Road User Behaviour Research Articles

A Back-of-Queue Model of a Signal-Controlled Intersection Approach Developed Based on Analysis of Vehicle Driver Behavior

In smart cities, it is expected that transport, communication as well as the movement of people and goods will take place in the shortest possible time while maintaining a high level of safety. In recent years, due to the significant increase in the number of passengers and vehicles on the road and the capacity limitations of transport networks, it has become necessary to use new technologies for intelligent control and traffic management. Intelligent transport systems use advanced technologies in the field of data gathering, information processing, and traffic control to meet current transport needs. To be able to effectively control and manage road traffic, it is necessary to have reliable mathematical models that allow for a faithful representation of the real traffic conditions. Models of this type are usually the basis of complex algorithms used in practice in road traffic control. The application of appropriate models reflecting the behavior of road users contributes to the reduction of congestion, the vehicles travel time on the transport network, fuel consumption and the emissions, which in turn support broadly understood energy savings. The article proposes a model that allows for the estimation of the maximum queue size at the signal-controlled intersection approach (so-called: maximum back-of-queue). This model takes into account the most important traffic characteristics of the vehicles forming this queue. The verification allowed for the conclusion that the proposed model is characterized by high compliance with the actual traffic and road conditions at the intersections with signal controllers located in built-up areas in Poland. The obtained compliance confirms the possibility of using the model for practical applications in calculating the maximum back-of-queue at signal-controlled intersections located in built-up areas in Poland.

What factors influence risk at rail level crossings? A systematic review and synthesis of findings using systems thinking

Collisions between road users (drivers, cyclists, motorcyclists, pedestrians) and trains at rail level crossings (RLXs) remain an intractable issue. This systematic review aimed to determine what is known regarding the factors influencing risk at RLXs, with a focus on understanding the causal relationships across the entire sociotechnical system. A systematic search identified 88 published studies. The studies were categorised according to the type of outcome measured or analysed: (1) rates and severity of crashes; (2) unsafe and non-compliant road user behaviours; and (3) road user risk perception, attitudes and beliefs. Most studies focused on unsafe and/or non-compliant road user behaviour. The factors identified within the studies as influencing risk at RLXs were classified using the Accident Mapping (AcciMap) technique, a systems analysis framework. AcciMap maps the factors that influence behaviour across six system levels ranging from Government to the operating environment. Most of the factors identified within the studies related to physical attributes of the crossing itself, its operation, and the behaviours and attributes of road users. Comparatively fewer systemic factors were identified (i.e. policy and budgeting). Few relationships between factors were identified, indicating that little consideration has been given interactions between factors (i.e. how crossing design influences end-user decision making). A research agenda is proposed based on systems thinking and the use of a ‘many model’ approach to understand and address risk at RLXs.

Driving (and) Attention – Does Directional Illumination Support Driver’s System Supervision When Driving Automatically?

Partially automated driving changes the driving task into supervising the automation with occasionally a necessity to intervene. Due to this changing role, drivers of partially automated vehicles have need for interfaces that help them to supervise system operation, support detection of relevant events, as well as provide cues about system’s intentions. This study assessed to what extent directional illumination supports drivers in gaining mode awareness, i.e. in perceiving and understanding what the automation does and whether counter actions are necessary. An internet survey with video-clips of directional illumination within circumstances representative for partially automated driving measured respondents’ understanding of automation-mode and their understanding of required action. Measurements were compared with results from a base-concept, which provided cues for automation mode, but without directional illumination. Results showed that directional illumination raised perception and understanding of automation-mode noticeable when attention was requested for hazards located in the driver’s side-views. Compared to the base-concept, directional-illumination did not improve driver’s understanding of required actions. Development of explanatory instructions was recommended especially for events when changing road-situations interplayed with behaviour of other road-users. To attract attention to situations outside the vehicle, directional illumination was perceived significantly more suitable than the base-concept. The results of this study contribute to further development of directional illumination as a supporting interface while supervising partially automated driving.

