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Potholes are a pervasive road hazard with the potential to cause accidents and vehicle damage. Detecting potholes accurately is essential for timely repairs and ensuring road safety. However, existing detection methods often struggle to perform in adverse weather conditions, including rain, snow, and low visibility. This work aims to improve pothole detection across diverse weather and lighting scenarios, employing a two-phase strategy that integrates data augmentation with images generated by Generative Adversarial Networks (GANs) and the deployment of visual attention techniques. For this purpose, advanced models such as YOLOv8, RT-DETR, and our modified version of YOLOv8 were employed. In the first phase, multiple image-to-image translation models were trained and applied to a real-world dataset to generate synthetic images of potholes under different weather conditions, including rain, fog, overcast, dawn, and night. The detection accuracy results show improvements in all monitored metrics across most tested conditions following the incorporation of augmentation. The most significant improvement resulting from augmentation was observed in low-visibility conditions, captured during evening and night, with an increase of up to 11% and 19% in mean Average Precision (mAP@.5) across all models. The second phase employed different modifications of YOLOv8 with modules such as Attention-Based Dense Atrous Spatial Pyramid Pooling, Vision Transformer and Global Attention Mechanism to enhance the detection of potholes in challenging visual conditions. The compensation for increased model complexity, such as the utilization of depthwise convolutions, was also employed. To evaluate the effectiveness of this approach, a publicly available pothole dataset with images captured in diverse weather conditions is used. The results indicate that the proposed method achieved an 8.4% improvement pre-augmentation and a 5.3% improvement post-augmentation compared to the original YOLOv8, surpassing existing approaches in terms of accuracy and enhancing pothole detection in adverse weather conditions. [ABSTRACT FROM AUTHOR]

• TPB (Theory of Planned Behavior) is widely used to understand and explain aberrant driving behaviors. • TPB does not cover all behavioral aspects; therefore, it will be extended by additional constructs to provide a more comprehensive understanding of aberrant behaviors. • Additional constructs, such as moral norms and past behavior, will improve the predictability of the TPB. Exceeding speed limits is one of the most dangerous violations, causing severe injuries and many deaths every year. For that, several research works focused on understanding and explaining speeding behavior. The Theory of Planned Behavior (TPB) is widely used to understand and explain several human behaviors based on three primary constructs: attitudes, subjective norms, and perceived behavioral control. However, several studies have used different versions of the TPB to explain and understand speeding behavior, which constitutes a source of confusion for researchers and intervention designers. Based on the Theory of Planned Behavior, this study aimed to identify the main determinants of speeding behavior by screening and analyzing the results of relevant studies in the literature. A literature review will identify relevant research related to using TPB to explain and understand speeding behavior. Subsequently, a meta-analysis will synthesize and combine the results of the selected studies, providing an improved TPB model of speeding behavior. The obtained results suggest that in addition to the three standard constructs of the original TPB, moral norms and past behavior should be included as key determinants of speeding intention and behavior. Moral norms and past behavior have been found to be key determinants of speeding behavior. This finding will contribute to the development of more effective behavior change interventions aimed at encouraging adherence to speed limits by taking into account the influence of moral norms and past behavior. [ABSTRACT FROM AUTHOR]

• Distracted driving (DD) remains a significant global challenge to road safety. • Links between personality and DD examined in Australian and Italian drivers. • Neuroticism, Extraversion, and Agreeableness positively affected DD. • Conscientiousness affected attitudes and norms; Openness affected involuntary DD. • Invariances across cultures suggested the need for assessing drivers' personality. The present study aimed to investigate the association between the Big Five personality dimensions with the susceptibility of distracted driving (i.e., engagement in voluntary distraction, attitudes toward distraction, and susceptibility to involuntary distraction) comparing samples of Australian and Italian drivers. Distracted driving remains a significant global challenge to road safety, contributing to the occurrence of motor-vehicle crashes with serious consequences on public and environmental health. Despite efforts to explore factors underlying distracted driving, less is known on the role of the driver's personality in affecting the tendency to report distraction. Five hundred and fifty-one participants (55 % females; age range: 18–82 years; M ± sd: 40.4 ± 17.5) from Australia and Italy completed an online survey including questionnaires on personality and distracted driving. The invariance of the tested model was assessed through a multigroup path analysis considering personality traits as predictors and different facets of the susceptibility to distracted driving as outcomes, in a unique model. The effects of age, gender, and education were also controlled in the analyses. Results showed differences among nationality groups in personality traits as well as in susceptibility to distraction. The tested model showed invariance across nationality groups, suggesting positive effects of Neuroticism, Extraversion, and Agreeableness on all dimensions of susceptibility to distracted driving. Furthermore, Conscientiousness was found to affect attitudes toward distraction, perceived control, and perceived social norms. Finally, the tendency to report susceptibility to involuntary distraction was influenced by Openness. These results provide insights on the usefulness of assessing personality profiles to achieve road safety improvements and represent a valuable source of knowledge for the study of individual risk exposure. [ABSTRACT FROM AUTHOR]

Background: Methylphenidate is a routinely prescribed treatment for attention-deficit/hyperactivity disorder with misuse potential owing to its perceived performance-enhancing and euphoric properties. Although clinically effective, there is limited understanding of how methylphenidate affects safety-sensitive tasks such as driving when used by healthy individuals. Aim: Explore the acute effects of 10 mg methylphenidate on driving performance and gaze behaviour. Methods: Twenty-five fully licensed, healthy adults (mean age = 33.5 ± 7.8 years, 64% male) took part in two 40-min simulated highway drives with simultaneous eye movements monitored using a proprietary automotive-grade driver monitoring system (Seeing Machines). Driving performance was assessed using the standard deviation of lateral position, standard deviation of speed and steering variability. Visual scanning efficiency was determined using ocular metrics, such as fixation duration and rate, gaze transition entropy, and stationary gaze entropy, were assessed to determine visual scanning efficiency. Results: Methylphenidate significantly improved driving performance by reducing lane weaving and speed variation, particularly in the latter half of the drive. Although a significant reduction in fixation duration was observed, all other ocular metrics remained unchanged. Conclusions: Methylphenidate mitigates performance decrements typically associated with prolonged and monotonous driving. The absence of pronounced oculomotor effects suggests that a single 10 mg dose of methylphenidate has no deleterious impact on visual scanning behaviour during driving tasks with low-to-moderate cognitive demand. Future research should investigate the effects of methylphenidate under various dosing and driving conditions to better understand its impact. Trial Registration: ACTRN12620000499987. [ABSTRACT FROM AUTHOR]

