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Background: Excessive car use hampers progress toward tackling global health challenges, including climate and sustainability issues, and is linked to lower physical activity. Local communities have been developing solutions through bike buses. This paper, one of the first on bike buses, explores how a bike bus is perceived by other road and place users. Methods: An interdisciplinary survey instrument with dialogic-style questions was distributed on flyers to road and place users on 1 bike bus route including when the bike bus was passing. Responses were thematically analyzed using analytic framing to identify themes and subthemes. This paper focuses on 2 domains related to physical activity's link to the global health challenges of climate change and sustainability issues of excess car traffic: climate actions and community-level activism. Results: Overall, 172 responses were received from 542 distributed flyers (31.8%). The climate actions domain identified a bike bus as a solution that creates a transport mode shift, reduces emissions, and improves air quality. Themes associated with community-level activism domain include perception that a bike bus is a form of activism or a protest and that it is a disruption to the status quo on the roads. Conclusions: Our findings indicate that this active mobility practice goes beyond the utility of getting children to school and being a way to promote physical activity. A bike bus is seen as local community action that could create a transport mode shift, draw attention to road space allocation, and help tackle global health challenges. [ABSTRACT FROM AUTHOR]

Abstract:Introduction: Mood and emotion variously affect our behaviors. The present study examined the relationship between positive and negative affect and driver behaviors as well as the mediating role of perceived stress and driving anger. Method: A total of 369 drivers (age: M = 35.52, SD = 10.61) participated in the study by completing the Positive and Negative Affect Schedule, the Perceived Stress Scale, the Driver Anger Scale, the Driver Behaviour Questionnaire, and the Positive Driver Behaviour Scale. Results: Six serial mediation analyses revealed multiple mediations explaining violations the most and positive behaviors the least. Both positive and negative affects were associated with greater anger, leading to more violations. Moreover, positive affect was negative and negative affect was positively related to perceived stress, which in turn was related to increased violations. For positive behaviors, positive affect was associated with less perceived stress, which was associated with more positive behaviors. Conclusion: Moods and emotions reveal a tendency to behave in ways that can be dangerous to drivers and other road users. The study was particularly important because it was the first in the literature to examine positive and negative affect on driver behaviors, perceived stress, and driving anger. [ABSTRACT FROM AUTHOR]

The article discusses the design and implementation of an open inquiry-based lesson sequence for teaching the subject of "motion and force" in primary school, specifically targeting fourth-grade students. The study outlines the research phases, activities, and pilot testing conducted to refine the lesson sequence based on feedback from experts and students. The findings suggest that the open inquiry-based approach can effectively facilitate student learning and support meaningful learning in primary school science courses, challenging negative attitudes towards science teaching. The study emphasizes the importance of providing sample lesson sequences to help primary school teachers integrate inquiry-based learning into their classes and encourages further research on open inquiry-based activities for primary school science education. [Extracted from the article]

Driving behavior is a unique driving habit of each driver, and it has a significant impact on road safety. Classifying driving behavior and introducing policies based on the results can reduce the severity of crashes on the road. Roundabouts are particularly interesting because of the interconnected interaction between different road users at the roundabouts, in which different driving behavior is hypothesized. This study investigated driving behavior at roundabouts in a mixed traffic environment using data-driven unsupervised machine learning to classify driving behavior using a data set from three roundabouts in Germany. We used a data set of vehicle kinematics for a group of different vehicles and vulnerable road users (VRUs) at roundabouts and classified them into three categories (i.e., conservative, normal, and aggressive). The results showed that most drivers proceeding through a roundabout can be classified into two driving styles—conservative, and normal—because traffic speeds in roundabouts are relatively lower than at other signalized and unsignalized intersections. The results also showed that about 77% of drivers who interacted with pedestrians or cyclists were classified as conservative drivers, compared with about 42% of drivers who did not interact with pedestrians or cyclists, and about 51% of all drivers. Drivers tend to behave abnormally when they interact with VRUs at roundabouts, which increases the risk of crashes when an intersection is multimodal. The results of this study could help to improve the safety of roads by allowing policymakers to determine effective and suitable safety countermeasures. The results also will be beneficial for advanced driver-assistance systems (ADAS) as the technology is deployed in a mixed traffic environment. [ABSTRACT FROM AUTHOR]

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces. [ABSTRACT FROM AUTHOR]

Predicting the severity of crashes has become a significant issue in research on road accidents. Traffic accident severity prediction is essential for protecting vulnerable road users and preventing traffic accidents. For practitioners to identify significant risk variables and set appropriate countermeasures in place, explainability of the forecast is also essential. Most previous research ignores the severity of property loss caused by traffic accidents and cannot differentiate between different levels of fatalities and property loss severity. Additionally, while an understandable structure of deep neural networks (DNN) is significantly lacking in existing works, understanding traditional systems is quite simple. An inability to use structural data when describing forecasting and the many attempts to incorporate neural networks afflict the absence of hidden layers. We propose a Deep Learning (DL) framework for forecasting traffic crash severity to overcome the accident severity prediction. It has three steps to process. Initially, collected input data are cleaned. Data cleaning is performed in a preprocessing step. We conduct experiments on two datasets, A Countrywide (US) Traffic Accident Dataset and UK Road Accident Dataset. The outcomes of the experiments demonstrate that the proposed technique outperformed other approaches and produced the best accuracy. [ABSTRACT FROM AUTHOR]

Accidents, a persistent concern on the rise over decades, contribute significantly to human fatalities, especially with the growing number of road users and extensive global road construction. A pivotal goal for transportation policymakers is reducing accident numbers and severity. This study focuses on evaluating key parameters influencing traffic safety, specifically pavement conditions and weather elements. Our findings highlight the critical role of pavement friction and roughness in accidents, with rutting during rain and night having a pronounced impact. The influence of pavement conditions on accident safety varies with road level and class. Poor pavement exacerbates single-vehicle crashes on high-speed roads but results in less severe single-vehicle and more severe multi-vehicle crashes on low-speed, low-level roads. Furthermore, the effect of pavement safety differs between two-lane and multi-lane roads. Examining weather conditions, precipitation exhibits a direct, linear relationship with accident frequency, but its effect on severity is variable, depending on multiple parameters. Fog, smoke and accidents before sunrise have heightened effects, while the impact of other climatic factors like wind speed and temperature remains undetermined. Demographic factors also play a role, with middle age identified as the safest, and accidents among older and younger individuals tend to be more severe. Additionally, accidents are more severe among women than men. Finally, the study evaluates the impact of billboards and air pollution, revealing that both contribute to increased accidents. This comprehensive analysis sheds light on the intricate interplay between pavement conditions, weather characteristics, and various demographic factors, providing valuable insights for policymakers and stakeholders in enhancing road safety. [ABSTRACT FROM AUTHOR]

The integration of Autonomous Vehicles (AVs) into our modern society hinges on gaining widespread acceptance from potential road users. To indicate the preparedness of these road users and elucidate their perspectives regarding the use of AVs in future, it is imperative to conduct surveys gauging public acceptance and satisfaction with this emerging mode of transportation. This paper reports the results of a comprehensive questionnaire study involving 1,000 individuals in Gyor City. The survey's primary objective was to assess participants' attitudes and willingness to embrace autonomous vehicles within the city's road networks. The study delved into various socio-demographic factors, such as age, gender, and employment status, while also exploring participants' prior knowledge and opinions regarding the advantages and limitations of AVs. The findings reveal a generally favorable disposition among the public toward the inclusion of AVs in urban traffic, paving the way for the acceptance of mixed traffic patterns. Notably, respondents in younger age groups exhibit greater enthusiasm for incorporating AVs into their daily transportation, whereas individuals aged 65 and above express more reservations, displaying a conservative outlook. Furthermore, participants with prior knowledge and a deeper understanding of AVs exhibit a markedly more positive inclination toward this emerging technology compared to those lacking such familiarity. [ABSTRACT FROM AUTHOR]

Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re‐reroute traffic or implement partial closure. Both options have significant implications for peri‐construction road capacity, traveler costs, and the project duration and cost. This study presents a decision‐making methodology to facilitate the choice between full road closure and partial closure. The presented decision‐making methodology is a bi‐level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower‐level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path‐loyal travelers who do not change their routes from their pre‐construction routes. The bi‐level mixed integer nonlinear model is solved using a reinforcement learning‐based algorithm (the multi‐armed bandit‐guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path‐loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions. [ABSTRACT FROM AUTHOR]

• Cognitive psychology principles are used to explain task switching during driving. • Cognitive overload and underload impair subsequent driving performance. • Attentional switch costs following a takeover response can persist for 5 min. • Suggestions are given for improving the safety of Level 3 automated driving. As the semi-automation of motor vehicles advances, the prevalence of multitasking and task switching while driving has increased. In the next phase, known as conditional automated driving (level 3 automation), drivers will be able to fully engage in distracting tasks, yet they must be prepared to promptly resume control of the vehicle and maintain safe driving if requested to by the vehicle. In such situations, the driver's ability to flawlessly switch between the distracting task and the driving task becomes vitally important. This narrative review discusses conditional automated driving within the framework of cognitive psychology concepts of attention and task switching. Delayed reaction time and deteriorated driving performance are attributed to cognitive overload and switch cost. Factors that contribute to driving switch cost are identified and categorized, and several road safety concerns are raised, including: i) switch cost may last for between 20 s to 5 min; ii) inexperienced drivers may be unable to adequately control the vehicle after resuming manual control; and iii) low- and high-intensity non-driving tasks have a greater impact on takeover performance. To minimise the risk to road safety, suggestions have been provided to vehicle manufacturers, road users and regulatory authorities. [ABSTRACT FROM AUTHOR]

• Automated vehicles require inclusive analysis of justice and safety disparities. • We analyse the acceptance of CAV deployment for drivers and pedestrians. • Individual differences shape CAVs' acceptability to adopt advanced automotive technologies. • Participants support CAVs but emphasise potential harm to vulnerable road users. • Economic barriers: Cost and benefits significant for CAVs' acceptance. As technological development towards connected and automated vehicles (CAVs) continues to rise, addressing the challenges associated with their integration is crucial, especially regarding public acceptance. This study explores the acceptability of CAVs, focusing on their potential role in enhancing safety and justice within the transport system. Semi-structured interviews were conducted with a diverse participant group, including 18 car drivers (aged 20–79, M = 48.3, SD = 18.77) and 12 pedestrians (aged 18–61, M = 36.0, SD = 12.94). Thematic analysis was employed to identify and contextualise factors influencing CAV acceptability, encompassing usefulness, ease of use, effectiveness, affordability, and social acceptability. Results emphasised safety as a top priority for both drivers and pedestrians. Trust and system reliability were also common concerns, varying with participants' roles and transport experiences. Both drivers and pedestrians identified cost and economic benefits as significant barriers to CAV acceptance. Moreover, shared apprehensions about justice in adopting CAVs acknowledged the imperfections inherent in technological advancements. Participants supported CAVs but raised concerns about potential harm to vulnerable road users. Both car drivers and pedestrians expressed concerns that introducing CAVs could exacerbate existing injustices these road users face. This study offers valuable insights into how individual differences influence CAV acceptability, contributing to understanding their preparedness to adopt advanced automotive technologies. [ABSTRACT FROM AUTHOR]

• Cooperative behaviors among road users, such as predicting and compensating for another road user's mistakes, were investigated. • A 12-day traffic observation with stationary mounted cameras was conducted at an urban T-intersection in Germany, where cars with priority appearing from the right encountered cars or bikes without priority coming from the left. • Cyclists were found to disregard the "priority-to-the-right" rule more often than motorists. • Road users with priority are more likely to yield when turning right and arriving at the intersection later. • Road users who disregard the "priority-to-the-right" rule are more likely to stay near the center of the road, while those who yield tend to move closer to the roadside. The integration of autonomous driving into mixed traffic environments poses unique challenges, especially at unsignalized intersections where communication and cooperation with human road users is necessary. This study addresses a critical research gap by investigating the adherence to the "priority-to-the-right" rule at an urban unsignalized intersection, comparing behavioral patterns of cyclists and motorists. Utilizing stationary mounted cameras, a 12-day traffic observation of an urban T-intersection in Braunschweig, Germany, was conducted. The class and the trajectory of road users as well as their scenarios were identified. In 202 cases, cars appearing from the right (ego, with priority) encountered cars or bikes coming from the left (foe, without priority). The study analyzed the impact of variables, ego's direction, foe's class and lateral position and their arrival time on their passing order through descriptive statistics and logistic regression. The findings reveal that cyclists disregard the "priority-to-the-right" rule more often than motorists. Additionally, road users who have the priority are more likely to yield when turning right, arriving at the intersection later, and encountering an opposing road user who is close to the center of the road. This study highlights the importance of implicit communication in traffic and provides essential benchmarks for developing more human-like autonomous driving systems, capable of interpreting and responding to nuanced road user interactions at unsignalized intersections. [ABSTRACT FROM AUTHOR]

• Three- stage least squares (3SLS) estimation was employed to model the data. • Significant associations between VRU's perceived safety and road user characteristics. • Significant associations between VRU's perceived safety and the presence of an eHMI. • No significant associations between VRU's perceived safety and road environment. • eHMIs are achieving their goal of successfully communicating to vulnerable road users. Research has demonstrated the benefits of external human–machine interfaces (eHMIs) in increasing vulnerable road users' (VRU) feeling of safety in interactions with automated vehicles (AVs). However, two key gaps exist in the literature. First, existing studies examined AV-VRU communication aspects in the context of conventional roads with traffic controls, but not for shared spaces where VRU-AV interaction is reliant on communication between the two parties. Second, limited knowledge is available on the differences between cyclists and pedestrians when interacting with AV. This paper aims to address these gaps through an online questionnaire among 254 cyclists and pedestrians in Australia and the UK. Perceived safety was measured in terms of willingness to cross in front of an AV, feeling of security, and feeling of relaxation. Results from a three-stage least square regression analysis identified differences in the factors for pedestrians and cyclists. Pedestrians that were male, over the age of 35, not regular cyclists, or residents of the UK reported lower feelings of safety, relaxation, and willingness to cross than their counterparts. Similar results were found cyclists who are older than 45 years, and UK residents compared to other cyclist participants. Both pedestrians and cyclists reported more willingness to cross and higher feelings of security and relaxation when an eHMI was present. These findings indicate that for effective use and understanding of eHMIs targeted interventions are needed to address the specific concerns of different demographic groups, as identified in this research. By increasing public understanding and acceptance of AVs – as well as eHMIs – across all demographic groups, researchers can promote a smooth integration of these technologies into shared spaces. [ABSTRACT FROM AUTHOR]

