Traffic Psychology Research Articles

텍스트 마이닝을 통한 교통심리학 연구 동향분석

This study analyzed trends in traffic psychology research through topic modeling, presenting research achievements and suggesting new research topics. A total of 140 papers related to traffic psychology were collected, predominantly published in journals affiliated with The Korean Psychological Journal of Culture and Social Issues. To identify trends in research topics, topic modeling was conducted on the Korean abstracts, yielding 10 topics: ‘Drunk Driving’, ‘Psychological Factors’, ‘PTSD’, ‘Elderly Driver’, ‘Traffic Legal Violation’, ‘Driver Characteristics’, ‘Traffic Environment’, ‘Driving Stress’, ‘Traffic Safety Facilities’, and ‘Road Rage’. These topics revealed a diverse range of research fields, with studies in 2005 focusing on the elderly and in 2009 on driver characteristics. Research in traffic psychology has consistently been conducted over the past 30 years, covering various topics aimed at preventing traffic accidents. With the advent of the autonomous driving era and the emerging need for coexistence between humans and technology, this study underscores the importance of research on humanity. It proposes that future research should build upon existing studies to explore the philosophical and ethical aspects related to psychology in the context of autonomous driving.

Effect of Emotionalizing Sounds on the Estimation and Evaluation of Displayed Safety Distances

Musicological and traffic psychology research shows that emotions can be changed by certain tone combinations or sound characteristics and that emotions, in turn, influence our driving behavior. Nevertheless, there are no studies on how a dynamic active sound design could influence driving behavior via changing the emotional state of drivers in certain driving situations. Based on a previous study, emotionalizing sounds, characterized by their capacity to evoke specific emotional responses in individuals, were created and used to investigate their effect on the perception of safety distances in an online study. To test this, participants made statements on the safety distance shown in videos of cars following scenarios combined with emotionalizing sounds. The results show a significant difference in the estimated safety distance for videos combined with sounds invoking positive emotions like light-heartedness vs. sounds invoking negative emotions like feeling threatened. The odds of the safety distance being evaluated as too small compared with appropriate were two to three times higher for some threatening sounds vs. the positive sounds. The results further suggest that threatening sounds influenced participants’ wishes to increase the depicted safety distances. The results show that emotionalizing sounds had effects on the participants, though not all were statistically significant.

A multimodal physiological dataset for driving behaviour analysis

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset’s scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available on our GitHub.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Extended reality in the automotive sector: A bibliometric analysis of publications from 2012 to 2022

The present study aims to present a bibliometric analysis of publications related to “Extended Reality” (XR) in the automotive sector. XR is revolutionizing the industry in all fields, and the automotive is one of the sectors that has had much to gain from this technology and its components (Virtual Reality, Augmented Reality, Mixed Reality). Articles on XR in the automotive field that were published from 2012 to 2022 were retrieved from the Scopus database. Extracted items were analysed in terms of the document type, document language, year of publication, country, authors, affiliations, sources, citations, keywords, and research domains. The open-source tool VOSviewer was used to visualize trends in research on XR applied to automotive. The analyses of 1584 documents revealed that the total number of publications has continually increased over the last 11 years. The country producing most of the articles in this field was Germany, followed by the United States and China. The most productive journal is Transportation Research Part F: Traffic Psychology and Behaviour and the institution that issued most of the articles is Technical University of Munich. From the analysis of author keywords, the prominent research areas currently involving the use of XR technologies in automotive can be highlighted: virtual prototyping, design, manufacturing, sales, training, driver or pedestrian behaviour analysis, and ergonomics. More recently, terms like artificial intelligence and autonomous vehicles have started to be used more frequently in studies in the field. The current study reveals an expanding corpus of literature on XR-based applications for the automotive sector using bibliometric methods. Researchers and stakeholders can use this study as a useful reference to comprehend the big picture and the state-of-the-art in this area.

The Conditionality of the Rule among Motorcyclists

The problem of transportation accidents (in general) depends largely on human factors. In the road sector, traffic psychology studies the behavior of road users. Among the categories of users, motorcyclists is part of the group of “vulnerable road users”, and facts and figures confirm their exposure to risk. The theory of conditionality was developed in order to understand the psychological underpinnings of the relationship to the rule. These works defend the existence of two systems of rules: legal and social. Studies carried out with motorcyclists show the particularity of this group: the pleasure of riding, a particular relationship to speed, conditionality differences according to the type and power of the motorbike. The conditionality of the rule among motorcyclists seems to have the particularity of being strongly anchored in their group identity.

