6]. Scenario-based testing is a promising method where individual traffic situations are tested using virtual simulation. These tests are repeatable, safe, and can be done in parallel, reducing the amount of time required for testing. Domain experts usually handcraft these scenarios to explore the underlying system's vulnerabilities. However, handcrafted critical scenarios from the domain experts suffer from limited variation. It only accounts for known-safe and known-unsafe situations and does not handle unknown-unsafe situations. State-of-the-art scenario-based validation focuses mainly on the variation of the static part of the scenarios, namely the environment, infrastructure, road network. These variations would have been sufficient for the previous generation driver autonomy features (e.g., automatic braking, lane following) because of limited ODD and scope for human intervention during critical situations. But newer technologies up to fully autonomous driving require the vehicle to perceive its surroundings with dynamic traffic participants (other human-driven vehicles, pedestrians, cyclists) and take decisions appropriately, with limited to no human intervention. To ensure the safety and appropriateness of these decisions, high/full autonomous driving systems need to be tested with a wide variety of combinations of the dynamic elements.

Objective The objective of this research was to evaluate the immediate impact of the demerit points system on speeding behavior of drivers in Al Ain. Al Ain is the fourth largest city in the United Arab Emirates, located about 120 Km from Dubai. Methods The spot speed data of randomly selected vehicles using speed guns was collected from three major arterial urban roads.The data was collected three months before and three months after the implementation of the demerit points system. The free flow conditions were considered during the data collection. The data was analyzed using SPSS program. Results and Conclusions The results revealed that the demerit points system has statistically no significant impact on the speeding behavior of drivers in Al Ain. One of the plausible reasons for insignificance affect of the demerit point system could be the lack of effective traffic monitoring system. This study recommends that to influence the drivers’ behavior an effective traffic monitoring system should be provided to achieve the desired outcomes of introducing tougher laws such as demerit point system. The study also recommends to collect more data to investigate the changes in the speeding behavior of drivers.

Grab is one of the Transport Network Vehicle Service (TNVS) in the Philippines. GrabCar is one of services that Grab provides. GrabCar driving is one of the transportation services in the Philippines.

The study focuses on the advantages and disadvantages of a full-time GrabCar driver. The researchers narrowed down the focus of the study by creating statements of the problem. What are the benefits that a GrabCar driver considered when choosing GrabCar driving as his full-time job, the advantages and disadvantages on his work and the struggles that he experiences in his work.

The respondent is a professional, male, and currently full-time GrabCar driver for one year that lives in Taguig City for 30 years. The researchers gathered data within four days. In- depth interview and observation was made with the respondent in his house. Based on the findings, a GrabCar driver considered GrabCar driving as his full-time job for it provides higher income, incentives and no supervision. The advantages are identified by the respondent which are cashless transaction, weather condition, traffic and flexibility of time. While, the disadvantages are the traffic and working alone. Moreover, the struggles that the respondent particularized are the physical pain and the rider's behavior.


Keywords: TNVS, Grab, GrabCar, Riders, Full-time, Driving

Sixteen states and the District of Columbia have adopted driving privilege legislation which allows undocumented residents to obtain legal authorization to drive. This working paper by the Indiana Driving Privilege Card Project proposes driving privilege legislation for Indiana and aims to contextualize and quantify the projected social and economic benefits to the state across five areas: traffic safety, insurance, economic growth, state revenue, and law enforcement.

Authors: Patrick Aimone, Edward Brunicardi, Camila Antelo Iriarte, Megan Brinkman, Alexa Dalgleish, Alyssa DiPaolo, Hana Ferrero, Grace Franco, Niam Malkani, Benjamín Rascón Gracia, Anna Rask, Joel Skelley, Nicholas Trittipo, and Madeline Ward.

Recommended citation: Aimone, P., et al. (2021). Safer Roads, A Stronger Indiana: Projected Social and Economic Effects of Driving Privilege Card Legislation in Indiana. Notre Dame Student Policy Network.

Reckless driving is a major contributing factor to road morbidity and mortality. While further research into the nature and impact of reckless driving, particularly among young people, is urgently needed, the measurement of reckless driving behaviour also requires increased attention. Three major shortcomings apparent in established measures of driver behaviour are that they do not target the full range of reckless driving behaviours, they measure characteristics other than driving behaviours, and/or they fail to categorise and label reckless driver behaviour based on characteristics of the behaviours themselves. To combat these shortcomings, this paper reports the development and preliminary validation of a new measure of reckless driving behaviour for young drivers. Exploratory factor analysis of self-reported driving data revealed four, conceptually distinct categories of reckless driving behaviour: those that increase crash-risk due to (a) distractions or deficits in perception, attention or reaction time (labelled “distracted”), (b) driving under the influence of drugs or alcohol (labelled “substance-use”), (c) placing the vehicle in an unsafe environment beyond its design expectations (labelled “extreme”), and (d) speed and positioning of the vehicle relative to other vehicles and objects (labelled “positioning”). Confirmatory factor analysis of data collected from a separate, community sample confirmed this four-factor structure. Multiple regression analyses found differences in the demographic and psychological variables related to these four factors, suggesting that interventions in one reckless driving domain may not be helpful in others.