• AVs can be a suitable mobility option for unaccompanied child transportation for a majority of parents in our study. • Parents wanted to train their children and gain first-hand experience with the autoELF vehicle before allowing them to use the autoELF vehicle unaccompanied. • Parents would like to replace most of the previously accompanied car journeys to the children's leisure activities with the autoELF vehicle. • Only two parents cited school commuting as a use case for the autoELF vehicle and AVs in general. • Parents cited self-responsibility and the ability to regulate emotions as the two most important skills for children to be able to use the autoELF vehicle unaccompanied. Driverless or autonomous vehicles (AVs) have the potential to address children's mobility disadvantage by enabling them to become more independent from their parents and other adult drivers before they reach the legal age for obtaining a driver's license. In an online contextual interview study, we interviewed N =22 parents of underage children from Germany to investigate their willingness to use AVs for unaccompanied transportation of their children. The goal of the interview study was to investigate whether AVs are a suitable option to support unaccompanied transportation of children from the parents' perspective and how these AVs should be designed considering the parents' concerns and needs. In contrast to former acceptance studies, we familiarized the participants with an existing AV concept called autoELF. We created a user scenario to enable the parents to better imagine the situation. In the study, parents first described their children's current mobility in a normal week. Parents were then asked about the expected benefits, concerns, child-related prerequisites and technical requirements for using AVs, as well as the possibilities for integrating AVs into their family's mobility. Results showed that AVs can be a solution to provide children with unaccompanied transportation. In contrast to previous studies, the majority of parents interviewed in this study were willing to use the AV for the transportation of unaccompanied children, but only after parents had gained initial experience with the vehicle and trained their children in its use. Regarding the unaccompanied use by their children, parents based their consent to use the AV on their children's emotional and cognitive abilities rather than on their age. In their children's daily mobility, parents intended to replace most of the previously accompanied car journeys during leisure time with the autoELF vehicle without compromising their children's active mobility, such as walking or bicycling. In contrast to previous literature, only a few parents cited school commuting as a use case for the autoELF vehicle. Our qualitative interview study highlights the potential of AVs for unaccompanied child transportation. Moreover, it stresses the importance to use specific, tangible concept when investigating family AVs. In the next phase of the research project, a physical prototype of the autoELF vehicle was tested with children and older adults as the primary user groups. [ABSTRACT FROM AUTHOR]

• Link drivers and pedestrians in real-time distributed simulation. • Present driver's bimodal braking strategies. • Identify lateral movements as implicit cues in road user interactions. • Showcase pedestrians' behavioural adaptations to kinematic cues. • Highlight decisions of both actors shaped by time gaps and road infrastructure. As we move towards a future with Automated Vehicles (AVs) incorporated in the current traffic system, it is crucial to understand driver-pedestrian interaction, in order to enhance AV design and optimization. Previous research in this area, which has primarily used naturalistic observations or single-actor virtual reality simulations, has been limited by its inability to draw causal conclusions, also due to a lack of real human–human interactions. Our study addresses these limitations by employing a high-fidelity distributed simulation setup that links drivers in a motion-based simulator with pedestrians in a CAVE-based environment. This method allows for the examination of real-time and reciprocal interactions across a range of road-crossing scenarios. Using thirty-two pairs of drivers and pedestrians, we investigated how different factors, such as the presence of zebra crossings and varying time gaps of the approaching vehicle, influence driver behaviour and pedestrian crossing decisions. The effect of drivers' control of the vehicle during such crossings (e.g., braking behaviour and lateral deviation) on pedestrians' crossing decisions were also analysed. We found that the distribution of drivers' average deceleration values were bimodal, where drivers either markedly yielded to pedestrians, or continued in their path, with very few instances of intermediate behaviour. We also found that pedestrian decisions were seemingly influenced by the different braking strategies adopted by the driver, with pedestrians crossing before the vehicles in response to soft and early, or late and hard braking, while late and soft braking often resulted in the vehicle passing first. We also observed a slight lateral movement of the vehicle away from pedestrians when drivers were not yielding, but more of a lateral deviation towards them when yielding. This may be because drivers subconsciously transfer their walking interaction habits to their driving behaviour, to avoid a collision with pedestrians. Finally, our results showed a stronger influence of these kinematic cues on pedestrian crossing decisions, when compared to zebra crossings. As well as highlighting the value of a novel approach for investigating vehicle–pedestrian interactions, this study illustrates how vehicle cues can assist pedestrian decisions, adding new knowledge in the development of human-like behaviour for future AVs. [ABSTRACT FROM AUTHOR]

Large numbers of vulnerable road users were killed in truck crashes. In this study, ensemble machine learning models are constructed to predict the injury severity of the vulnerable road user (VRU) to truck (VRU-T) crashes. The study is based on the five years (2017–2021) of VRU-T crash data in the Shandong Province from the Center for Accident Research in Zibo. The injury severity of VRUs is estimated using machine learning ensemble models- Stacking, Voting, Random Forest, and eXtreme Gradient Boosting (XGBoost). Compared to the other three models, the Stacking has excellent predictive performance on the pedestrian and non-motorized datasets. Then, SHapley Additive exPlanations and Partial Dependence Plot box are introduced to analyze risk factors qualitatively and quantitatively. The innovative findings of this study are as follows: (1) as VRUs age, they are more likely to be seriously injured in truck crashes; (2) middle-aged truck drivers and truck drivers with medium driving experience increase the probability of VRUs being severe and fatally injured in truck crashes; (3) crashes involving heavy trucks, under signalized crossing, or on the national and provincial road and urban road have a positive effect on the crash severity for cyclists, and E-Bike riders. [ABSTRACT FROM AUTHOR]

Accurate detection plays a critical role in improving the safety situation of vulnerable road users. This study extends infrastructure-based LiDAR application to all three major vulnerable road user groups including pedestrians, cyclists, and wheelchair users. Two critical problems for accurate detection of small-sized road users are scanning angle variability and feature fluctuation. To address these issues, a feature-based classification method combined with prior LiDAR trajectory information is developed. Effective dimension-related features are proposed and five classifiers including artificial neural network (ANN), random forest (RF), adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and long short-term memory (LSTM) are tested with a novel feature engineering process. A total of seven features are selected from the point cloud of clusters for vehicle/pedestrian/cyclist/wheelchair classification. By updating these significant features based on prior information of the entire trajectory, the performance of road user classification (imbalanced datasets) has been significantly improved. Experimental study is conducted to examine the recall rate, F1-score, and AUC of vehicles, pedestrians, cyclists, and wheelchairs before and after integration with prior trajectory information. The result shows the trained AdaBoost, RUSBoost, and LSTM classifiers with prior trajectory information can achieve recall/F1-score/AUC: (1) Low traffic volumes – vehicles (100%/99.96%/99.96%), pedestrians (99.96%/99.96%/99.97%), cyclists (99.74%/99.45%/99.67%), and wheelchairs (99.22%/99.68%/99.01%) and (2) Moderate traffic volumes – vehicles (99.39%/99.44%/99.69%), pedestrians (98.33%/97.99%/98.64%), and cyclists (95.41%/94.29%/94.40%), using 32-laser LiDAR sensors (10 Hz). [ABSTRACT FROM AUTHOR]