Motorcyclist fatalities have risen in Latin and Asian countries, highlighting the necessity for a systematic approach through the Safe System (SS) to achieve zero fatalities. Traditional studies often rely on data-driven methodologies, which may lead to biased results and may fall short of capturing the systematic SS perspective. This paper develops a causal analysis of crash severity involving motorcyclists based on the SS approach and Pearl's causal inference paradigm, using observational data. Initially, the main sources of bias related to traditional categorical modeling applied to crash severity analysis were illustrated through a simulation example. Subsequently, a literature review was conducted to establish a conceptual framework rooted in the SS. Finally, causal hypotheses were formulated and tested using structural equation modeling applied to data collected from motorcyclist crashes on federal highways in the state of Ceará/Brazil. The outcomes of the causal model support the initial hypothesis that alcohol use significantly contributes to the severity outcome of motorcycles-related crashes. The model unveils a noteworthy association between weekends and alcohol consumption and an inclination among younger individuals toward newer motorcycles with more powerful engines. The adoption of a causal approach enhances result reliability by controlling for confounding variables and incorporating a theoretical framework. [ABSTRACT FROM AUTHOR]

Identification of road crash Hotspots related to adverse weather is critical in implementing effective measures for reducing road crashes. A reliable hotspot identification method and firm criteria for evaluation are essential for accurately predicting crash risk Hotspots related to adverse weather. These steps will significantly enhance the accuracy of hotspot predictions and improve road safety efforts. This study investigates significant road safety issues related to adverse weather conditions in Jordan by testing hotspot identification methods (HSIDs) for crashes occurring under these conditions. The study applies and compares several hotspot ranking techniques including the Crash rate method (CR), the Crash frequency method (CF), the potential for improvement using predicted crashes (PI), and the Empirical Bayes method (EB), using a detailed dataset of weather-related crashes. The HSID methods are systematically tested using several statistical tests and assessed to determine the reliable approach/es for detecting hazardous sites during adverse weather conditions. The analysis encompasses urban and rural road segments, involving their operational and road characteristics and various crash characteristics. By integrating modern statistical tools, the study provides a detailed assessment of crash-identified Hotspots under adverse weather conditions and highlights major findings. The findings provide significant insights for decision-makers, emphasizing the importance of targeted initiatives and better road safety measures in identifying hotspots. Based on the findings, the study proposes some techniques for improving road safety related to poor weather Given the study's limitations, further and more comprehensive research is recommended to address these limitations and reach more conclusive results. This offers a strong foundation for decision-makers and traffic safety authorities to put into practice strategies to lower crash rates and enhance safety during inclement weather. [ABSTRACT FROM AUTHOR]

Integrated Science, Technology, Engineering, and Mathematics (STEM) education is a novel strategy to promote holistic learning experiences. In terms of road and traffic safety education for primary and secondary school students, this integration provides a complete framework for addressing the complexities of transportation networks and preventing accidents. This review paper examines the relationship between integrated STEM education and road/traffic safety, emphasizing its importance, obstacles, and possible influence. The paper is not a thorough evaluation of integrated STEM education, but it is well-referenced and should direct readers to more resources. The paper includes case studies and examples to help readers gain a better understanding of Integrating STEM into Road/Traffic Safety Education. [ABSTRACT FROM AUTHOR]

The design of effective road safety countermeasures requires a network diagnosis supported by data. Moreover, infrastructures need to be ready for the introduction of vehicle-to-infrastructure communications to support technologies, as truck platooning. Ascendi, a motorway concessionaire, developed an action plan to decrease crash frequency and casualties that rests in data recorded by automatic vehicle counting devices. To have a good network representation, new equipment will provide more data, thus enhancing the selection of countermeasures. We developed a multilevel decision-support approach to define equipment location. The process stages correspond to three levels of analysis: (1) clustering for road segment classification (network level); (2) quantification of the devices to install ensuring similar coverage (concession level); (3) device allocation according to geographical and cost criteria (segment level). An iterative and participatory process involving Ascendi resulted in a proposal for adding 43 devices to the existing 72, increasing the network coverage to 39%. [ABSTRACT FROM AUTHOR]

Objectives: This article presents an analysis of the traffic-calming effects of bus rapid transit (BRT) by studying changes to motor vehicle speeds before and after implementation of Albuquerque Rapid Transit (ART) infrastructure in Albuquerque, New Mexico. Methods: While ART construction was completed in spring 2018, the BRT buses did not operate until December 2019; providing a unique opportunity to explore the influence of BRT infrastructure sans BRT buses (i.e., to tease apart the effects of BRT infrastructure and operations). We used validated data from StreetLight InSight to compare before/after changes to average motor vehicle speeds and 85th percentile motor vehicle speeds at 46 ART sites and 36 control sites. Results: Findings suggest that infrastructure associated with BRT systems can improve traffic safety by reducing vehicle speeds. Speed decreases at the ART sites were especially strong in terms of 85th percentile decreases, suggesting that the BRT infrastructure is especially effective at limiting excessive speeding. Motor vehicle 85th-percentile speeds along the ART corridor were reduced by 11.5% (compared to a 5.8% decrease at control sites). The 85th-percentile speeds at the ART sites decreased from 32.3 mph to 28.6 mph, which is an especially important range for vulnerable road-user safety outcomes. While ART intersections saw the largest decreases in absolute speeds (a reduction of 4.1 mph in 85th-percentile speeds), ART mid-block sites had larger decreases relative to the control mid-block sites (decreases in 85th-percentile speeds were 73.7% greater at ART mid-block sites than at control mid-block sites). BRT-related lane reductions were linked with particularly strong speed reductions; there were 85th-percentile speed reductions of 4.1 mph (12.6%) when general vehicle lanes were removed versus 2.2 mph (7.8%) when lanes were not removed. Conclusions: Speed reductions were experienced across the ART corridor even though 87.0% of BRT locations did not have a change in posted speed limit, suggesting that physical changes to the roadway associated with BRT were impactful in terms of speed reductions and in turn could possibly promote traffic injury prevention by decreasing the number and severity of crashes. [ABSTRACT FROM AUTHOR]