A systematic mapping review of surrogate safety assessment using traffic conflict techniques

Safety assessment of road sections and networks have historically relied on police-reported crash data. These data have several noteworthy and significant shortcomings, including under-reporting, subjectivism, post hoc assessment of crash causes and contributing factors, limited behavioural information, and omitted potential important crash-related factors resulting in an omitted variable bias. Moreover, crashes are relatively rare events and require long observation periods to justify expenditures. The rarity of crashes leads to a moral dilemma—we must wait for sufficient crashes to accrue at a site—some involving injuries and even death—to then justify improvements to prevent crashes. The more quickly the profession can end its reliance on crashes to assess road safety, the better.Surrogate safety assessment methodologies, in contrast, are proactive in design, do not rely on crashes, and require shorter observation timeframes in which to formulate reliable safety assessments. Although surrogate safety assessment methodologies have been developed and assessed over the past 50 years, an overarching and unifying framework does not exist to date. A unifying framework will help to contextualize the role of various methodological developments and begin a productive discussion in the literature about how the various pieces do or should fit together to understand road user risk better.This paper aims to fill this gap by thoroughly mapping traffic conflicts and surrogate safety methodologies. A total of 549 studies were meticulously reviewed to achieve this aim of developing a unifying framework. The resulting framework provides a consolidated and up-to-date summary of surrogate safety assessment methodologies and conflict measures and metrics.Further work is needed to advance surrogate safety methodologies. Critical research needs to include identifying a comprehensive and reliable set of surrogate measures for risk assessment, establishing rigorous relationships between conflicts and crashes, developing ways to capture road user behaviours into surrogate-based safety assessment, and integrating crash severity measures into risk estimation.

Crossing or Not? Context-Based Recognition of Pedestrian Crossing Intention in the Urban Environment

The booming self-driving cars need to understand the behaviors of other road users for better performance. Recognizing pedestrians’ crossing intentions is one of the most critical capabilities of self-driving cars to guarantee safe operations in the urban environment. Some researchers try to predict the future trajectories of pedestrians to avoid a potential collision. Others extract skeleton features from pedestrians to detect specific actions related to the crossing intention. All these methods either neglect the abundant appearance information of pedestrians, or work poorly under severe conditions, e.g., pedestrians standing far away, dim light, and occlusions. In this work, a pedestrian crossing intention recognition (PCIR) framework is proposed to recognize pedestrians’ crossing intention. A module for searching targets of interest is introduced to find the pedestrians who are likely to cross the streets, and perform scene perception simultaneously. An action recognition module utilizes a 3D convolutional neural network (CNN) to automatically extract spatiotemporal features that imply early actions before crossing, like limb movements. A distance encoding module makes full use of the contextual cues, e.g., distances between pedestrians and the oncoming vehicle, local traffic scenes around pedestrians, and vehicle speeds, to improve the recognition accuracy obtained from the action recognition module only. Finally, the PCIR framework fuses these factors to predict pedestrians’ crossing intentions by minimizing a focal loss. Experimental evaluations verify the effectiveness of the proposed method in recognizing pedestrians’ crossing intentions. Comparisons with the skeleton-based methods reveal the robustness of the PCIR framework in the urban environment.

Development of the pedestrian anger expression inventory

Objective There has been extensive research into road user behavior, although how pedestrians express their anger has yet to be explored. This is important given the high vulnerability of pedestrians and the additional risks that angry people often undertake. The present study developed a tool to measure one’s tendency to engage in aggressive acts while walking: The pedestrian anger expression inventory (PAX). Methods The survey consisted of 37 items adapted from the Driving Anger Expression Inventory as well as a subset of items from the Pedestrian Behavior Questionnaire. Ten items from the Trait Anger Scale (TAS) questionnaire were also used to assess the general anger tendencies of individuals. A total of 475 participants from Tehran provided complete responses to a questionnaire administered via paper and pencil. Results A Principal Component Analysis showed a 30-item, 3-factor model describing three ways of expressing anger: 1) Anger Expression-In (internalizing anger), 2) Anger Expression-Out (in the form of aggression), and 3) Adaptive/Constructive Expression (dealing with anger in a constructive way). Hierarchical linear regression showed that trait anger was a significant predictor of pedestrian anger expression, above other demographic variables (age and gender). However, age and gender remained significant predictors of pedestrian anger. As age increased, the tendency to become angry while walking decreased. Male pedestrians were also significantly more likely to express their anger aggressively than female pedestrians. Conclusions In this study, we developed a questionnaire to measure anger expression in pedestrians, highlighting three broad ways pedestrians deal with their anger (internally, externally, or constructively). This questionnaire was used in Iran and therefore, further research is required to validate these tools among different samples and populations.