In recent years, rapid advancements in the Autonomous Vehicles (AVs) industry have greatly motivated the research and development in pedestrian trajectory prediction and risk assessment. One of the critical requirements for AVs is to predict the future trajectories of pedestrians and provide collision warnings in an accurate and prompt manner. Nevertheless, accurate prediction of pedestrian trajectories remains a technical challenge, mainly caused by the heterogeneity of pedestrian crossing behavior and uncertainties in vehicle-pedestrian interactions. This paper proposes a deep learning-based method for pedestrian trajectory prediction and risk assessment, using trajectory data extracted from roadside LiDAR data and corresponding signal phasing information at MLK and Georgia Avenue in Chattanooga, TN. Meanwhile, a set of criteria referred to as the risk factor is established to quantitatively evaluate the risk of the pedestrian crossing behavior, which also serves as a learnable feature. A Long Short-Term Memory (LSTM) network is proposed, which takes the following data as the input: the pedestrian trajectory data, signal phasing data, and risk factors from the past 10 steps. Meanwhile, the network predicts the pedestrian trajectory and risk factor at the future time step. In the experimental study, the root-mean-square errors between the predicted and ground truth x and y coordinates are 0.225 meters and 0.377 meters, respectively, and the F1 score value for the risk factor is 99.6%, demonstrating the efficacy of the proposed LSTM-based methodology on pedestrian trajectory prediction and risk assessment. [ABSTRACT FROM AUTHOR]

Truck collisions with vulnerable road users (VRUs) are infrequent, but often severe or fatal. While research has investigated factors contributing to safety and collisions, a synthesis of human-related contributors remains missing. This scoping review follows PRISMA guidelines to synthesise studies on the effects of human factors on truck-VRU safety. With a focus on human factors related to pedestrians, cyclists, and truck drivers, five scientific databases were searched and 3,414 records were screened. Twenty-four articles met the inclusion criteria. Most were published after 2015, indicating a limited but growing focus on human factors affecting truck-VRU safety. Half of the studies analysed police collision reports, while others used qualitative data collection methods, like questionnaires and focus groups, or human subject experiments. Compared to passenger-vehicle literature, notably fewer behavioural studies were identified, highlighting a need for further behavioural human factors studies of truck-VRU interactions. Sociodemographic and vision-related factors were most frequently investigated and found to significantly affect collision occurrence and severity. Driving and cycling experience and training, and road user distractions were examined less, albeit being important. This review bridges a literature gap by focusing on human characteristics, states, decisions, and errors affecting truck-VRU safety, offering insights for road-user-centred mitigation strategies. [ABSTRACT FROM AUTHOR]

Overtaking is a complicated maneuver that has implications for the operation and safety of road users on two-lane undivided roads. Understanding the factors influencing the overtaking behavior and process of human drivers becomes essential in the wake of developments in driver awareness, vehicular capabilities, and roadway design. This review classifies the factors which govern the overtaking process of drivers into five categories: vehicle, road, traffic, environment, and driver. Post classification, the effect of each factor based on empirical evidence is discussed on the overtaking behavior of drivers. Based on the analysis, a potential framework is suggested to understand the overtaking process better. Furthermore, future research directions are discussed in the context of modeling approaches, generalizability, connected and automated vehicles, and data collection efforts. [ABSTRACT FROM AUTHOR]

Preparing the Manual on Uniform Traffic Control Devices (MUTCD) involves gathering inputs from various public and the National Committee on Uniform Traffic Control Devices (NCUTCD) sponsoring agencies. The NCUTCD assists in developing standards, guidelines, and warrants for traffic control devices and practices employed to regulate, warn, and guide traffic flow on roadways. This organization advises the Federal Highway Administration (FHWA) and other relevant agencies on suggested updates and interpretations to the MUTCD and other nationally recognized standards. Examples of such sponsoring agencies include AASHTO, ASCE, Institute of Transportation Engineers (ITE), American Highway Users Alliance (AHUA), National Association of City Transportation Officials (NACTO), National Association of County Engineers (NACE), International Bridge, Tunnel and Turnpike Association (IBTTA), and American Traffic Safety Services Association (ATSSA). Other national and regional agencies have also been invited to provide feedback on the draft manual. Although all comments are considered valuable, those from federal agencies may hold more weight due to their expertise in specific subject matters. This study utilized text-mining techniques to analyze the comments from the sponsoring agencies, aiming to identify commonalities and distinct features in their feedback. The main question addressed was whether each agency effectively represented its interests and if there were shared interests among them. The study also explored the suggestions, questions, and recommendations raised by each agency. The findings revealed that some concerns were shared among agencies, although in most cases, they represented individual interests. Specifically, some agencies shared a common interest in speed limits, whereas others had similar topics concerning signs and markings. Other shared interests are bike lanes, 85th percentile speed, and pedestrian- and bus lane–related topics. This study's insights into the interests and concerns of various MUTCD sponsoring agencies can aid in creating more effective, engaging, collaborative, and well-rounded technical documents. As a limitation, this study only used the current MUTCD revision comments, and hence the results reflect only recent concerns from the agencies. Practical Applications: The Federal Highway Administration collects comments from both the general public and NCUTCD sponsoring agencies for modifying the MUTCD. This study used the comments from the NCUTCD sponsoring agencies submitted by May 2021 and conducted supervised and unsupervised text mining analysis to understand any available patterns, differences, or commonalities in the agencies' concerns in shaping the MUTCD. The findings indicate the presence of various shared interests among the agencies and some specific features that were specific to the agencies. In addition, it was observed that the majority of the agencies have a great concern for the safety and well-being of society by showing a common interest in safety influencing factors such as speed, road signs, and the definition and use of the 85th percentile speed. This understanding can foster greater collaboration among the agencies, leading to more well-rounded and comprehensive guidelines. [ABSTRACT FROM AUTHOR]

Objective: In 2021; half of crash fatalities occurred at night when some road users, like pedestrians, are particularly vulnerable. Automatic emergency braking (AEB) systems can avoid or mitigate collisions by automatically applying the brakes, but their performance may be hindered in low lighting. The purpose of this study was to estimate the proportion of real-world crashes where headlights could provide enough visibility for the driver or AEB system to detect and avoid the collision. Methods: This study used IIHS headlight testing data and Crash Report Sampling System crash data from 2016 to 2021. The median 5-lux visibility distance was calculated for each IIHS headlight rating: good, acceptable, marginal, and poor. For three response profiles, a typical human driver, a fast human driver, and a camera-based AEB system, the maximum travel speed in which a vehicle could come to a complete stop within the 5-lux distance was computed. The real-world crash coverage of each headlight system and response profile was defined as the proportion of crashes that occurred on dark, unlit roads with a speed limit below the estimated maximum travel speed, where the vehicle could stop within the visibility distance. Results: The median low-beam visibility distance for poor-rated headlights was 68.2 m and for good-rated headlights was 103.1 m on the right side of straight roads. The typical human driver could have enough time to detect and avoid 46.2% of nighttime pedestrian crashes with poor-rated headlights and 70.1% with good-rated headlights. The visibility distance greatly increases with high beams and would allow the typical human driver to detect and avoid over 90% of nighttime crashes. Good-rated and acceptable-rated headlights allow AEB systems to theoretically have enough visibility distance to avoid nearly every nighttime crash. Conclusions: Vehicles with good- or acceptable-rated headlights and active high beams are theoretically capable of providing sufficient light for speeds up to 165.8 km/h (103 mph), even covering most speeding vehicles. Future simulations of AEB systems on vehicles equipped with good- or acceptable-rated headlights and automatic high beams may not need to consider headlight visibility. [ABSTRACT FROM AUTHOR]