Objective: Road traffic crashes are mainly caused by three concurrent factors: infrastructure, vehicle, and human factors. Regarding infrastructure, in recent decades, a series of management tools and procedures called Road Infrastructure Safety Management (RISM) have been proposed. The aim of RISM procedures is to support road authorities in the prevention and mitigation of future road traffic crashes. One of these procedures is the In-built Road Safety Assessment (IRSA) methodology. The peculiarity of an IRSA methodology is the underpinning method used to assign a score to a road section with the aim of identifying those road sections in a network with safety-related infrastructure deficiencies. The objective of this paper is to provide an overall literature review of existing methodologies used worldwide for network-wide road safety assessment for rural road. Methods: The review was conducted following the guidelines provided by Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) checklist 2020. The characteristics of these methodologies were compared with respect to the following research questions: What are the general characteristics of IRSA methodologies? What risk method/index is applied? Which data collection method/technique is used? What types of road parameters are considered for the assessment? What is the level of expertise needed to implement the methodology? Where and how are the results validated? Results: As a result, 14 IRSA methodologies were identified. Also, the review showed that similar road parameters were used including: operating speed, road surface, low curve radius, poor sight distance (horizontal and vertical curves), lane width, undivided road (median type), shoulder width, sight obstructions (landscape, obstacles and vegetation), absence of traffic signs and road markings, traffic flow (AADT), intersection quality and density of intersections/lateral accesses. Conclusions: Despite these similarities, some differences were observed in risk formulation, safety quantities of parameters, level of expertise required, and validation of studies. Researchers may use these findings to develop future road safety assessment methodologies, while road practitioners can make use of this in identifying suitable network-wide assessment methods for safety assessments of road infrastructures. Finally, a series of recommendations for future research work on IRSA methodologies is suggested. [ABSTRACT FROM AUTHOR]

Dynamical vehicle performances depend on its center of gravity's position. It is necessary to determine this position as closely as possible. The current methods of determining the center of gravity's position are either not sufficiently accurate or are very demanding in relation to finances, time and special equipment. At the same time, there is a lack of accurate data on the center of gravity's position of current vehicles for further research. Therefore, the purpose of this study is to propose such a methodology of measuring the center of gravity's position, including its height, which is accurate enough and has as low-demand measuring equipment as possible. Another purpose of this study is to obtain data on the change in the centers of gravity's positions in different vehicles due to various load distributions. Extensive experimental measurements, according to the innovative methodology, were performed with five vehicles. The positions of the center of gravity were determined for various load distributions, and the results were analyzed in detail. [ABSTRACT FROM AUTHOR]

In-vehicle information system (IVIS) use while driving has raised concerns about driver distraction, especially for young drivers. To understand better their psychological factors, an extended theory of planned behavior (TPB) was employed to predict young drivers' IVIS use intentions and behavior while driving. A two-wave longitudinal survey was conducted to explore the temporal effects of 'intention–behavior' causality. At Time 1, 236 qualified participants completed a main questionnaire assessing the standard TPB constructs (attitude, subjective norms, and perceived behavior control) and the extended constructs (descriptive norms, moral norms, and perceived risks). At Time 2, 145 follow-up questionnaires measuring self-reported behavior were successfully administered. The hierarchical multiple regression analyses showed that the standard constructs account for 36.5% of the intention variance and 41.2% of the behavior variance. The extended constructs additionally contributed 20.3% of intention variance. All variables were identified as significant predictors of intentions, except for perceived crash risks and perceived risks of being caught and fined. The sole significant predictor of prospective behavior was intention. Theoretically, the findings further support the efficacy of the TPB in explaining IVIS use while driving. Practically, it is helpful to design non-legal interventions that sustainably reduce young drivers' engagement in IVIS-related distractions. [ABSTRACT FROM AUTHOR]

Issue Addressed: E‐scooters are sought after for their cost‐effectiveness, sustainability, and efficiency in urban transportation. However, this popularity has been accompanied by a surge in injuries, prompting a deeper investigation into the factors influencing risk perceptions among e‐scooter users. Methods: Using a qualitative approach, we conducted 19 interviews with those who attended an emergency department as a result of e‐scooter injury. We aimed to understand the psychosocial effects of these incidents and employed thematic analysis to discern recurrent patterns in participants' experiences, focusing on alterations in daily life, community response, perception shifts, and avenues to enhance safety awareness. Results: The findings underscored significant disruptions to daily life due to injuries, demonstrating enduring impacts on lifestyle and wellbeing. Participants exhibited a perceptual shift, transitioning from perceiving e‐scooters as enjoyable to viewing them as dangerous. Recommendations for enhancing safety awareness included accessible and clear safety education, pre‐ride briefings, real‐time safety guidance, temporal rental restrictions, and mandatory breathalysers before e‐scooter use. Conclusions: The study underscores the importance of considering not only the individual experiences and perceptions of e‐scooter injuries but also the broader social context, including the night‐time economy. Leveraging peer narratives and community engagement is vital to reshape risk perceptions and promote harm reduction messages. So What?: A comprehensive approach through proactive interventions and robust educational strategies is essential to foster responsible e‐scooter usage and prioritise public safety. [ABSTRACT FROM AUTHOR]

The slowdown in economic development caused by traffic accidents in Spain, together with the disparity in the risk of death or injury due to traffic accidents in its provinces, makes it necessary to evaluate their road safety performance. Therefore, the objectives of the present research are, on the one hand, to find out the level of road safety efficiency of Spanish provinces in the period 2014–2018. On the other hand, it is also aimed to determine to what extent the annual efficiency change is originated by pure changes in efficiency or by technological changes. To achieve both objectives, the nonparametric technique of Data Envelopment Analysis and the Malmquist Index have been used based on the consideration of the Safety Performance Indicators (SPI) as the inputs of the process. The research findings show changes in the level of road safety efficiency in each of the years of the study period. Thus, it is possible to identify a geographical delimitation of the causes that generate changes in efficiency in recent years. A contraction in efficiency and technological progress is identified in part of the Spanish provinces in the north, northeast, and south of Spain. [ABSTRACT FROM AUTHOR]

