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Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen's d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen's d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds.

The printed circuit board (PCB) is the core control unit of electromechanical equipment. In order to determine the influence of the coupling vibration caused by vehicle-road interaction on the PCB reliability of roadside electromechanical equipment, first, the dynamic load of the vehicle tire is solved by establishing the dynamic model of a vehicle road. Then, the acceleration response data generated by road vibration are obtained by solving the road finite element model. Finally, the power density spectrum of the acceleration response is taken as input excitation, and the deformation response of the PCB under vehicle-road coupling vibration is analyzed. The experimental results show that when the vehicle is driving close to the roadside, the vibration caused by vehicle-road coupling will lead to a large deformation of the PCB, and the deformation value reaches 0.170 mm, which can cause structural damage to the PCB. This shows that the vehicle-road coupling vibration can affect the reliability of the roadside electromechanical equipment; thus, the optimal design of the PCB layout is created. After optimization, the first-order modal frequency of the PCB is increase by 5.4%, which reduces the risk of the components breaking away from the PCB substrate.

Road traffic safety can be ensured by preventing and controlling the potential risks in road traffic operations. The relevant literature was systematically reviewed to identify the research context and status quo in the road traffic operation risk prevention and control field and identify the key study contents needing further research. As research material, the related English and Chinese literature published between 1996 and 2021 (as of 31st December 2021) was obtained through the Web of Science Core Collection and Chinese Science Citation Database. These research materials include 22,403 English and 7876 Chinese papers. Based on the bibliometrics, this study used CiteSpace software to conduct keyword co-occurrence analysis in the field. The results show that the relevant research topics mainly covered the risks of drivers, vehicles, roads, and the traffic environment. In the aspect of driver risks, the studies focused on driving behavior characteristics. In terms of vehicle risks, the related studies were mainly about the vehicle control system, driving assistance system, hazardous material transportation, automated driving technology, safe driving speed, and vehicle collision prediction. For the road risks, the safe driving guarantee of high-risk road sections, driving risks at intersections, and safe road alignment design were the three study hotspots. In terms of traffic environment risks, identifying traffic risk locations and driving safety guarantees under adverse weather conditions were the two main research highlights. Moreover, mathematical modeling was the main method for studying road traffic operation risk. Furthermore, the impact of environmental factors on drivers, the emergency rescue system for road traffic accidents, the connection between automated driving technology and safe driving theory, and the man-machine hybrid traffic flow characteristics are the subjects needing further research.

In order to fully understand the research progress of human factors and traffic safety in curve driving, from the perspective of driver-vehicle-road-environment dynamic traffic system, this paper explored the current research status and development trend of human factors of curve driving, and displayed the development process and structural relationship of human factors research of curve driving by using scientific knowledge map. Through the core collection database of Web of Science, 1408 English literatures related to human factors research of curve driving published from 2012 to 2022 (as of October 1, 2022) were obtained, and the literatures in this field were sorted and analyzed based on the VOSviewer visualization software. The results show that China, Tongji University and Accident Analysis and Prevention are the country, institution and journal with the largest contribution rate in the field of human factors research on curve driving. Co-citation analysis shows that the research contents in this field are divided into 5 clusters: driver's visual characteristics, risk of collision, vehicle dynamics characteristics, the influence of traffic engineering facilities on driving behavior, selection of driving speed. The co-occurrence analysis of keywords shows that the topics of curve geometry design and vehicle dynamics, driving behavior and risk, driving speed and safety, behavior prediction and intervention measures are the current research hotspots in the research field. It is found that the development trend of traffic safety improvement in curves is to construct a continuous, consistent, multi-level visual reference frame conforming to driving expectation through visual guiding technology, and summarizes the technical concept of linear visual guidance. This study can provide a reference for the study of human factors of curve driving. [ABSTRACT FROM AUTHOR]

This paper discusses low-cost approaches capable of ranking traffic intersections for the purpose of signal re-timing. We extracted intersections that are comprised of multiple roads, defined by alphanumeric traffic message channel segment codes per international classification standards. Each of these road segments includes a variety of metrics, including congestion, planning time index, and bottleneck ranking information provided by the Regional Integrated Transportation Information System. Our first approach was to use a ranking formula to calculate intersection rankings using a score between 0 and 10 by considering data for different times of the day and different days of the week, weighting weekdays more heavily than weekends and morning and evening commute times more heavily than other times of day. The second method was to utilize unsupervised machine learning algorithms, primarily k-means clustering, to accomplish the intersection ranking task. We first approach this by checking the performance of basic k-means clustering on our data set. We then explore the ranking problem further by utilizing data provided by traffic professionals in the state of Tennessee. This exploration involves using MATLAB to minimize the mean-squared error of intersection rankings to determine the optimum weights in the ranking formula based on a city's professional data. We then attempted an optimization of our weights via a brute-force search approach to minimize the distance from ranking formula results to the clustering results. All the ranking information was aggregated into an online SQL database hosted by Amazon web services that utilized the PHP scripting language. [ABSTRACT FROM AUTHOR]