Особенности информационно-психологического воздействия новостного видеоконтента интернет-телевидения на правосознание и поведение участников дорожного движения

The article analyzes the informational and psychological impact of modern internet television on the basis of the materials taken from news video coverings of the official YouTube channel of the Russian Television and Radio Broadcasting Company. The research work provides the results of online polls and consolidated opinions of traffic police officers who analyzed the aforementioned video materials as part of the study. Parts of the videos are described that have potentially positive informational and psychological effect on the consciousness and behavior of road users and pertaining to the dynamics of traffic law violation events, the offending driver's life circumstances, the significance of correct professional action of traffic patrol officers of the State Road Traffic Safety Inspectorate. Based on the progress made in different sciences and practical domains of human activity, several recommendations have been formulated aiming to develop methodologies for positive informational and psychological effect on road users.

Comparison of Multivariate Regression Models and Artificial Neural Networks for Prediction Highway Traffic Accidents in Spain: A Case Study

In recent years Spain shows the great reduction in the accident rate that has been achieved and the improvement of the behavior of road users, despite this, there is still a need to improve many areas. In 2016 for the first time since the last 13 years, the number of deaths increased by 7% concerning to the previous year. In this paper, analysis, and prediction of road traffic accidents (RTAs) of high accident locations highways in Spain, were undertaken using Artificial Neural Networks (ANNs), which can be used for policymakers, this paper contributes to the area of transportation safety and researchers. ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to forecast future trends.There are many ANN models for predicting the number of accidents on highways that were developed using 4 years of data for accident counts on the Spain freeway roads from 2014 to 2017. The best ANN model was selected for this task and the model variables involved highway sections, years, section length (km), annual average daily traffic (AADT), the average horizontal curve radius, Slope gradient, traffic accidents with the number of heavy vehicles. In the ANN model development, the sigmoid activation function was employed with the Levenberg-Marquardt algorithm and the different number of neurons.The model results indicate the estimated traffic accidents, based on appropriate data are close enough to actual traffic accidents and so are dependable to forecast traffic accidents in Spain. However, it demonstrates that ANNs provide a potentially powerful tool in analyzing and predicting traffic accidents. The performance of the model was in comparison to the multivariate regression model developed for the same purpose. The results prove that the ANN model stronger forecasted model which produced estimates fairly close to forecast future highway traffic accidents with Spanish conditions.

Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction

Although traffic conflict techniques have proven to be effective means for road safety analysis, they still suffer from incomplete conceptualization, observer subjectivity, and high data collection cost. To address these problems, video analysis has been increasingly applied to gain a better understanding of the behaviors of road users based on detailed motion data. However, the motion patterns underlying these data are rarely extracted to study the safety of their interactions. This article presents a vision-based method of traffic conflict detection through learning motion patterns from trajectories, for which an original algorithm was established through clustering and subsequent modeling. Using the extracted path and velocity information, we clustered trajectories hierarchically by applying an improved fuzzy $K$ -means algorithm with a modified Hausdorff distance. Each obtained cluster was taken as a labeled set to determine the structure and train the parameters of a hidden Markov model (HMM) that encoded the spatiotemporal characteristics of the trajectories as motion patterns. Based on the targeted trajectory predictions by the learned HMMs following the conflict development, a probabilistic model was developed to estimate the collision likelihood between vehicles to identify traffic conflicts. The experimental results obtained using actual traffic videos demonstrated the applicability of the algorithms for learning motion patterns and the feasibility of the approach for traffic conflict detection. The predicted trajectories were sufficiently accurate to calculate the collision probability, which was qualified as an indicator for measuring the conflict severity. These findings will have important implications for effective improvements in active road safety.