• Peer pressure on self-reported driver behaviours was examined. • The Peer Pressure on Risky Driving Scale has been adapted into Turkish. • Direct or indirect peer pressure to take risks decreased with age. • Only for males, risk-discouraging direct pressure increased with age. • Aberrant behaviours increased with either direct risk-encouraging or indirect peer pressure. Peers play an important role in shaping the behaviours of their counterparts at different stages of life. Studies have shown that peer influence/pressure has a significant role in the traffic environment as well as in many other social settings, especially for risky behaviours. Therefore, the aim of this study was to examine age and sex differences in respect of peer pressure and the role of peer pressure on driver behaviour in Türkiye. A total of 405 drivers with a mean age of 23.84 (SD =4.82) years completed an online survey consisting of the Peer Pressure on Risky Driving Scale (PPRDS), the Brief Sensation-Seeking Scale (BSSS) and the Short Driver Behaviour Questionnaire (S-DBQ, measuring aberrant and positive driver behaviour). First, the psychometric structure of the PPRDS, used for the first time in Turkish, was found to support the original structure, addressing direct pressure in the form of risk-encouraging or risk-discouraging and indirect pressure. Indirect pressure was seen to decrease with age for both males and females, whereas risk-discouraging direct pressure increased with age only for males. Sensation-seeking was positively related to aberrant behaviours. Risk-encouraging direct peer pressure and indirect pressure were positively associated with aberrant (risky) driver behaviour, whereas indirect peer pressure was associated with decreased aberrant driving behaviour and increased positive (helpful, polite) driving behaviour. The findings suggested that drivers feel strong peer pressure in traffic, but it varies by age and sex, and this pressure is stronger for young males. These findings have important implications for road safety and intervention studies. [ABSTRACT FROM AUTHOR]

Frequent highway accidents occur in the Guizhou region, among which poor visibility due to fog is one of the main causative factors. In this region, traditional large-scale, high-computational-power fog monitoring systems are difficult to install and have high costs due to complex terrains, high altitudes, and winding roads, causing traffic management departments to fail to obtain fog information accurately and timely, which undoubtedly becomes a significant safety hazard. To solve this problem, this study proposes a fog monitoring solution based on the lightweight deep learning model ABNet. The solution first preprocesses the input images, including generating the fog concentration distribution map using the fog imaging model, and obtaining the high-frequency component image using filters based on the 2D discrete wavelet transform. Subsequently, these two processed images and the original image are fed into the three branches of the ABNet for training to fully extract fog concentration and high frequency information, thereby improving model performance and prediction accuracy. The ABNet model parameters only require 38.52MB, and the computational complexity is a mere 1.71GFLOPs, effectively solving the limited storage and computational resources problem in edge computing. The model was evaluated using the Guizhou highway fog weather data set, and ABNet exhibited impressive performance with a composite classification accuracy as high as 92.3%, reaching 92.4% in average precision rate and 92.3% in average recall rate. In comparison, the performance of models like VisNet, VGG16, EfficientNetV2, and Swin Transformer V2 seemed inferior. The experimental results validated the excellent performance of the ABNet model in terms of accuracy and efficiency. The ABNet model in this study, with its lightweight deep learning design, small parameter scale, and lower computational power requirements, provides a solution suitable for complex terrains and practical environments of edge computing devices, and it provides vital technological support to improve traffic safety on the highways in the Guizhou region. [ABSTRACT FROM AUTHOR]

Road traffic accidents (RTAs) are critical global issue, resulting in over 1.3 million deaths annually, with the burden disproportionately affecting low and middle income countries like Nigeria. Thus, the objective of this paper is to develop a predictive model for Road Traffic Accident Involvement in Southwest Nigeria: A Case Study of Ekiti, Osun, and Oyo States using appropriate standard methods after collecting accident data from relevant agencies for a period of ten years (2014-2023). The results indicate a strong correlation between accident cases and the number of people involved, with fatal and minor accidents having the most significant effects. The model explains 75.5% of the variance in accident outcomes, offering insights into predictive trends that can inform road safety interventions. The study highlights the importance of data-driven approaches in mitigating RTAs and improving road safety management in the study region. Recommendations include stricter traffic law enforcement, enhanced road maintenance, and safety education initiatives to reduce the frequency and severity of accidents in the region. [ABSTRACT FROM AUTHOR]

Objective: Excessive speed is a major risk factor for serious injuries and death. However, speeding remains a pervasive problem around the world. This study aimed to investigate the factors associated with speeding behavior in the city of Buenos Aires, Argentina. Methods: A sample of vehicles (n = 34,967) from ten locations in the city was observed in two waves during 2021. Measurements were made at different times and days of the week. Observation sites were free of intersections, traffic lights, speed bumps and cameras, allowing drivers to speed freely. Data on speed, drivers and vehicle types were collected. Factors associated with speeding were identified through logistic regression analyses. Results: 15.3% of vehicles were observed to be speeding. Roads with posted speed limits of 40 km/h showed higher speeding compared to 60 km/h roads. 77% of vehicles traveled above 30 km/h on local roads, and 30% above 50 km/h on avenues. Motorcycles, both commercial and private, showed a higher percentage of speeding compared to all other vehicles. Speeding was lower among women, among adults over 60 years of age, and among those using cell phones. Conclusion: It is crucial to strengthen strategies for increased compliance with speed limits. Actions targeting motorcyclists must be a priority. [ABSTRACT FROM AUTHOR]