A review of 115 studies on Australian local area traffic management (LATM) schemes covers network planning, computer modelling, overall design considerations, the deployment of various traffic control devices, project evaluation and numerous before and after case studies. However, no research has been published about the formulation of LATM policies and the processes involved that were formulated during the 1970s and 1980s and aimed at discouraging non-local through traffic in residential areas, improving road safety, and improving environmental amenity through physical devices. This paper develops a conceptual model of the interactions amongst institutions of government (state and local), organisations (national research institutes and universities), and civil society (the consulting industry, lobby groups and community action groups). The model is implemented through a series of unstructured interviews with key players involved with research and advocacy, capacity building, and state government policy makers that determined: who was responsible for the governance of LATM schemes? What were the respective roles of institutions and organisations in relation to the early formulation of policies and plans, especially issues of authority? Who were the key players in these institutions and organisations? To what extent did external influences of ideas by overseas agents (policy transfer) occur in decision making? A recently implemented LATM scheme (Seven Ways) by Waverley Council describes the latest approaches, including community participation. The conclusions note the importance of a society investing in road research, having universities capable of delivering high-quality professional development programs, and having a consulting industry that is willing to deliver innovative, practical advice to local governments. Suggestions are made about areas for further research [ABSTRACT FROM AUTHOR]

To better understand the research focus and development direction in the field of driving behavior active intervention, thereby laying a scientific foundation for further research, we used the combination of topic words and keywords to retrieve relevant articles from the Core Collection Database of Web of Science (WOS). A total of 578 articles published from 1992 to 2022 were finally obtained. Firstly, the time distribution characteristics, country distribution, institution distribution and main source journal distribution of published articles were explored. Then, by using the CiteSpace and VOSviewer software, cited reference co-citation analysis, keyword co-occurrence analysis and burst detection analysis were carried out respectively to visually explore the knowledge base, research topic, research frontier and development trend of this field. The results indicate that the USA, Australia and China are the three most active countries in the studies of driving behavior active intervention. Accidental Analysis & Prevention, Transportation Research Part F: Traffic Psychology and Behavior, and Journal of Safety Research are widely selected journals for publications related to this field. The research frontiers in the field of driving behavior active intervention focus on: “traffic safety and crashes analysis, as well as enforcement intervention”, “driving risk and education for young drivers”, “information provision and driving behavior”, “workload and situation awareness for automated driving”. It is worth noting that in recent years, “warning system”, “time”, “work load” have become research hotspots in this field. To sum up, by a bibliometric overview of research on driving behavior active intervention over the past thirty years, this paper clarifies the development skeleton of this research field, determines its hot topics and research progress, and provides a reference for the follow-up exploratory scientific research in this field. [ABSTRACT FROM AUTHOR]

Road traffic injuries and crashes are one of the major public concerns contributing to mortality and morbidity figures across the globe. Researchers estimated that around 90% of all causative factors for crashes are attributed to road users of which drivers are the principal controlling elements. Therefore, understanding complex human driver behavior and their possible violations or errors are necessary to control and prevent accident occurrence to a considerable extent. Studies on driver behavior of commercial vehicles such as trucks are scattered widely and scarcely explored hindering the possibility of road safety outcomes. This underscores the need to excavate and synthesize the past studies for an effective understanding of human factors causing truck crashes. In this paper, an attempt has been made to systematically review the pieces of literature and to identify the causative factors affecting truck driver behavior. The trend of studies shows a promising framework for improving truck driver safety on taking care of human factors influencing crashes. Most kinds of literature have cited unsafe driving behaviors as a predominant source of truck crashes. The outcomes of this research can be utilized by transportation firms and stakeholders for identifying the possible lags to develop pragmatic and possible effective preventive measures featuring truck driver safety. [ABSTRACT FROM AUTHOR]

Road traffic safety should be evaluated throughout the entire life-cycle of road design, operation, maintenance, and expansion construction. However, traditional methods for evaluating road traffic safety based on traffic accidents and conflict technology are limited by their inability to account for the complex environmental factors involved. To address this issue, a new road safety evaluation method has emerged that is based on driving behavior. Because drivers' behaviors may vary depending on the driving environment and their personal characteristics, evaluating road safety from the perspective of driver behavior has become a popular research topic. This paper analyzes current research trends and mainstream journals in the field of road safety evaluation of driving behavior. Additionally, it reviews the three most commonly used driving behavior data collection methods, and compares the advantages and disadvantages of each. The paper proposes the main application scenarios of road safety evaluation methods based on driving behavior, such as road design, evaluation of the effects of road appurtenances, and intelligent highways. Furthermore, the paper summarizes a driving behavior index system based on vehicle data, driver's physiological and psychological data, and driver's subjective questionnaire data. A comprehensive evaluation method based on the fusion of each index system is presented in detail. Finally, the paper points out current research problems and the future development direction of the road safety evaluation method based on driving behavior. [ABSTRACT FROM AUTHOR]