The Project entitled “Eye Tracking based Driver Fatigue Monitoring and Warning System”
consists of the hardware and the software modules. The main idea behind this project is to
develope a non-intrusive system which can detect fatigue of driver and issue a timely warning.
Since large number of road accidents are caused by driver drowsiness. Hence this system will
be helpful in preventing many accidents, and consequently save money and reduce personal
suffering.
This system will detect eye movement to detect the fatigue state of driver. By monitoring the
eyes using camera and developing an algorithm we can detect symptoms of driver fatigue early
enough to avoid an accident. So this project will be helpful in detecting driver fatigue in
advance and will gave a warning output in form of sound and vibration. For indication of
warning we will use two approaches i.e one by blowing alarm and second by seat belt vibration
whose frequency will vary between 100 to 300 Hzs. Moreover the warning will be deactivated
manually rather than automatically. So for this purpose a deactivation switch will be used to
deactivate warning.
Moreover if driver felt drowsy there is possibility of sudden acceleration or de-acceleration
hence we can judge this by plotting a graph in time domain and when all three input variables
shows a possibility of fatigue at one moment then a warning signal is shown in form of text or
red colour circle. This will directly give an indication of drowsiness/fatigue which can be
further used as record of driver performance or can be used by traffic police which can take
further action accordingly.

Road traffic is known to have its own complex dynamics. One implication of complexity is that road traffic collisions have become an unwelcome but unavoidable part of human life. One of the major causes of collisions is the human factor. While car manufacturers have been focusing on developing feasible solutions for autonomous and semi-autonomous vehicles to replace or assist human drivers, the proposed solutions have been designed only for individual vehicles. The road traffic, however, is an interaction-oriented system including complex flows. Such a system requires a complex systems approach to solving this problem as it involves considering not only pedestrians, road environment, but also road traffic which can include multiple vehicles. Recent research has demonstrated that large-scale autonomous vehicular traffic can be better modeled using a collective approach as proposed in the form of vehicular cyber-physical systems (VCPS) such as given by Li et al. (IEEE Trans Parallel Distrib Syst 23(9):1775–1789, 2012) or Work et al. (Automotive cyber physical systems in the context of human mobility. In: National workshop on high-confidence automotive cyber-physical systems, Troy, MI, 2008).  To the best of our knowledge, there is currently no comprehensive review of collision avoidance in the VCPS. In this paper, we present a comprehensive literature review of VCPS from the collision-avoidance perspective. The review includes a careful selection of articles from highly cited sources presented in the form of taxonomy. We also highlight open research problems in this domain.

Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about driving environment.
Studies have proved that automated vehicles have a potential to decrease traffic congestion on road networks by reducing the time headway, enhancing the traffic capacity and improving the safety margins in car following. Furthermore, vehicle movement and driver’s behavior of conventional vehicles will be affected by the presence of automated vehicles in traffic networks. Despite different encouraging factors, automated driving raises some concerns such as possible loss of situation awareness, overreliance on automation and degrading driving skills in absence of practice. Moreover, coping with complex scenarios, such as merging at ramps and overtaking, in terms of interaction between automated vehicles and conventional vehicles need more research.
This thesis work aims to investigate the effects of automated vehicles on driver’s behavior and traffic performance. A broad literature review in the area of driving simulators and psychological studies was performed to examine the automated vehicle effects on driver’s behavior. Findings from the literature survey, which has been served as setup values in the simulation study of the current work, reveal that the conventional vehicles, which are driving close to the platoon of automated vehicles with short time headway, tend to reduce their time headway and spend more time under their critical time headway. Additionally, driving highly automated vehicles is tedious in a long run, reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of automated vehicles on traffic performance, a microscopic simulation case study consisting of different penetration rates of automated vehicles (0, 50 and 100 percentages) was conducted in VISSIM software. The scenario network is a three-lane autobahn segment of 2.9 kilometers including an off-ramp, on-ramp and a roundabout with some surrounding urban roads.
Outputs of the microscopic simulation in this study reveal that the positive effects of automated vehicles on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably decreased by 8.09% during p.m. peak hours in scenario with automated vehicles. Besides, Smoother traffic flow with less queue in the weaving segment was observed. Result of the scenario with 50% share of automated vehicles moreover shows a feasible interaction between conventional vehicles and automated vehicles. Meaningful outputs of this case study, based on the input data from literature review, demonstrate the capability of VISSIM software to simulate the presence of automated vehicles in great extent, not only as an automated vehicle scenario but also a share of them, in traffic network. The validity of the output values nonetheless needs future research work on urban and rural roads with different traffic conditions.