Road damage detection (RDD) is critical to society's safety and the efficient allocation of resources. Most road damage detection methods which directly adopt various object detection models face some significant challenges due to the characteristics of the RDD task. First, the damaged objects in the road images are highly diverse in scales and difficult to differentiate, making it more challenging than other tasks. Second, existing methods neglect the relationship between the feature distribution and model structure, which makes it difficult for optimization. To address these challenges, this study proposes an efficient dense attention fusion network with channel correlation loss for road damage detection. First, the K‐Means++ algorithm is applied for data preprocessing to optimize the initial cluster centers and improve the model detection accuracy. Second, a dense attention fusion module is proposed to learn spatial‐spectral attention to enhance multi‐scale fusion features and improve the ability of the model to detect damage areas at different scales. Third, the channel correlation loss is adopted in the class prediction process to maintain the separability of intra and inter‐class. The experimental results on the collected RDDA dataset and RDD2022 dataset show that the proposed method achieves state‐of‐the‐art performance. [ABSTRACT FROM AUTHOR]

Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice. [ABSTRACT FROM AUTHOR]

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Despite various interventions in road safety work, fatal and severe road traffic accidents ( R T A s ) remain a significant challenge, leading to human suffering and economic costs. Understanding the multicausal nature of R T A s , where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in R T A s , focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns ( B P s ), using the Austrian R T A database. This study proposes Fisher's exact test to estimate the relationship between an accident-related variable and fatal and severe R T A s (severe casualties). In terms of pattern recognition, this study develops the maximum combination value ( M C V ) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX method, created to accurately detect and analyse variable-specific B P s in R T A data. Findings indicate significant B P s contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity. [ABSTRACT FROM AUTHOR]

Tunnel-interchange sections are characterized by complex driving tasks and frequent traffic conflicts, posing substantial challenges to overall safety and efficiency. Enhancing safety in these areas is crucial for the sustainability of traffic systems. This study applies behavior adaptation theory as an integrated framework to examine the impact of environmental stimuli on driving behavior and conflict risk in small-spaced sections. Through driving simulation, 19 observation indicators are collected, covering eye-tracking, heart rate, subjective workload, driving performance, and conflict risk. The analysis, using single-factor ranking (Shapley Additive Explanation), interaction effects (dependence plots), and multi-factor analysis (Structural Equation Modeling), demonstrates that driving workload and performance dominate the fully mediating effects between external factors and conflict risk. High-load environmental stimuli, such as narrow spacing (≤500 m) and overloaded signage information (>6 units), significantly elevate drivers' stress responses and impair visual acuity, thereby increasing task difficulty and conflict risk. Critical factors like saccade size, heart rate variability, lane deviation, and headway distance emerge as vital indicators for monitoring and supporting driving decisions. These findings provide valuable insights for the operational management of small-spacing sections and enhance the understanding of driving safety in these areas from a human factor perspective. [ABSTRACT FROM AUTHOR]

Pavement skid resistance plays a crucial role in ensuring road safety and avoiding accidents. In the past, the laboratory evaluation of the skid resistance was carried out by studying only the coarse aggregates of the wearing course. To overcome this drawback, the Wehner-Schulze (WS) machine was developed in Germany in the 1960s. This equipment, composed of a polishing unit and a measuring unit, has great potential in predicting pavement skid resistance and its evolution over time, but is still little known in the pavement community (especially outside Europe). For these reasons, there is a need of a comprehensive review of the existing technical-scientific literature concerning the use of the WS machine. Specifically, this paper focuses on the main factors affecting the skid resistance in WS tests, the correlation of WS data with other laboratory test methods and with field skid resistance/polishing, and the available prediction models that have been validated through WS measurements. The critical analysis of the existing literature highlights that it is possible to correlate WS data with typical skid resistance field measurements as well as WS polishing with traffic polishing, but further efforts are needed in this regard. Future work should focus especially on open-graded mixtures and innovative asphalt mixtures (e.g., containing recycled materials and additives). From the perspective of pavement management, based on a theoretical background, the WS test results could be used as starting point for simplified prediction models of the in-situ skid resistance. [ABSTRACT FROM AUTHOR]

This paper describes an approach for developing strategic cycle network planning tools. Based on our experience developing and deploying the Cycle Route Uptake and Scenario Estimation (CRUSE) Tool for Ireland, we outline the underlying methods, including disaggregation of origin–destination data with the open source 'odjitter' software, incorporation of additional trip purposes, routing, scenario generation, and development of an intuitive user interface that is tested and used by practitioners. Commissioned by the national infrastructure agency Transport Infrastructure Ireland, CRUSE provides estimates of current and potential future cycling levels under 'snapshot' scenarios to inform investment decisions. The publicly available results at https://cruse.bike/ enable planners, engineers, and other stakeholders to make more evidence-based decisions. CRUSE goes beyond previous work by: modeling networks at high spatial resolution; simulating multiple trip purposes (social, shopping, personal utility, recreational, and cycle touring), supplementing official origin–destination datasets on travel for work and education; and providing estimates of 'quietness' (a proxy for cyclist comfort and route preference) at the route segment level. Three network types—'Fastest', 'Balanced', and 'Quietest'—help plan both arterial and residential cycle networks. Workshops with stakeholders were used to inform the development of the tool. Feedback shows that the tool has a wide range of uses and is already being used in practice to inform urban, inter-urban, and rural cycle network designs. The approach is flexible and open source, allowing the underlying ideas and code to be adapted, supporting more evidence-based and effective cycling policies and interventions internationally. [ABSTRACT FROM AUTHOR]

AbstractUnderstanding the nature of traffic accidents in relation to urban access networks is crucial for building safer and more resilient cities. This paper examines the issue of traffic accidents through the lenses of urban configurational theory and urban land use. Three data layers were used in the study, including space syntax analysis conducted in Depthmap X, geotagged traffic accidents collected by the police department, and geotagged land-use data. The method involved superimposing these data layers and exploring potential correlations using a geographic information system (GIS). The findings indicate significant correlations between the spatial frequency of traffic accidents and the choice measure (at 2500 m), local integration, and active land use. The findings of this study can help inform planners and policymakers about the best location to implement safety measures to reduce the risk of traffic accidents in urban access networks. [ABSTRACT FROM AUTHOR]