Evaluating emotionalizing effects of active sound designs

Numerous results in the field of musicological research and traffic psychology research show that emotions can be altered by certain sets of pitches or sound characteristics, and emotions on the other hand influence our driving behaviour. Regardless, there is no research on how different artificial vehicle sounds could influence the driving behaviour via emotions. We want to create an active sound design that can alter the driver's emotions to increase traffic safety in certain driving scenarios. To evaluate which harmonic compositions and sound characteristics could be used as stimuli, we first extracted sound characteristics from different music pieces or sounds that have a proven emotionalizing effect, and generated new sounds based on said characteristics that also could work in the context of an active sound design for electrical vehicles. With these sounds, we conducted a subject study with 45 participants to evaluate if people perceive the intended emotions, and further determine the best sounds to use in future subject studies to evaluate their impact on drivers' behaviour. This paper describes the extracted sounds, their characteristics and psychoacoustical properties. Furthermore, it discusses the correlation between the sound properties and the perception of and emotional effect on the subjects.

Aggressive Driving, Rule-Breaking, and Driver Stress in Spanish-speaking countries

Driver Aggression is a phenomenon many have studied in all five continents. It has been the focus of curiosity for all sorts of disciplines, and science has been unable to curb it, much less park it. Driver Stress, Road Aggression and Road Rule-Breaking are crucial elements in traffic psychology. Those who do not respect the rules are a major factor in increased risk and the occurrence of major traffic accidents. Little work has been done on how these phenomena behave in Spanish-speaking countries, much less the development of useful instruments to evaluate them. This study aims to generate two unique scales that can be used in Spanish speaking countries regardless of culture or geography, as well as determining the influence Driver Stress has on Driver Aggression and Rule-Breaking. This study comprises a sample of 1827 drivers from Mexico, Guatemala, Costa Rica, and Spain. Through this paper, a small yet significant relationship was found between Driver Stress, Driver Aggression, and Driver Rule-Breaking. This suggests that personality factors are at play in these relationships in Spanish-speaking countries, and that these variables are an important factor on how drivers behave. This study also delivers two useful tools for evaluating Driver Aggression (LatinCAT) and Driver Rule-Breaking (LatinDANT) that have excellent construct validity for further research in Spanish-speaking countries.

THE IMPACT OF TRAFFIC AS A CAUSE OF PSYCHOLOGICAL BARRIERS AND THE DEVELOPMENT OF PHOBIAS

The theme proposed in this bibliographic review seeks to understand the psychological barriers caused by the impact of traffic on the lives of subjects, contextualizing the role of traffic psychology and how it can help individuals to reframe their experiences in traffic, identifying and treating phobias to restore functional behavior in the field of traffic. In order for the work to reach its objective, it was necessary to use books, articles, monographs, dissertations, among others, configuring a bibliographical research, which made it possible to understand how harmful the emotional scars and sequelae that traffic can leave in individuals and the need for awareness about the psychological damage caused by the behavior of individuals in traffic and the measures that can be taken to prevent and/or treat phobias arising from traffic.

The history of international traffic and transport psychology: How a few created a conference, division, and journal for the many

At the 7th International Conference on Traffic and Transport Psychology (ICTTP), I made a comment that our special issue to come from 10 selected papers could be augmented by one more: a brief history of ICTTP, its home division (Division 13 of the International Association of Applied Psychology, or IAAP), and flagship journal (Transportation Research Part F: Traffic Psychology and Behaviour, or TRF). This paper is the result of that suggestion. What follows is not an empirical article that readers are accustomed to finding in TRF. It is a narrative that introduces readers to a few key individuals and moments in time that were critical in the creation of these three components of the traffic psychology discipline. The history in this paper could not be comprehensive, for as I explored the public record and spoke with or received email communications and documents from 12 individuals who were among the founders or knew critical details about their actions, I discovered there is so much more material worthy of exploration than could be addressed in this one manuscript. Alas, I am limited to introducing our historical organization only as a launching point for readers and me to have a foundation to research more. Readers will learn (and for some be reminded) of the discipline’s international organizational roots. We as a community have benefited in significant ways from these founding members. It is time to give them their due.