Traffic simulation has become an invaluable part of the traffic engineering toolbox. However, the majority of driver models are designed to recreate traffic performance based on interactions among vehicles. In keeping with this pursuit, most are fundamentally built to avoid collisions. This limits the applicability of using these models for addressing safety concerns, especially those regarding pedestrian safety performance. However, by explicitly including some of the sources of human error, these limitations can, in theory, be overcome. While much work has been done toward including these human factors in simulation platforms, one key aspect of human behavior has been largely ignored: driver distraction.

This work presents a novel approach to inclusion of driver distraction in a microsimulation or agent-based model. Distributions of distraction events and inter-distraction periods are derived from eye-glance data collected during naturalistic driving studies. The developed model of distraction is implemented – along with perception errors, visual obstructions, and driver reaction times – in a simulated mid-block pedestrian crossing.

The results of this simulation demonstrate that excluding any of these human factors from the implemented driver model significantly alters conflict rates observed in the simulation. This finding suggests that inclusion of human factors is important in any microsimulation platforms used to analyze pedestrian safety performance.

This article explores what the legal, sociological, and scientific literature tells us about risky behavior and what the law can – and can’t - do about it. The article focuses on cell phone use – and the push to regulate it - as a parable about the limits of the law in regulating two things which Americans love – advanced

This article reviews a transactional model of driver stress and fatigue, and its ergonomic application to designing vehicle systems for `stress-tolerance’. Disturbances of subjective state are controlled by cognitive stress processes of appraisal and coping. Both personality factors and situational stressors may elicit maladaptive patterns of cognition that generate subjective stress symptoms, elicit potentially dangerous coping strategies, and interfere with information-processing and attention to the task at hand. Studies using a driving simulator have explored the behavioural consequences of several qualitatively di. erent forms of `stress’, that can be loosely labelled as anxiety, anger and fatigue. Implications of the model for design are reviewed, focusing on road engineering, in-car systems, and automation of vehicle functions. A transactional analysis focuses on evaluation of the cognitions produced by vehicle systems, problems of distraction and overload, and maintaining active task involvement. The article concludes with guidelines for design to minimize safety problems associated with stress and fatigue.

With a marked increase in advanced driver assistance systems (ADAS) being designed and deployed for cars, there is a logical emergence of studies that critically examine the influence these have on driver behavior and attitudes towards risk and safety. The research question addressed within this paper asks to what extent the level of perceived criticality or risk on the part of drivers influences their acceptance of advanced assistance.

Lane changing of traffic flow is a complicated and significant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic "conscious" in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.

This paper presents a preliminary view of learner driver education in Ireland, comparing and contrasting with other international regions1. In so doing, it discusses accident statistics, learner driver experiences, the Irish system comparing with
other international countries, and future direction of driver education and safety. This study is being done from the perspective of public policy, health and safety, and driver education best practices, to provide safer driving environments for learner drivers and other road users.
Learning to drive in Ireland, requires drivers to pass a theory test, and then obtain` a learner’s permit, which allows learners practice driving whilst supervised. The objective of driver education is to produce safer drivers, typically defined as drivers less likely to involved in road traffic accidents. Driver education programmes have been shown to achieve important objectives related to mobility and independent driving by providing an efficient and effective means for learner drivers to learn how to drive and pass the practical driving test (Mayhew & Simpson, 1996; Williams, 2006). Driver education gives new drivers a stronger foundation of training on which they can build their knowledge and skills. Lessons cover the most important skills, knowledge and behaviours needed to become a safe driver and draw on our knowledge as to the major causes of collisions in Ireland. (RSA.ie)
According to Mayhew, D.R., (2007), it is possible that improved driver education could potentially augment the known safety effectiveness of graduated driver licensing systems (GDL). In particular, there are opportunities to improve driver education so that it achieves its safety objectives, and ensure that programmes in the future complement GDL, and the Essential Driver Training (EDT) programme in Ireland, contributing to overall safety benefits2.
Learner and novice drivers remain a major danger on the road, to themselves, their passengers and other road users, with
study after study showing that young people are far more likely to be involved in a crash than older drivers. Young drivers are
grossly overrepresented in the official accident figures and each statistic represents a tragic waste of life that could be
prevented. Inexperience, youthful bravado and sheer recklessness can all play a part in these accidents. Tough action, and
meaningful reform to better equip learner drivers to handle the dangers of driving is required. We have always approached
the young driver problem primarily from a road safety perspective. The key objective is to improve the safety of learner
drivers3 and provide a safe driving environment in which learners can practice safe driving skills. In primary research
undertaken by the author in 2018, 31% of Advanced Driving Instructors (ADIs) surveyed were of the opinion that EDT did not
provide a safe driving environment for learner drivers4.
Current and future efforts to improve driver education, and better integrate it with GDL programmes, need to be rigorously evaluated to determine what does and does not work to reduce learner/novice driver crashes, and as importantly, to understand why this is the case. Driving provides a terrific sense of freedom, but it’s also potentially the most dangerous action we do daily. Learning to drive isn’t just about passing a test – it’s about learning skills from experience and continually improving and advancing the quality of those skills.