Objective: Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data. Methods: From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists). Data consisted of anonymized video and sensor data (Global Positioning System and accelerometer) from vehicles equipped with Nexar dash cameras. Each event was qualitatively evaluated to assess the collision geometries and other extrinsic collision factors (e.g., time of day, roadway location, presence of occlusions). Previously published injury risk models were utilized to provide estimates of Maximum Abbreviated Injury Scale MAIS2+ and MAIS3+ injury potential. Collision severity distributions and aggregated injury risk estimates were compared. Results: Vehicle speeds at the time of impact ranged from 0 to 39 mph, and VRU speeds ranged from 0 to 27 mph. In general, vehicles were traveling at lower speeds at the time of impact when turning in comparison to straight travel. Nearly all events (95%) were associated with MAIS3+ injury risk estimates below 10%. Collisions involving a potential occlusion or the vehicle responding to surprising behavior by the VRU were associated with higher estimates of injury risk than those without occlusion or with the vehicle initiating the conflict. Based on accumulated risk for each event, it can be estimated that approximately 55 persons could be moderately injured (MAIS2+) and approximately 6 persons could be seriously injured (MAIS3+). Conclusion: These data indicate that responding to surprising VRU behavior, having visibility be potentially occluded, and vehicle travel behavior were associated with differences in collision speed and injury risk estimation. The specific comparisons made in this study are not intended to be comprehensive but serve as a starting point for considering baseline driving risk associated with VRU collisions in dense urban areas. [ABSTRACT FROM AUTHOR]

Numerous attempts have been made to create a secure system that meets the criteria and requirements of the automotive vehicle development life cycle. However, a critical gap exists in the secure development lifecycle, particularly concerning the development and maintenance of software after the vehicle has been sold by the manufacturer. This step is often overlooked by original equipment manufacturers (OEMs), especially after the expiration of the vehicle warranty period, given the cost that it will require to update and test the software in their vehicles. This paper addresses the issues that affect current and future vehicle cybersecurity, during the maintenance of cybersecurity, and how the neglect of it could end up creating hazards for the vehicle owner or other road users. To accomplish this, we will employ the technology adoption model (TAM) as a theoretical framework, which is used to understand and predict how organizations adopt technology. Thus, through qualitative and quantitative research, including text mining, we identify the challenges in the adoption and diffusion of cybersecurity maintenance in the automotive sector and its supply chain. In addition, we propose possible solutions on how to maintain a level of security that will benefit road users, OEMs and regulators, covering the cybersecurity needs for the vehicle's usable life, taking into account the vehicle's heterogeneity of components and technology, connectivity, environmental impact and cost of production and maintenance of a vehicle. [ABSTRACT FROM AUTHOR]

Introduction: To avoid the uncertainty present in a cooperation between manual vehicles (MVs) and automated vehicles (AVs), cooperative connected automated mobility (CCAM) is frequently proposed. In this contribution, we consider the situation of a motorway slip road where an AV (SAE level 3 or higher) on the motorway cooperates by opening a suitable gap for an entering AV. To ensure a meeting of the cooperation partners, speed adjustment by the system might often be necessary. Methods: To investigate the acceptance of an automated cooperative motorway access assistant a driving simulator study (N = 41) was conducted. Participants drove six laps on a virtual test track in a level 3 AV. Three levels of speed adjustment and two levels of an HMI (with or without HMI) were tested. They experienced the automated cooperative motorway access assistant in two situations. Firstly, when they entered the motorway themselves and benefited from the system, and secondly, when the system reduced their vehicle's speed to allow for cooperation with another vehicle on a motorway slip road ahead. Results: The results show that the acceptance of the assistant depends significantly on the magnitude of speed reduction: the greater the speed adjustment, the worse the user rating, and the more often the cooperation was terminated. Obviously, the acceptance depends on the perspective: If the users themselves benefit from the speed adjustment of others, they rate the system significantly better than if they themselves must accept the restriction. Only in the case of a small speed reduction, the assistant is rated as well as if the user benefits from the system. The availability of a human-machine interface (HMI) providing information about the system status influenced acceptance only marginally but had a significant influence on the trust in the automation. Discussion: The results suggest that people are willing to accept a personal disadvantage in order to give others an advantage in the context of CCAM. However, acceptance depends on the magnitude of the disadvantage. The overall agreement scores of the system with HMI did not differ from those without HMI, although the HMI was found to slightly reduce the termination of cooperation. Three possible reasons for this result, which differs from previous research, are discussed. [ABSTRACT FROM AUTHOR]

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy. [ABSTRACT FROM AUTHOR]

This study is a significant endeavor involving the development and testing of a comprehensive methodology to incorporate driving behavior into the analysis and prediction of vehicle fuel consumption. It underscores the crucial importance of understanding how different driving behavior affect fuel efficiency. The framework we present is a theoretical construct and a practical tool. It provides a robust, multi-step process for linking driving behavior to fuel consumption, leveraging both traditional statistical methods and advanced machine learning techniques to derive actionable insights. To test the framework, we used a naturalistic data that includes about 5408 different road users in a mixed traffic environment and urban settings in Germany. We applied a microscopic fuel consumption model to calibrate the framework and an unsupervised clustering algorithm to classify the behavior of the driver interacting with each other and with vulnerable road users. The framework includes developing Linear regression model as a baseline, which yields an R-squared of 0.511 and a Mean Squared Error (MSE) of 0.031, indicating moderate predictive accuracy. The final step includes choosing Random Forest as a better model, which achieves a higher R-squared of 0.956 and a lower MSE of 0.003. We also found that conservative and aggressive driving leads to significantly higher and more discrepancy in fuel consumption than normal driving behavior. These insights can promote more efficient driving practices, leading to significant fuel savings and environmental benefits. Article highlights: Driving behavior significantly impacts fuel consumption; smooth, controlled driving leads to greater fuel efficiency. Insights from this study can inform ADAS development, enabling real-time, fuel-efficient driving assistance systems. Promoting normal driving habits can reduce fuel use, aiding eco-friendly driving and sustainable transport solutions. [ABSTRACT FROM AUTHOR]

Good visibility of lane markings is important for all road users, particularly autonomous vehicles. In general, nighttime retroreflectivity is one of the most challenging marking visibility characteristics for agencies to monitor and maintain, particularly in cold weather climates where agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in cold weather climates with routine snowplow activity. Recently, transportation agencies in cold weather climates have begun deploying improved recessed, durable pavement markings that can last several years and have very high retroreflective properties. Several dozen installations may occur in a state in any calendar year, presenting a challenge for states that need to program annual repainting of traditional waterborne paint lines, but not paint over the much more costly durable markings. This study reports on the utilization of mobile mapping LiDAR systems to classify and evaluate pavement markings along a 73-mile section of westbound I-74 in Indiana. LiDAR intensity data can be used to classify pavement markings as either tape or non-tape and then identify areas of tape markings that need maintenance. RGB images collected during LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be used by agencies to develop accurate pavement marking inventories to ensure that only painted lines (or segments with missing tape) are repainted during annual maintenance. Repeated tests can also track the marking intensity over time, allowing agencies to better understand material lifecycles. [ABSTRACT FROM AUTHOR]