Ramifications of Roadways Conditions and Traffic Features on Roads Safety : A Safety Perspective for Jalandhar, Punjab

India is in a developing phase and safety on roads is at a premature stage. Accidents are increasing day by day leading to a great loss of life. Some people are disabled, others suffer damage to property and health, and degradation of environment resulting in a situation in India that is at a tocsin stage. Official records show that approximately 1,42,000 persons were killed and approximately 4,80,000 injured in traffic accidents in India in 2014 (NCRB 2015). However, this is underestimated since many accidents are off the records and all accidents are not reported. The high accident rate is due to inadequacy of highways and GT roads which cannot meet traffic demands or some other reasons like vehicular defect, poor road geometry or road user behaviour. These accidents deal a heavy blow to the economy of a country. Hence, road safety is a necessity for reduction in accidents.

A Survey on Predicting Pedestrian Safety based on Physical and Psychological factors of Pedestrians Motion

Walking is the special mode of transportation that comprises 60% of the World’s population. This population includes the pedestrians as the focus of sight, because they are considered as the road users. Understanding the behavior of road users is a difficult task as people have complex mindsets. When people are crossing the road, they have been subjected to various attitude issues and other psychological issues. The prediction of the pedestrian’s motion is very ridiculous, because their motions has to be predicted related to the traffic parameter. A prediction algorithm always uses the past experiences to anticipate the future events. These algorithms use the set of sample data from a sample collected over a period. This makes prediction algorithms more ideal to use within smart environments. So as a part of this study, we have compared various algorithms, for their accurateness of the prediction on pedestrians’ movements. This paper also focuses on the various pedestrian’s data set, pedestrian behavior estimation and the various algorithms used to predict the behavior of the people. As a part of our research on pedestrian safety prediction would be done by providing an efficient prediction algorithm based on their movements, ecological factors, pedestrians’ psychological factors, other outreaches.

On Representation Learning for Road Networks

Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and road segments in road networks. To implement the RLRN framework, we propose a new neural network model, namely Road Network to Vector (RN2Vec), to learn embeddings of intersections and road segments jointly by exploring geo-locality and homogeneity of them, topological structure of the road networks, and moving behaviors of road users. In addition to model design, issues involving data preparation for model training are examined. We evaluate the learned embeddings via extensive experiments on several real-world datasets using different downstream test cases, including node/edge classification and travel time estimation. Experimental results show that the proposed RN2Vec robustly outperforms existing methods, including (i) Feature-based methods : raw features and principal components analysis (PCA); (ii) Network embedding methods : DeepWalk, LINE, and Node2vec; and (iii) Features + Network structure-based methods : network embeddings and PCA, graph convolutional networks, and graph attention networks. RN2Vec significantly outperforms all of them in terms of F1-score in classifying traffic signals (11.96% to 16.86%) and crossings (11.36% to 16.67%) on intersections and in classifying avenue (10.56% to 15.43%) and street (11.54% to 16.07%) on road segments, as well as in terms of Mean Absolute Error in travel time estimation (17.01% to 23.58%).

A new approach for index construction: The case of the road user behavior index

In recent years, composite indicators have become increasingly recognized as a useful tool for performance evaluation, benchmarking, and decision-making by summarizing complex and multidimensional issues. In this study, we focus on the application of data envelopment analysis (DEA) on index construction in the context of road safety and highlight the shortcomings of using the classical DEA models. The DEA method assigns a weight to each indicator by selecting the best set of weights for the unit under evaluation. The flexibility in selecting the weights in the classical DEA approach may lead to two interrelated problems: compensability and unfairness. These shortcomings are, respectively, overcome traditionally by imposing weight restrictions and applying a common weights approach. However, the problem of evaluating a layered hierarchy of indicators with a common set of weights (CSW) has not been addressed in the literature. To fill this gap, we propose a new approach for index construction to determine an optimal CSW to assess all units simultaneously while reflecting the hierarchical structure of the indicators in the model. The applicability of the suggested common-weight approach is illustrated by a case study on constructing a road user behavior index for a set of European countries. From a theoretical point of view, our approach provides a fair and identical basis for evaluation and comparison of countries in terms of driver’s behaviors and, from a practical point of view, it significantly reduces the required computational burden for solving the formulated model. The obtained results clarify the sharper discrimination power of our model compared to the other methods in the literature.