Although the existing Driver Behavior Questionnaire (DBQ) covers a wide range of drivers' aberrant behaviors, advances in technology have made some questions out of date. These advancements could lead to human errors while driving, and therefore some items of DBQ need to be updated to reflect the influence of technology on driving behavior such as mobile phone usage while driving. This study aims to modify the widely used DBQ by including "mobile phone usage while driving" items and validate it in Iranian context. The impact of demographic items on each factor scale is also investigated. A shortened DBQ that include drivers' aberrant behaviors and additional questions on mobile phone usage while driving was developed. A total of 298 drivers (168 males and 130 females) between the ages of 18 and 60 participated in this study. Results showed that the mean score of two mobile phone usage items is significantly correlated with violation behaviors. Besides, younger drivers, male drivers, and drivers who were involved in an accident in the past three years behave more aberrant. Statistical analysis shows that drivers who use their mobile phones while driving are more likely to be involved in a traffic crash. Moreover, mobile phone usage while driving decreases significantly by age and males use their mobile phones more than females while driving. [ABSTRACT FROM AUTHOR]

Vehicular driver behaviour is an important factor for vehicular speed and safety at horizontal curves, few studies have attempted to understand this aspect. In this regard, the objective of the present study is to evaluate the vehicular speed characteristics with the effect of driver behaviour at horizontal curves. The study uses real traffic environments where vehicle speed, driver behaviour (lane change or not) and number of vehicles in the opposite lane (approaching vehicles in the opposite lane) data were recorded by video graphic technique. A total of four horizontal curves with different radiuses on two-lane undivided two-way roadways have been studied. Further, the curve characteristics (radius, length of curve, etc.) data were collected from a preliminary survey. The data was analyzed with a multiple linear regression model by considering vehicle speed as the dependent variable and radius of curve, driver lane change behaviour, number of vehicles in the opposite lane, type of vehicle, and shoulder width as independent variables. The study discovered that driver lane change behavior, number of vehicles in the opposite lane, vehicle type in the opposite lane, and shoulder width all have significant effects on vehicle speed characteristics at horizontal curves, in addition to the radius of the curve. These findings are useful for the design of horizontal alignment and evaluating countermeasures for vehicular driver safety at horizontal curves. [ABSTRACT FROM AUTHOR]

Several research studies conducted in North America and Europe have consistently shown that converting signalized intersections into roundabouts leads to safety benefits. These studies have led North American and European transportation agencies to convert hundreds of signalized intersections into roundabouts over the last few decades. Meanwhile, the Emirate of Abu Dhabi, the largest emirate in the United Arab Emirates (UAE), has replaced many of its long-standing roundabouts with signalized intersections (i.e., controlled by traffic lights) despite the fact that no UAE-based scientific study comparing the safety performance of these two intersection types can be found in the literature. Hence, the objectives of this paper are to (i) identify factors that significantly contribute to fatal intersection-related crashes and (ii) assess the in-service safety performance of signalized intersections and roundabouts. It is anticipated that the findings from this research will provide road designers and decision-makers with much needed scientific guidance on which factors contribute to fatal intersection crashes as well as on whether converting existing roundabouts into signalized intersections improves public safety or not in Abu Dhabi. The findings from this research should also benefit neighboring Gulf Cooperation Council (GCC) countries considering their similarities in road design, traffic characteristics, and driving culture. [ABSTRACT FROM AUTHOR]

Autonomous driving decision is a critical component of automatic driving system, which informs and updates the unmanned vehicle of object movements. However, end‐to‐end autonomous driving decision is still a great challenge due to the different scales of traffic target in the wild dynamic traffic scenes. To solve these problems, this paper proposes a novel model with attention mechanism and spatiotemporal features extraction. Specifically, for the important spatial information of traffic targets with scale differences, the spatial dimensions of height H, width W and channel C are independent of each other to build sparse spatial attention map. Moreover, different sparse masks are trained for spatial network by pruning elements of feature maps at the end of each block of backbone, which improves the accuracy of the two subnetworks of spatial network by 2.3% and 3.9%, respectively. Then the extracted spatial information is introduced jointly to the time sequence network with previous speed as input to obtain the vehicle steering angle and speed. Experiments on public virtual datasets show that the prediction accuracy of the model reaches 85.8%. Compared with other state‐of‐the‐art models, our model increased by 4.8% and 2.2%, respectively. [ABSTRACT FROM AUTHOR]