Predicting perceived risk of traffic scenes using computer vision

Perceived risk, or subjective risk, is an important concept in the field of traffic psychology and automated driving. In this paper, we investigate whether perceived risk in images of traffic scenes can be predicted from computer vision features that may also be used by automated vehicles (AVs). We conducted an international crowdsourcing study with 1378 participants, who rated the perceived risk of 100 randomly selected dashcam images on German roads. The population-level perceived risk was found to be statistically reliable, with a split-half reliability of 0.98. We used linear regression analysis to predict (r = 0.62) perceived risk from two features obtained with the YOLOv4 computer vision algorithm: the number of people in the scene and the mean size of the bounding boxes surrounding other road users. When the ego-vehicle’s speed was added as a predictor variable, the prediction strength increased to r = 0.75. Interestingly, the sign of the speed prediction was negative, indicating that a higher vehicle speed was associated with a lower perceived risk. This finding aligns with the principle of self-explaining roads. Our results suggest that computer-vision features and vehicle speed contribute to an accurate prediction of population subjective risk, outperforming the ratings provided by individual participants (mean r = 0.41). These findings may have implications for AV development and the modeling of psychological constructs in traffic psychology.

The Conflict Between Pedestrians and Right-turning Vehicles at Signal Intersections from the Psychological Level

With the accelerating urbanization, the urban population growth, road construction by leaps and bounds, the rapid improvement of the road traffic participants formed a fierce contradiction, intersection pedestrians and right turn drivers often conflict, pose a threat to pedestrian traffic safety, also signal the overall operation efficiency and service level has an important influence. The traffic violations of pedestrians and drivers will inevitably be accompanied by a series of psychological activities, and they are dominated by their psychological activities. Therefore, from the perspective of traffic participants’ psychology, it is of great significance to solve the violations of pedestrians and right-turning vehicles from the root cause. Based on psychological theory, survey data, behavioral psychology, traffic psychology, safety psychology, social psychology, criminal psychology and other relevant knowledge, this paper adopts both qualitative and quantitative analysis methods to analyze and study the traffic violations of pedestrians and right-turn vehicle drivers.

Holistic, science-based bus acceleration – Combining simulation, computer vision and psychology to develop measures to prioritise public transport in urban areas

Buses play a particularly crucial role in connecting rural regions with cities. The goal of the approach described in this paper is to combine the three research fields: Traffic simulation, Computer Vision and Traffic Psychology in order to pursue a holistic approach that promises dependable bus acceleration with high acceptance by road users. First simulation results estimate a reduction in bus travel time by up to 59 percent between two bus stops. Moreover, the detection of 78 percent of cars and 73 percent of buses in the early development phase shows that a detection, which fulfills the strict data protection standards by using thermal cameras, can be used to control the bus acceleration. In combination with our approach to empirically validate adaptive bus acceleration measures from a psychological perspective we propose a novel holistic method of science-based bus acceleration.

Rasch analysis of the scores of the difficulties in emotion regulation scale (DERS) in a traffic context

The functional approach of emotion regulation has been mainly used in the clinical context. However, other fields such as traffic psychology have also adopted this perspective, showing interest in how difficulties in emotion regulation might interfere in driving. Various driving styles have been associated with maladaptive emotion regulation. Difficulties in Emotion Regulation Scale (DERS) is the most commonly used instrument to assess ER difficulties and it is often used in the context of driving. Most studies have scored DERS by using procedures of the classical test theory approach instead of advanced psychometrics. In this study, a sample of 318 male drivers aged between 20 and 69 (M age = 41.6 years, SD = 11.0) completed the DERS. The total sample was divided in two halves: drivers with road traffic offences and matched controls. Data analysis was carried out using the Rasch Rating Scale Model (RSM). Results indicated that response categories performed inadequately. Collapsing the 5 original categories into 3 new categories was necessary. To meet the unidimensionality requirement, both the first DERS item and the awareness subscale were removed. Model-data fit was then good enough. Item Separation Reliability (ISR = 0.97) was excellent and Person Separation Reliability (PSR = 0.89) was quite good. Even though requirements of invariant measurement were met, there was low validity evidence for the driving context.

Development and Validation of Psychological Fitness to Drive Scale for Filipinos

AbstractRoad transportation for Filipinos is a daily task that subjects an individual to risks and dangers, leading to damages and deaths annually, this leads to developing and validating a Psychological Fitness to Drive Scale for Filipinos (PFDSF) to assess Filipino drivers’ mental readiness and save lives. Via test development, 154 items were generated after a rigorous process of conceptualization and item generation from pre-survey, interviews, and related literature with English and Filipino test booklets as delivered to 102 participants for pilot testing. Online field testing was delivered due to the COVID-19 pandemic. About 463 samples went through a series of factor analyses to determine the existing factors and retain highly relevant items, resulting in two factors namely, Risky Driving Behavior (RDB) with 50 items (α=0.968), and Responsible and Safe Mobility (RSM) having 22 items (α=0.915), for a total of 72 items. PFDSF was found to be psychometrically sound with validity in its content, construct, factors, and high reliability (α=0.967). Future researchers are invited to use the scale and explore other areas for utility with specific population, age, gender, exploration of a social desirability factor, further strengthening of its criterion, convergent and divergent validity considering computer-assisted online version its localization to different major dialects.Keywords: Psychological fitness to drive, test development and validation, Filipino drivers, road safety and traffic psychology