Effect of Culture on Gender Differences in Risky Driver Behavior through Comparative Analysis of 32 Countries

This study examines the effect of culture on gender differences in road user risky behaviors. With the hypothesis that gender differences are not solely because of biological factors, and that the existence and magnitude of differences between gender groups vary according to cultural context, because of differentiated social expectations in relation to gender roles, a secondary analysis was made of the E-Survey of Road Users’ Attitudes (ESRA) 2018 database, comprising 25,459 car drivers (53% male) surveyed by an online questionnaire in 32 countries distributed in eight cultural clusters. The interactions between gender and culture in reported behavior, and personal and social acceptability of four violations were analyzed: drinking and driving, speeding, not wearing a seatbelt, and the use of a cellphone while driving. The results show significant gender differences on risky behaviors and attitudes and complex interactions between gender and culture, with men valuing crash-risk behaviors more than women do in all cultural clusters observed. Interactions between gender and culture are more frequent on declared behaviors and personal acceptability than on perceived social acceptability, and on drinking and driving, and not wearing a seatbelt, more than on speeding and the use of a cellphone while driving. In addition, gender differences are greater in Western countries than in the Global South. These gender differences in road user behaviors, attitudes, and perceptions as results of an interaction between biological and evolutionary factors and cultural and social factors are discussed. These results could be useful to better tailor road safety campaigns and education.

Contested Spaces, Shared Concerns

Roadways are sociocultural spaces constructed for human travel which embody intersections of technology, transportation, and culture. In order to navigate these spaces successfully, autonomous vehicles must be able to respond to the needs and practices of those who use the road. We conducted research on how cyclists, solid waste truck drivers, and crossing guards experience the driving behaviors of other road users, to inform the development of autonomous vehicles. We found that the roadways were contested spaces, with each road user group enacting their own social constructions of the road. Furthermore, the three groups we worked with all felt marginalized by comparison with car drivers, who were ideologically and often physically dominant on the road. This article is based on research for the Nissan Research Center - Silicon Valley, which took place as part of a Design Anthropology course at the University of North Texas.

Evaluation of Most Influential Factors Affecting Road Traffic Accidents in Sidon, Lebanon

Enlargement of the transportation industry in developing countries has had a tremendous impact on the advancement of those countries. This advancement has had both positive and negative impacts on society. Drawbacks primarily include the emission of harmful gases from vehicles, traffic overcrowding, and most importantly, injuries, and deaths by traffic accidents, which have increased significantly. This paper focuses on and investigates the human, vehicle and environmental factors affecting road traffic accidents in Lebanon in order to increase the knowledge surrounding how the elements have impacted the increase in accidents in Lebanon. A quantitative method (questionnaire) was used to collect data to understand the behavior of road users, while the relative importance index (RII) was used to analyze the data. The results illustrate that the top three vehicle-related factors that lead to an increase in the accidents in Lebanon according to the respondents are steering wheel defects with an RII of 3.807, mechanical faults with an RII of 3.489 and 2.934 for lack of vehicles among all the groups. Furthermore, the human factors were recognized to have a slight impact on road traffic accidents with the highest RII of 1.719 for failure to follow driving codes. In conclusion, a country’s development not only depends on economic measure, but it is also determined based on how effective the traffic system infrastructure is and the steps taken to minimize injuries.

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixed-traffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic road-user behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending theprevious work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories.

Bibliometric analysis of road traffic injuries research in the Gulf Cooperation Council region.