Article pays attention to the impact of using the trailer on driving performances of a studied vehicle combination.The research was performed via extensive experimental measurements. A new methodology of determining a technical condition of the trailer’ brakes was proposed and verified, which can be also applied during the regular technical inspection. Various load distributions in the trailer caused the centre of gravity’s position of the vehicle was changed, which may increase the risk of skidding or disconnection of the trailer from the towing vehicle. Also, the vehicle’s ability to accelerate and decelerate decreased considerably due to the loading up. There were assessed the braking characteristics of the combination of vehicles as well, depending on the technical condition of the trailer’s brakes. The importance of this article lies in the quantification of selected factors on road safety in relation to driving with a combination of vehicles. [ABSTRACT FROM AUTHOR]

Accurately predicting traffic accident severity is crucial for road safety. However, existing studies lack interpretability in revealing the relationship between accident severity and key factors. To address this issue, we propose a new interpretable analytical framework. The framework utilizes XGBoost and SHAP to select effective factors. Then the AISTGCN model is constructed by improving the STGCN through the local attention mechanism to predict the severity of the accident. Finally, DeepLIFT is used to interpret the forecasts and identify key factors. Our experiments using real-world UK accident data demonstrate that our proposed AISTGCN outperforms baseline models in outcome prediction with an accuracy of 0.8772. The computation time was reduced and the reliability of predictions was enhanced through screening for effective factors. Furthermore, DeepLIFT provides more reasonable explanations when explaining accidents of different severity, indicating that vehicle count significantly impacts. Our framework aids in developing effective safety measures to reduce accidents. [ABSTRACT FROM AUTHOR]

Buses are among the most accessible and frequently used means of transport. Due to its importance, road safety analysis is frequently conducted to reduce accidents. This paper studied the relationship between weather conditions and the causes of accidents to improve road safety, focusing on long-distance services between Madrid and Bilbao (Spain). We employed Latent Class Clustering (LCC) and Hierarchical Ordered Logit models to identify these factors' relationships. Additionally, Kaplan-Meier survival analysis was adopted to provide temporal insights into accident occurrences. The main results show a downward trend in accidents since 2019, with manoeuvres being the most frequent cause. LCC reveals that "manoeuvres and car invading lanes in the opposite direction" in "clear and cloudy weather" has the highest probability of occurrence (63%). The hierarchical-ordered logit model indicates that rainy weather significantly affects all accident causes. Kaplan-Meier survival analysis reveals a vertical initial decline in survival probability within the first ten days, emphasizing a high initial accident risk. The integrated approach used in this work provides a thorough understanding of accident hazards, which is its main contribution. By integrating LCC, Hierarchical Ordered Logit models and Kaplan-Meier survival analysis; we could offer a comprehensive and nuanced interpretation of the connection between weather and bus accidents. The findings highlight the need for rapid and sustained safety interventions, enhancing robustness and providing actionable insights for improving bus safety. [ABSTRACT FROM AUTHOR]

In order to determine the critical elements influencing the frequency and severity of two-wheeler-related traffic crashes, this study explores these incidents along eleven years in Vienna. Applying sophisticated multivariate statistical approaches, a comprehensive dataset is used and includes variables about rider demographics, weather conditions, vehicle features, and crash circumstances to reveal intricate correlations and interactions among these elements. Is there significant and distinctive difference based on gender and age with specific conditions under which crashes are occurring influencing different injury degree. Multiple regression undoubtedly points fields for action in statistically based findings providing the most important answer to this research: why there are so many crashes and what is leading cause of injured two-wheelers. The research yields insightful information that politicians and practitioners of road safety may use to improve two-wheeler safety regulations and lower the number of serious injuries and fatalities. Highlights: The stunning fact that just one rider in every fourteen may be uninjured Every second two-wheel rider sustain light injury and every eight severe Lane type, crash circumstance and consequence proves to be significant predictors for crash [ABSTRACT FROM AUTHOR]

Roads represent very important infrastructure and play a significant role in economic, cultural, and social growth. Therefore, there is a critical need for many researchers to model crash injury severity in order to study how safe roads are. When measuring the cost of crashes, the severity of the crash is a critical criterion, and it is classified into various categories. The number of vehicles involved in the crash (NVIC) is a crucial factor in all of these categories. For this purpose, this research examines road safety and provides a prediction model for the number of vehicles involved in a crash. Specifically, learning vector quantization (LVQ 2.1), one of the sub-branches of artificial neural networks (ANNs), is used to build a classification model. The novelty of this study demonstrates LVQ 2.1's efficacy in categorizing accident data and its ability to improve road safety strategies. The LVQ 2.1 algorithm is particularly suitable for classification tasks and works by adjusting prototype vectors to improve the classification performance. The research emphasizes how urgently better prediction algorithms are needed to handle issues related to road safety. In this study, a dataset of 564 crash records from rural roads in Calabria between 2017 and 2048, a region in southern Italy, was utilized. The study analyzed several key parameters, including daylight, the crash type, day of the week, location, speed limit, average speed, and annual average daily traffic, as input variables to predict the number of vehicles involved in rural crashes. The findings revealed that the "crash type" parameter had the most significant impact, whereas "location" had the least significant impact on the occurrence of rural crashes in the investigated areas. [ABSTRACT FROM AUTHOR]

• Confirmed the three factor 23-item MAD. • MAD was invariant across gender, age group, aggressive driving tendencies. • Men had higher anger means than women. • Younger drivers had higher anger than older drivers. • Anger tendencies were related to greater aggression. Introduction: Driver anger and aggression have been linked to crash involvement and injury outcomes. Improved road safety outcomes may be achieved through understanding the causes of driver anger, and interventions designed to reduce this anger or prevent it from becoming aggression. Scales to measure anger propensities will be an important tool in this work. The measure for angry drivers (MAD; Stephens et al., 2019) is a contemporary scale designed to measure tendencies for anger across three types of driving scenarios: perceived danger from others, travel delays, and hostility or aggression from other drivers. Method: This study aimed to validate MAD using a representative sample of Australian drivers, stratified across age, gender, and location. Participants completed a 10-minute online survey that included MAD, sought demographic information (age, gender, driving purpose, crash history), as well as the frequency of aggressive driving. Multigroup confirmatory factor analyses (MGCFA) assessed how stable the structure of the MAD was across drivers of different ages, gender, purposes for driving and those who do or do not display anger aggressively. MAD was invariant across all groups, showing that all drivers interpreted and responded to MAD in the same way. Results: A comparison of latent means showed anger tendencies were higher for men compared to women, for younger drivers compared to older drivers, and for those who drive mainly for work compared to those who mainly drive for other reasons. When controlling for driver factors, driving anger was associated with increased odds of being aggressive while driving. Practical Applications : Overall, this study demonstrated that MAD is an appropriate scale to measure anger tendencies and can be used to support interventions, and evaluation of interventions, to reduce anger and aggressive driving. [ABSTRACT FROM AUTHOR]