Road accidents cause considerable losses to road users and to society. The steady increase in the number of vehicles leads to increased traffic volume. Therefore, there is a real need to improve passenger safety by developing passive safety systems. This article presents the results of experimental tests of structures absorbing kinetic energy, which could be used in the front section of a vehicle in order to reduce the consequences of passenger car head-on collisions. A number of crash tests of selected structures were conducted under various load conditions. An analysis was carried out of parameters enabling the authors to assess the level of energy absorption by the absorbers made, and compare these to absorbers available on the market. The tests carried out made it possible to determine energy absorption capability of the crash boxes prepared and to identify a structure exhibiting the most advantageous properties from the point of view of its prospective use. Of all of the absorbers analysed, in the context of energy absorption, it was the absorber made of glass-fibre-reinforced polyphenylene sulphide that produced the most advantageous results. Nonetheless, favourable results were obtained for all of the structures tested. [ABSTRACT FROM AUTHOR]

This research presents findings from laboratory experiments on a novel method for identifying and differentiating objects using radar signatures and a specialized convolutional neural network architecture. Previously introduced by the authors, this method has been validated through real-world measurements in an urban environment with a 24 GHz frequency-modulated continuous-wave radar. This study describes how radar signatures, generated in the MATLAB (R2023b) environment from I and Q signals captured by the uRAD USB v1.2 radar, were processed. A database of radar signatures for pedestrians, cyclists, and vehicles was created, and a tailored convolutional neural network was trained. The developed solution achieves an accuracy of over 95% in distinguishing between various objects. The simulation results and successful tests support the application of this system across various sectors. The key innovations include distinguishing multiple objects from a single radar signature, a custom architecture for the convolutional neural network, and an application that processes radar data to produce near-real-time recognition results. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the study is to identify the concept of transport infrastructure and to evaluate the impact of changes in road transport infrastructure on road traffic safety. Design/methodology/approach: The study comprises a theoretical part, which encompasses the concept of transport infrastructure and its importance for the proper operation of state’s economy and a research part, which contains an analysis of survey results conducted among the road users – residents of the Lubelskie Voivodeship. Findings: Road infrastructure facilitates the movement of people across the globe. Over recent years, road transport infrastructure has developed noticeably, which may also have an impact on the economic development of Poland. Road users rated the technical condition of road infrastructure as rather satisfactory and average. Similarly, aspects of traveling by car or other vehicles on public roads have improved over the past years. According to the respondents, road transport infrastructure affects road traffic safety. Consequences of inadequate infrastructure conditions may include increased traffic intensity on certain sections, likelihood of road or vehicle damage, accidents and collisions, increased travel costs, extended travel time and routes, and changes in designated routes that are burdensome, even significantly so, for road users. Research limitations/implications: Future research on transport infrastructure could be expanded to encompass other branches of transport, such as rail, air, sea, and inland water transport, and a comparison of transport infrastructure in Poland with that of other European and world countries. Social implications: It can be inferred that the development of transport infrastructure will enhance citizens' quality of life by increasing road safety. Originality/value: From the perspective of the study's value, the topic of changes in transport infrastructure and road traffic safety is vital and relevant, as many people use roads daily, and their safety is crucial for both individuals and the state. [ABSTRACT FROM AUTHOR]

Motorcycle crashes remain a significant public safety concern, requiring diverse analytical approaches to inform countermeasures. This study uses machine learning to analyze injury severity in crashes in Thailand from 2018 to 2020. Traditional and advanced models, including including random forest (RF), support vector machine (SVM), deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), and eXtreme gradient boosting (XGBoost), were compared. Hyperparameter tuning via GridSearchCV optimized performance. XGBoost, with a tradeoff score of 105.65%, outperformed other models in predicting severe and fatal injuries. SHapley Additive exPlanations (SHAPs) identified significant risk factors including speeding, drunk driving, two-lane roads, unlit conditions, head-on and truck collisions, and nighttime crashes. Conversely, factors such as barrier medians, flashing traffic signals, sideswipes, rear-end crashes, and wet roads were associated with reduced severity. These findings suggest opportunities for integrated infrastructure improvements and expanded rider training and education programs to address behavioral risks. [ABSTRACT FROM AUTHOR]

Random traffic flows and the influence of traffic signals at intersections often cause significant variations in travel times. This has implications for signal control planning and design. This study aims to compare the variability between signalized and unsignalized corridors in Lahore and identify the challenges faced by road users. The study was conducted through a literature survey that collected data on trip purpose, frequency, travel time, and transportation mode choice from various sources. Results show that signalized corridors have lower accident rates and shorter travel times compared to unsignalized corridors. Unsignaled corridors, on the other hand, offer better access and lower fuel costs. Although signalized corridors are more effective in managing traffic flow and improving safety, further research is needed to explore alternative solutions such as roundabouts that can improve mobility and sustainability. The results of this study can serve as a reference for transportation planners and policy makers in designing a more efficient traffic system. This study also suggests the need for further evaluation of traffic control in various urban contexts to optimize safety and efficiency. [ABSTRACT FROM AUTHOR]

The pedestrians are the most exposed amongest all road traffic users to the possibility of being attacked or harmed, either physically or emotionally. In the present investigational study, an attempt is made to develop the model on pedestrian road crossing behaviour by considering the elements of pedestrian crossing behaviour, especially the size of the vehicular gap accepted by pedestrians and also pedestrians decision to cross or not cross the road. A field investigation was carried out at two zebra crossing facilities located at two major roundabouts in Shivamogga city. Data collection and extraction was done by utilising video-graphic data in real-time traffic conditions by the Department of Traffic Police, Shivamogga and also, by observing the behaviour of pedestrians while crossing the road. The study will also aid in determining correlations between pedestrian characteristics, crossing facilities, and vehicular traffic. This data will be analysed by regression methods in statistical analysis. As a result of this research, it is intended that a model of pedestrian gap acceptance and pedestrian crossing choice could be developed that also includes the factors that pedestrians look for when accepting the vehicle gaps between the vehicles to cross the crosswalks in roundabouts. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver's field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification. [ABSTRACT FROM AUTHOR]

Collisions of over-height vehicles with low clearance bridges is commonly encountered worldwide. They have caused damage to bridge structures, interruption to traffic, injuries or even fatalities to road users. To mitigate such risks, passive systems that involve warning gantries, flashing lights and illuminated signage are commonly installed. Semi-active systems using laser- or infrared-based detection systems in conjunction with visual warnings have been implemented. Nevertheless, some drivers ignore these visual warnings and collisions continue to occur. This paper presents a novel concept for a collision prevention system, which makes use of a series of sensor-activated, motorized rumble strips. These rumble strips span across a certain distance ahead of a low clearance bridge. When an over-height vehicle is detected, a mechanism is triggered which elevates the rumble strips. The noise and vibrations produce a vigorous alert to the offending driver. They also increase effective friction of the road surface, thus assisting to slow down the vehicle and shorten the stopping distance. The strips will be lowered after a certain time has elapsed, thus minimizing their effects on other vehicles. This article presents a conceptual framework and quantifies the vibration and noise caused by rumble strips in road tests. Road tests indicated that the vibration level typically exceeded 1 g and noise level reached approximately 90 dB in the cabin of a 3.5-ton truck. Fabrication of a proof-of-concept mechanized rumble strip model was presented and verified in an outdoor environment. The circuitry and mechanical design, and requirements in actual implementation, are discussed. The proposed event-triggered rumble strip system could significantly mitigate over-height vehicle collisions that cause major disruptions and injuries worldwide. Further works, including a comprehensive road test involving various types of vehicles, are envisaged. [ABSTRACT FROM AUTHOR]