Background: Despite governmental interventions, the Gulf Cooperation Council (GCC) region continues to experience higher road traffic crash and fatality rates relative to Western nations. This trend suggests a potential disconnect between Road Traffic Injuries (RTI) research and the mitigation measures put in place. Method: Here, we present an in-depth bibliometric analysis to obtain a comprehensive understanding of RTI research in the GCC region. The Web of Science database was used to search and retrieve the relevant articles during the period of 1981-2019. Results: The volume of RTI research increased from 2015-2019, suggesting an increased focus on traffic safety in the GCC region. Saudi Arabia had the highest RTI research productivity level (126 publications); Bahrain had the lowest (7 publications). Inconsistent with its low publication volume, Hammad Medical Corps of Qatar had the highest citation impact score of 16.33. Global collaboration for RTI research was highest between Saudi Arabia and the United States. The most prevalent publication journal for the region was Accident Analysis and Prevention. The most common keywords were " road traffic accidents" and " road traffic injuries"; terms such as " mobile phones", " pedestrian safety", " pedestrians", and " distracted driving" were least common. In the five most productive GCC nations with respect to RTI research (Saudi Arabia, United Arab Emirates, Qatar, Kuwait, and Oman), researchers tended to publish works related to road traffic safety in traffic safety-oriented journals. Conclusions: The quantity and quality of RTI publications in GCC is insufficient to meet the increasing related public health and economic burden in the region. The trends among publication volumes, citations, and impact were inconsistent. There is a lack of research collaboration among the institutions. Most of the research related to RTI is being conducted by researchers with a medical background. Research focusing on pedestrians, cyclists and road user behavior is also inadequate.

Bibliometric analysis of road traffic injuries research in the Gulf Cooperation Council region

Background: Despite governmental interventions, the Gulf Cooperation Council (GCC) region continues to experience higher road traffic crash and fatality rates relative to Western nations. This trend suggests a potential disconnect between Road Traffic Injuries (RTI) research and the mitigation measures put in place. Method: Here, we present an in-depth bibliometric analysis to obtain a comprehensive understanding of RTI research in the GCC region. The Web of Science database was used to search and retrieve the relevant articles during the period of 1981-2019. Results: The volume of RTI research increased from 2015–2019, suggesting an increased focus on traffic safety in the GCC region. Saudi Arabia had the highest RTI research productivity level (126 publications); Bahrain had the lowest (7 publications). Inconsistent with its low publication volume, Hammad Medical Corps of Qatar had the highest citation impact score of 16.33. Global collaboration for RTI research was highest between Saudi Arabia and the United States. The most prevalent publication journal for the region was Accident Analysis and Prevention. The most common keywords were “ road traffic accidents” and “ road traffic injuries”; terms such as “ mobile phones”, “ pedestrian safety”, “ pedestrians”, and “ distracted driving” were least common. In the five most productive GCC nations with respect to RTI research (Saudi Arabia, United Arab Emirates, Qatar, Kuwait, and Oman), researchers tended to publish works related to road traffic safety in traffic safety-oriented journals. Conclusions: The quantity and quality of RTI publications in GCC is insufficient to meet the increasing related public health and economic burden in the region. The trends among publication volumes, citations, and impact were inconsistent. There is a lack of research collaboration among the institutions. Most of the research related to RTI is being conducted by researchers with a medical background. Research focusing on pedestrians, cyclists and road user behavior is also inadequate.

SAFETY AND SECURITY STUDIES OF RAILWAY LINES WITH PUBLIC ROADS (CASE STUDY OF RAILROAD CROSSING IN THE KENJERAN ROAD OF SURABAYA CITY)

Railways are a mode of transportation that is being liked by the public because of its efficiency. The train has its own road in the form of rails, it doesn’t mean the train doesn’t intersect other transportation. The train crossing is a conflict area for public road users with train. This includes train crossing at Kenjeran street. The kenjeran street itself is a national road that is quite strategic because it is one of the accesses to the Surabaya-Madura toll road. The research this time aims to find out the security aspects that must be improved at the train crossing kenjeran street. Including rider behaviour at the time of the train crossing. The method used for this research is direct observation of the field and also shares questionnaires to find out if a public road user understands regulations while on a train crossing. The research results of many signs or road markers are incomplete so that many road users do not know that in front of the train crossing. Other public road user behaviour is also a lot of offense.