As many as 4,601 people were injured or killed on the roads in Norway in 2022. This number is too high and highlights the necessity of putting road safety on the agenda. The car industry today is represented by a vast increase in advanced technology for drivers to interact with, and knowing how and when to use this technology is crucial for safe driving. Thus, our research question was: Does the Norwegian driver training industry teach advanced driver assistance technology (ADAS) and touch screens in their driver training? A survey was distributed to 1,058 driving schools in Norway, resulting in 333 responses. The results showed that driving instructors spend considerably more effort on teaching inbuilt driving assist technologies that enhance driving safety than on those that are designed to enhance safety but that, if not understood well, could lead to hazardous outcomes. Suggestions on how to improve driver training and driving instructor education are made. Research and practical implications are presented to make sure that driving education is updated and aligned with technological advancements in the automobile industry and to ensure traffic safety for all road users. [ABSTRACT FROM AUTHOR]

Objective: This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems. Background: Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven. Method: Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods. Results: Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found. Conclusion: Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo. Application: The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces. [ABSTRACT FROM AUTHOR]

The United Nations Convention on the Rights of Persons with Disabilities and the Sustainable Development Goals stipulate that persons with disabilities have equal rights to access and safely use transport systems. The aim of the current study is to explore stakeholders' perspectives of the relationship and prioritization between safety and accessibility in the Swedish discussion of disability and transport. The data consist of interviews with 15 informants from the National Council for Disability and Transport and other key stakeholders. Reflexive thematic analysis led to identification of four themes: basis for priorities is a matter of governing; challenges to measuring and evaluating different values; importance of knowledge and building forums; and a universal system of accessibility and safety is a challenge. The results indicate the road ahead for a possible holistic and sustainable governance in the transport systems. However, how this will be put into practise is not yet defined. Points of interest: According to several regulations, persons with disabilities have equal rights to safety and accessibility in the transport system. However, there are challenges to combining different concepts and perspectives. Lack of mandates for institutions to address both safety and accessibility leads to goal conflicts and a risk that focuses become entrenched with clearly defined boundaries. There are challenges in evaluating different values and perspectives in relation to accessibility. A facilitating aspect for sustainability regarding safety and accessibility is to create opportunities and systems to allow for the exchange of knowledge. Integration of safety and accessibility may be encouraged by the use of existing sets of holistic approaches (i.e. Vision Zero and Universal Design). [ABSTRACT FROM AUTHOR]

A well-developed road network plays a crucial role in fostering social and economic progress within a region. However, road crashes resulting in massive injuries and deaths profoundly affect socioeconomic development. There is a need therefore to identify working approaches used in road safety strategic management which provide evidence and a foundation to achieve safer road transport. This may be achieved through a systematic literature review considering both state-of-the-art technologies and best practice. Such a review is presented in this paper. The review involved searching twenty-six bibliographic databases and twenty-four websites of road-related organizations. Following the EPPI-Reviewer methodology, the researchers identified 30 studies that demonstrated various methods employed in the strategy development process. The review highlighted the prevalence of information technology in crash data analysis, particularly concerning big data applications. Moreover, existing resource allocation methods primarily focus on local countermeasures prioritization and ranking based on benefit cost analysis. However, the review identified a gap in comprehensive crash database understanding, and only a few single-objective optimization methods have been developed for strategy development, while there is a need for data mining methods and multi-objective optimisation methods supported by expert knowledge. [ABSTRACT FROM AUTHOR]

As the elderly population grows, there is a greater concern for their safety on the roads. This is particularly important for elderly pedestrians who are more vulnerable to accidents. In Spain, one of the most aged countries in the world, the elderly accounted for 70% of all pedestrian deaths in 2019. In this study, the focus was on analysing the occurrence of elderly pedestrian-vehicle collisions in Spanish municipalities and how it is related to the built environment. The study used the hurdle negative binomial model to analyse the number of elderly and non-elderly pedestrian accidents per municipality in 2016-2019. The exploratory analysis showed that cities above 50,000 inhabitants were safer for the elderly, and larger provincial capitals had lower elderly pedestrian traffic accident rates. The occurrence of all pedestrian traffic accidents was linked to the socio-demographic features. For elderly pedestrians, land use was found to be influential, with a lower proportion of land covered by manufacturing and service activities linked to a smaller number of accidents. Results showed that improving road safety for older pedestrians may not necessarily compromise the situation for the rest of population. Hence, policymakers should focus on infrastructure improvements adapted to the needs of elderly pedestrians. [ABSTRACT FROM AUTHOR]

Background: The rapid growth of the digital economy has transformed various labor markets, including the food sector. The transient nature and the peculiar work environments of food delivery workers raise concerns about occupational safety and health (OSH). This study aims to better understand these issues by conducting a comprehensive exploratory survey on OSH among food delivery riders in Milan, Italy. Methods: The study employed structured interviews based on a validated questionnaire developed through a literature review, expert interviews, and input from riders and health and safety professionals. The survey was performed from July to November 2022 by interviewing riders at their main gathering points. Results: We interviewed 240 riders, 97% males, 81% less than 35 years old, and 83% with extra-European origin. Delivery was performed mainly by traditional bicycles (40%) and e-bikes (46%), with 44% working seven days per week and 23% working more than 8 hours daily. Overworking was significantly influenced by the type of contract, citizenship, and platform. Road accidents were reported by 39% of riders, influenced by type of vehicle, fatigue, and number of daily deliveries. Physical and verbal assaults (12% and 28%, respectively) were reported, as well as health-related issues, particularly musculoskeletal disorders. Conclusions: The findings underscore food delivery riders' complex challenges, emphasizing the need for targeted interventions. The study calls for collaborative efforts between policymakers, employers, OSH professionals, and stakeholders to enhance OSH standards and promote decent working conditions, aligning with the 2030 Agenda for Sustainable Development. [ABSTRACT FROM AUTHOR]