Objective: To determine whether typical road users appreciate the special optical properties of retroreflective materials. Background: Retroreflective surfaces reflect light back towards the source of the illumination. All drivers benefit from retroreflective materials, as they are required on road signs, on large trailers, in lane delineation, and other traffic control devices. Retroreflective markings can also greatly enhance the conspicuity of pedestrians at night, but pedestrians typically underuse retroreflective markings. One possible reason is that pedestrians may not appreciate the special optical properties of retroreflective materials. Method: Two experiments tested whether observers could correctly predict that retroreflective materials appear remarkably bright when illuminated by a source that is aligned with the observers' eyes. Observers used a magnitude estimation procedure to predict how bright retroreflective and non-retroreflective stimuli would appear during a demonstration designed to highlight retroreflectivity. They then judged the brightness again during the demonstration. Results: In general, observers underestimated how bright retroreflective stimuli would be and overestimated how bright diffuse reflective and fluorescent stimuli would be. The underestimates for retroreflective stimuli were particularly striking when the observers had not closely examined the stimuli in advance. Conclusion: The fact that road users do not appreciate retroreflectivity may help explain why pedestrians underuse retroreflective markings at night. Application: Educational interventions could prove useful in this domain. [ABSTRACT FROM AUTHOR]

• We conducted a systematic review on the topic of road traffic suicides. • 51 studies were identified and their content summarized in 12 topic areas. • We concluded that road traffic suicides are not a well-researched phenomenon. • The prevalence and consequences of this method, together with who uses it and why, deserve more research attention. • More efforts are needed to develop potential preventive measures for this suicide method. Suicide on the road network is a recognized suicide method. We conducted a systematic review with the main aim of summarizing the existing research literature on road traffic suicides covering all road users who attempt or die by suicide in road traffic. A secondary and more specific aim was to examine the consequences for possible secondary participants. Our ultimate aim was to identify prevention measures. Relevant publications (N = 48) were identified in Medline , PsycInfo , Scopus , and Web of Science databases and supplemented with three studies from a personal database. We covered and discussed the following twelve topics: Countries where the data has been collected; Publication years – a historical overview; The main purpose of the studies; Data sources; How suicide was confirmed/defined (Differences between decisions by crash investigators and those of official forensic examinations); The proportion of suicides on roads; Types of crashes; The second party; Who dies by suicide in road traffic? (Age and gender, Mental health history, and Other background factors); Why people choose this method of suicide; The prevention of road suicides; An overview of issues regarding quality of studies. We conclude that road traffic suicides are not a well-researched phenomenon, especially concerning the reasons why people choose this specific method, the consequences for the possible second party, and specific preventive measures. [ABSTRACT FROM AUTHOR]

• Motorists preferred when the traditional division between protected and unprotected road users was recreated. • Vehicles' mean maximum speed decreased when the traditional division was recreated within the shared space. • Distribution of vehicles decreased when the traditional division was recreated within the shared space. • Vehicles' mean maximum speed decreased with high pedestrian density. Shared spaces are intended to constitute an attractive urban space for everyone by blending protected and unprotected road users. However, our previous study showed that pedestrians prefer the traditional division between protected and unprotected road users, at the same time as elderly pedestrians focused less on traffic when the division was recreated by large flowerpots. This follow-up study aimed to examine the effects of external factors, such as alternative design and pedestrian density, on motorists' subjective experiences, and the speed and placement of their vehicles within a shared space. To investigate this, Q-methodology was used to examine 32 motorists' subjective experiences, and video recordings were analysed to measure the speed and placement of 150 vehicles on Fisherman's Square in Västervik, Sweden, both without (75 vehicles) and with (75 vehicles) large flowerpots deployed. The motorists preferred when the large flowerpots were deployed to recreate the traditional division. The results also showed that both the large flowerpots and high pedestrian density decreased vehicles' mean maximum speed. The deployment of large flowerpots also decreased the distribution of vehicles over the square. Recreating some type of division between protected and unprotected road users appears to be preferred. Further studies are required to ascertain whether this solution works for all road user groups, including cyclists and e-scooter riders. [ABSTRACT FROM AUTHOR]

• Contrasting colours were used to enhance e-scooter rider visibility for drivers. • Detection time was lower with contrasting colours on both the e-scooter and rider. • Conspicuity ratings were higher for e-scooters with contrasting aids than grey clothes. • Contrasting visibility aids can reduce accidents from poor e-scooter visibility. This study explores how different visibility aids of contrasting colours could enhance the e-scooter riders' conspicuity under daylight conditions. With the rise in popularity of shared e-scooter services, an increase in incidents involving e-scooter riders and motorized counterparts was noticed. 48 licensed drivers (mean age = 24.6 years) detected the e-scooter riders in the videos and images featuring a simulated urban road environment in daylight, while performing a secondary task (n-back). The time to detect standing and moving e-scooter riders and subjective conspicuity ratings were assessed in three visibility aid conditions: Control (grey clothes and grey backpack), Patterned Jacket (jacket and backpack with checkerboard black and yellow pattern) and Wheels-Backpack (yellow stripes on the e-scooter wheels and on a dark backpack). Improved detectability was observed for e-scooter riders in the Wheels-Backpack condition, resulting in a minimum 168 ms increase in detection time compared to other two conditions. In terms of conspicuity ratings, participants gave higher scores to riders wearing Wheels-Backpack and Patterned Jacket (6.02 and 6.31, respectively, on a Likert 10-point scale), with no significant difference between them. Detection from videos or images showed similar effects. For all of these analyses, Bonferroni post-hoc tests followed a Repeated-measures ANOVA. Distributing contrasting colours across the e-scooter and the rider enhanced their daylight conspicuity better than when contrasting colours were located on the riders' upper body. However, drivers did not realise that these two visibility aids were not equal in terms of conspicuity benefits. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to examine differences between police-reported injury severity and trauma registry data among persons with linked records in North Carolina and quantify the degree of alignment. Methods: We analyzed linked North Carolina trauma registry and motor vehicle crash data from 2018. Injury severity identification was assessed using police-reported 5-point scale KABCO from crash data and Injury Severity Score (ISS) from trauma records. The analysis was stratified by age, sex/gender, race, ethnicity, and road users type to examine differences across groups. We calculated sensitivity, specificity, positive predictive values, and negative predictive values between police-reported injury severity and trauma registry data using ISS as the gold standard. Results: A higher proportion of patients were classified as suspected minor injuries (39.0%) compared to moderate injuries in trauma registry (25.1%). Police-reported crash data underreported injury severity when compared to trauma registry data. Police-reported KABCO had a higher degree of specificity when classifying minor injuries (79.3%) but substantially underestimated seriously injured patients, with a sensitivity of 49.9%. These findings were also consistent when stratified by subpopulations. Conclusion: Hospital-based motor vehicle crash data are a main source of injury severity identification for road safety. Police-reported data were relatively accurate for minor injuries but not seriously injured patients. Understanding the characteristics of each data source both separately and linked will be critical for problem identification and program development to move toward a safe transportation system for all road users. [ABSTRACT FROM AUTHOR]