Development of Mindful Driving Attitudes Scale (MDAS): Validity and Reliability Study

Traffic etiquette can be conceptualized as following the rules without fear of traffic control and punishment, acquiring unwritten behaviors that require mutual understanding in traffic, and making these behaviors a habit. It can be said that education, which is one of the elements that make up traffic safety, is one of the most important tools to shape "traffic etiquette". In this context, the General Directorate of Private Education Institutions of the Ministry of National Education added the "traffic etiquette" course to the Private Driving Courses Education Curriculum in 2015. There is no measurement tool for traffic etiquette in the related literature. The aim of the study is to develop a measurement tool that will enable us to obtain more detailed information about traffic etiquette, and to examine its validity and reliability values. For this purpose, a total of 55 statements that could be scale items were determined, taking into account the main topics covered in the book titled “Traffic Etiquette” and the relevant literature, previously written by Hatipoğlu and Yasak (2016), and these were adapted to Likert type scale format and sample was applied. As a result of the analysis, a four-factor scale (Basic Values, Empathy and Interpersonal Communication, Violations of Rights, and To Warn Each Other) consisting of 33 items was obtained. Cronbach's alpha values of the factors ranged between .60 and .89. In the study, Driver Anger Scale, Driver Behaviors Scale and Interpersonal Relationship Styles Scale were used as concurrent validity criteria. As a result of the correlation analysis, significant and expected relations were found between MDAS and these scales. When the analyzes are evaluated, it can be said that MDAS is a valid and reliable measurement tool that can be used in the fields of traffic psychology and training of driver candidates.

Detection of driver fatigue driving behaviour based on deep learning algorithm from the perspective of traffic psychology

Detection of driver fatigue driving behaviour based on deep learning algorithm from the perspective of traffic psychology

The effect of traffic light spacing and signal congruency on drivers’ responses at urban intersections

Traffic lights are critical in regulating traffic flow and modulate the level of service and road user safety. As suggested by studies conducted with pedestrians, traffic light spacing and signal congruency could also impact a driver's decision-making process. However, requirements related to designing signalized intersections do not always consider the spacing between two consecutive traffic lights or the congruency of the information displayed. Here, using a classic traffic psychology paradigm, we developed a hybrid Go/No-go Flanker PC-based task to explore how traffic light spacing and signal congruency modulate drivers’ decisions in urban intersections. Real images of road intersections were edited to reproduce two specific conditions between traffic lights. Specifically, we manipulated both spacing (short vs. long) and congruency (congruent [e.g., red-red/green-green steady light] vs. incongruent [e.g., red-green/green-red steady light]). We found that incongruent information, displayed on short spacing traffic signals, delayed drivers’ responses without being detrimental to their decision-making processes. The results of this exploratory study could offer guidance to transportation engineers to simplify traffic light information readability and increase drivers’ awareness of traffic conditions and road safety.

Moral judgment, decision times and emotional salience of a new developed set of sacrificial manual driving dilemmas.

The growing interest in the subject of moral judgment in driver and autonomous vehicle behavior highlights the importance of investigating the suitability of sacrificial dilemmas as experimental tools in the context of traffic psychology. To this aim a set of validated sacrificial trolley problems and a new set of trolley-like driving dilemmas were compared through an online survey experiment, providing normative values for rates of participants’ choices; decision times; evaluation of emotional valence and arousal experienced during the decision process; and ratings of the moral acceptability. Results showed that while both sets of dilemmas led to a more frequent selection of utilitarian outcomes, the driving-type dilemmas seemed to enhance faster decisions mainly based on the utilitarian moral code. No further differences were observed between the two sets, confirming the reliability of the moral dilemma tool in the investigation of moral driving behaviors. We suggest that as moral judgments and behaviors become more lifelike, the individual’s moral inclination emerge more automatically and effectively. This new driving-type dilemma set may help researchers who work in traffic psychology and moral decision-making to approach the complex task of developing realistic moral scenarios more easily in the context of autonomous and nonautonomous transportation.