In two experiments we explored the influence of individual differences in working memory capacity (WMC) on hazard perception performance in a simulated driving task. In Experiment 1, we examined the relationship between WMC and hazard perception performance under control and dual task conditions, and self-reported driving behavior. Results revealed significant relationships between WMC, hazard perception performance and self-reported driving behavior. Participants lower in WMC performed poorer in dual task conditions and reported more instances of inattention when driving. In Experiment 2 we explored the gaze behavior of low and high WMC individuals whilst completing the hazard perception test under control and dual task conditions. Results revealed that low-WMC individuals had poorer hazard perception performance under dual task conditions and these performance decrements were mirrored in reductions in mean fixation durations on the hazard. Interestingly, pupillary dilation appears to discriminate between low- and high-WMC individuals and might be a useful index of attention for future research.

The proposed method comprises a computer vision-based system highly applicable in the detection of a possible accident while driving. A stereo depth camera is placed in front of the vehicle at a point through which all crucial viewing angles are captured. The camera will be covering a viewing angle of 180°. This idea proposes an efficient framework for accurate object detection using CNN along with an object tracking algorithm. This system will determine the probability of an accident based on the three parameters, which are: object approaching towards the line of movement, relative distance between the object and vehicle, and relative speed of the object with respect to the vehicle. It will provide a reliable technique to attain an efficient rate of detection with less erroneous indication of an accident. Since it covers 180°, it can accurately detect any object/vehicle coming from either side. By judging the speed of the vehicle and the trajectory of the object, the system will calculate the expected time of collision and in order to avoid the impact, an automatic braking mechanism will take place to cautiously reduce the speed of the vehicle in a calculated manner. In conclusion, this computer vision-based system will be a game-changer in accident detection and will make driving safer, easier, and more efficient.

Kecelakaan lalu lintas adalah suatu peristiwa di jalan yang tidak terduga dan tidak disengaja yang melibatkan kendaraan dengan atau tanpa pengguna jalan lain yang mengakibatkan korban manusia dan atau harta benda (UU. RI. No. 22 Tahun 2009). Studi Epidemiologi Cedera akibat Kecelakaan Lalu lintas di RSUD Dokter Soesilo Kabupaten Tegal menunjukan bahwa korban cedera akibat kecelakaan sebagian besar berumur 16-45 tahun, jenis kelamin laki-laki, jenis kendaraan sepeda motor (Wahyu dalam Notosiswoyo, 2014).
Tingginya angka fatalitas pengendara sepeda motor disebabkan oleh beberapa faktor antara lain pengendara sepeda motor sering kali melakukan manuver yang membahayakan (www.kompas.com diakses pada tanggal 12 Februari 2014 dalam Finiarty, 2014). Hal ini dibuktikan dengan dijumpai para pengemudi sepeda motor menghentikan kendaraannya melewati batas palang pintu. Bahkan, beberapa kali terlihat pengendara yang nekat menerobos meski palang pintu kereta api sudah tertutup (Resmadi, 2014).
Penyebab perilaku agresifitas pengemudi sepeda motor di perlintasan sebidang kereta api antara lain (1) faktor internal dan faktor eksternal dari setiap individu yang berdampak pada tingkat kesadaran hukum, (2) tayangan televisi yang belum digunakan sebagai sarana komunikasi yang baik, dan (3) keinginan seseorang agar cepat sampai tempat tujuan.
Cara mengatasi perilaku agresifitas pengemudi sepeda motor di perlintasan sebidang kereta api antara lain (1) peningkatan kesadaran hukum (2) menyisipkan tayangan-tayangan yang bertemakan keselamatan berlalu lintas, dan (3) manajemen yang baik serta pemilihan rute yang benar