Objective: In Bangladesh, drivers' failure to yield to pedestrians at designated crossings poses a significant safety risk and discourages their use of such crossings. The use of behavior change theories could be more appropriate in such complex situations where the interdependent behaviors of drivers and pedestrians interact. While many studies have identified factors that affect drivers' yielding behavior in the literature, fewer efforts have been made to apply behavior change theories in exploring and validating these factors, and to reach a consensus among competing road users. This study is among the first to utilize behavior change theories in Bangladesh to identify pedestrians' safety factors that could promote drivers' yielding behavior, upon which a consensus between drivers and pedestrians could be established. Methods: A self-reported attitudinal survey was administered to 202 drivers on two highways in Bangladesh with a questionnaire using the capability, opportunity, motivation, and behavior (COM-B) model for the comprehensive coverage of behavior change theories. The focus group interviews were also conducted with 40 pedestrians and 19 drivers who have experience with four crossing sites on the selected highways. The collected data were analyzed using a regression model to identify significant factors influencing the drivers' yielding behavior. These factors were then justified using a deductive thematic coding framework based on behavior change theories. Results: The regression model explained the variance in drivers' yielding by 45.1% with eight factors. The model found seven positive significant contributory factors in the drivers' yielding that could promote pedestrian safety. Of them, the motivation factors were avoiding random crossing by pedestrians, vulnerable groups, assertiveness, and facial fear expressions; and the opportunity factors were traffic signs or advanced yield lines, crossing in groups at specific times, and enforcement. Conclusions: The study's findings have practical implications for policymakers, highway designers, and other stakeholders involved in promoting pedestrian safety by acknowledging their stake in making any decision that might impact them. Highway designers can use the thematic coding framework to recommend any contributory factors involved, where competing drivers' unwillingness to yield is the primary threat to pedestrians' safety. [ABSTRACT FROM AUTHOR]

Objective: Injury outcomes for powered two- and three-wheeler (PTW) riders are influenced by the rider posture. To enable analysis of PTW rider accidents and development of protection systems, detailed whole-body posture data is needed. Therefore, the aim of this study is to fill this gap by providing collections of average male whole-body postures, including subpopulation variability, for different PTW types. This will enable future studies to explore the influence of PTW rider posture variation and to support safety system development. Methods: 3D photometric measurements of 51 anatomical landmarks were recorded on 20 (50th percentile male) volunteers in their preferred riding postures across three PTW types (naked, scooter, and touring). Following an outlier removal process, a principal component analysis (PCA) was performed to calculate average postures and principal components (PCs), to describe the observed posture variation, for each PTW. The visualization of the PCs was facilitated through kinematic linkage representations, connecting anatomical landmarks and estimated joint centers to form segments and characteristic joint angles. Results: The first seven PCs explained 80% of the variance in posture for each of the three PTWs. Across PTWs, these PCs frequently described combinations of postural features including variation in fore-aft seat positions, pelvic tilt, spinal curvature, head position, and extremity flexion-extension. Analysis revealed distinct differences in average postures across the three PTWs, on average, 10 ± 9° for the characteristic joint angles within a min-to-max range between the three PTWs. However, for all three PTWs, the variability between volunteers in characteristic joint angles on the same PTW were on average more than twice as large within a ± 2 SD range (26 ± 11°). Conclusions: The results suggest that PTW rider posture variation must be addressed by involving simultaneous adjustments of multiple body parts, as described by each of the first seven PCs, as a direct consequence of the human body interconnectedness. Furthermore, the study's findings challenge conventional assumptions that the relative distance between PTWs' handlebar, seat, and foot support predominantly influences rider postures. Instead, the research demonstrates that individual variability has a substantial influence on rider posture and should be considered in PTW safety development. [ABSTRACT FROM AUTHOR]

This study aims to analyze the usage patterns of rest areas on highways and toll roads using a data mining approach, utilizing popular time information from Google Maps and web scraping techniques. We collected popular time data for rest areas along highways and expressways using web scraping technology and Google Maps API. The research methodology includes a data collection phase that extracts popular time information from Google Maps using web scraping techniques. The collected data was then analyzed using data mining techniques to identify road user visitation patterns, specifically at rest areas. The analysis included understanding the most popular times visitors visit rest areas and providing deep insights into road user preferences. The findings indicate specific time patterns during which visitors visit rest areas. These findings can have important implications in the planning and managing of road infrastructure, enabling improved services at rest areas according to road users' needs. They can also assist in determining appropriate locations for constructing new rest areas or necessary improvements to existing rest areas. [ABSTRACT FROM AUTHOR]

Featured Application: Application as a decision-support tool in the management of level crossings. Railway level crossings pose risks to both train passengers and road users, worsened by the complex interaction between rail and road traffic, particularly on local railways. This study introduces a Risk Management (RM) framework designed to enhance safety at level crossings on local railways by providing a decision-support tool. By individually assessing risk factors, the framework allows for a precise evaluation of the current risk levels in terms of both Cumulated and Individual Risk levels, Individual Risk, and the impacts of potential modifications. The framework allows the quantitative assessment of risk and resource optimization, enabling infrastructure managers to identify high-risk crossings and select the most effective safety interventions. The framework was applied to 96 level crossings on an Italian local railway line, demonstrating its ability to support decision-making for targeted interventions as well as broader network-level actions, significantly improving system safety. [ABSTRACT FROM AUTHOR]

Driving while inattentive or fatigued significantly contributes to traffic accidents and puts road users at a significantly higher risk of collision. The rise in road accidents due to driver inattention resulting from distractive objects, for example, mobile phones, drinking, or tiredness, requires intelligent traffic monitoring systems to promote road safety. However, outdated detection technologies cannot handle the poor accuracy and the lack of real‐time processing possibility especially when combined with the variations of driving environment. This paper introduces "ME‐YOLOv8" which operates driver's distraction and fatigue through a modified version of YOLOv8, which includes modules multi‐head self‐attention (MHSA) and efficient channel attention (ECA) modules applied, where the goal of MHSA is to improve the sensitivity of global features and the ECA attentions focus on critical features. Additionally, a dataset was created containing 3660 images covering multiple distracted and drowsy driver scenarios. The results reflect the enhanced detection capabilities of ME‐YOLOv8 and demonstrate its effectiveness in real‐time scenarios. This study demonstrates a significant advancement in the application of AI to public safety and highlights the critical role that state‐of‐the‐art deep learning algorithms play in lowering the risks associated with distracted and tired driving. [ABSTRACT FROM AUTHOR]

Effective traffic management is crucial in addressing the growing complexities of urban mobility, and variable message signs (VMSs) play a vital role in delivering real-time information to road users. Despite their widespread application, there is limited comprehensive understanding of how VMS influence user behavior and optimize traffic flow. This systematic literature review aims to address this gap by examining the effectiveness of VMS in shaping user interactions and enhancing traffic management systems. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a thorough analysis of relevant studies was conducted to identify key factors influencing VMS impact, including message content and characteristics, complementary sources of information, user demographics, VMS location, and users' reliance on these signs. Additionally, the review explores the implications of displaying non-critical information on VMS and introduces virtual dynamic message signs (VDMSs) as an innovative approach for delivering public traveler information. The study identifies several research gaps, such as the integration of VMS with vehicle-to-everything (V2X) technologies, navigation systems, the need for validation in real-world scenarios, and understanding behavioral responses to non-critical information on VMS. This review highlights the importance of optimizing VMS for improved user engagement and traffic management, providing valuable insights and directions for future research in this evolving field. [ABSTRACT FROM AUTHOR]

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide. [ABSTRACT FROM AUTHOR]