The relevance of studying the Vision Zero strategy is driven by the growing challenges in road traffic safety faced by modern societies. Despite advances in technology and infrastructure, the number of fatalities and injuries in road accidents remains a significant issue, requiring new approaches for resolution. In the context of increased attention to sustainable development and reducing the negative impact on the environment, Vision Zero offers innovative solutions aimed at harmonizing human safety with the ecological and economic demands of modern transportation systems. The Vision Zero strategy represents a modern and ambitious approach to road safety, aiming for the complete elimination of deaths and serious injuries on the roads. This study is dedicated to a comprehensive analysis of the main goals and principles of the Vision Zero strategy, as well as its application in the context of road safety within the European Union. The aim of the study is to thoroughly examine the key objectives of Vision Zero, such as achieving zero fatalities in traffic accidents and reducing serious injuries. Special attention is given to defining serious injuries and methods to reduce them, including both technical and infrastructural solutions. The paper discusses the core principles of the strategy, including ensuring the safety of vehicles, infrastructure, and driver behavior, as well as the principles of a systems approach and proactive risk management. The findings of the study confirm that the Vision Zero strategy is an effective tool for reducing the number of traffic accidents and serious injuries, due to its comprehensive approach to road safety. Specifically, the research shows that achieving Vision Zero's goals has a significant socio-economic impact, including reduced medical costs and improved quality of life for citizens. The realization of these goals also promotes the implementation of new technologies and innovations in the field of safety, making transportation systems more efficient and reliable. By analyzing and justifying the importance of achieving the Vision Zero goals, the study underscores the need for a comprehensive approach to managing road safety and demonstrates the potential of this strategy to ensure a safer and more sustainable transportation environment in the European Union. [ABSTRACT FROM AUTHOR]

Mountain highways are linearly complex, with extensive curves and high accident injury rates, how to improve driving safety is the key to traffic safety management on mountain highways, and it also meets the need for harmonious and sustainable development of the society. Therefore, this study investigates the effects of different guardrail color configurations on the driving behavior of different styles of drivers when driving on mountainous curves from the perspective of improving road aids – guardrails. A virtual reality experiment was designed using a driving simulator and VR technology, and 64 subjects were recruited to participate and complete the experiment. Drivers with non-adaptive driving styles (Reckless, Angry, Anxious) traveled at significantly higher speeds than subjects with adaptive driving styles (Cautious) on mountainous roads; drivers with Cautious styles had better lane-keeping ability when passing through different radii of curves as compared to non-adaptive drivers; and the red and yellow guardrails were more effective in decreasing the speeds at which drivers passed and in increasing the stability of lane-keeping. The results of the study show that the effectiveness of red and yellow guardrails is better, which provides a reference for the traffic management department to propose a standardized color setting of guardrails in mountainous areas, which is conducive to the development of more precise traffic management measures to reduce the occurrence of traffic accidents. [ABSTRACT FROM AUTHOR]

This study aimed to examine the prevailing driver seatbelt compliance at the Madina Zongo junction in Accra, Ghana. An unobtrusive observational survey was conducted from 6 to 8 am and 5 to 7 pm on weekdays at randomly designated locations near the junction. A total of 3,054 vehicles were observed throughout the observation period. The data collected were analyzed with SPSS version 26. Cross-tabulations and Pearson's Chi-square test were employed for thorough analysis. The study revealed an overall seatbelt compliance rate of slightly over half (54.1%) among drivers, with the following breakdowns in various vehicle categories: large buses (76.2%), medium buses (98.1%), minibuses (44.0%), private cars (70.5%), taxi/uber (53.0%), and trucks (41.1%). Notably, seatbelt compliance was higher among females at 98.4%, compared to males at 49.2%. The study also identified a correlation between driver's gender and vehicle type with seatbelt compliance. Conversely, no significant association was found between seatbelt compliance and either the time of day or day of the week. The study offers significant findings regarding seatbelt usage trends at the Madina Zongo junction in Accra. These insights provide a basis for recommending targeted interventions such as policy decisions, public health campaigns, communication strategies, better enforcement, and road safety training programs. These interventions aim to raise awareness of unsafe attitudes and behaviors among drivers to improve seatbelt compliance and ultimately enhance road safety for all road users. [ABSTRACT FROM AUTHOR]

Roadwork zones represent a hazardous place within the highway system, with higher crash rates and injury severity, often due to excessive speed and noncompliance with speed limits. On freeways, a common layout is to close a full roadway and to divert traffic onto the opposite roadway, with one lane closed to accommodate the redirected traffic, by driving through the median strip. In this scenario, the second chicane can be a hazardous point if it is not correctly signaled. This paper examines the need to install a speed monitoring display (SMD) before the returning chicane on a bypass. A two-phase study was conducted on a Spanish freeway where a roadway was temporarily closed. Two measurement points were established on the bypass, one in the middle and one at the end, prior to the return chicane through the median strip. During Phase 1, the portable SMD was installed and during Phase 2, it was removed. The average and the 85th percentile of the speed distribution at each point during both phases were compared. Additionally, mean difference tests were conducted and a speed prediction model was developed. With the SMD, drivers reduced their speed from the midpoint to the returning chicane, within the range of 7-10 km/h. Conversely, upon removal of the SMD, motorists increased their speed while driving through the bypass, resulting in excessive speed at the most hazardous point, the chicane leading back to the original roadway. The difference in mean speed between the two phases was 18 km/h at the returning chicane. In addition to the traffic calming measures implemented prior to entering roadwork zones on freeways, which are conveniently established in the standards; it is necessary to evaluate potentially dangerous areas of the layout and implement additional measures where required. Specifically, in the case of final chicanes of bypasses with reduced radii, it is recommended that a speed monitoring display be installed as a mandatory element in order to inform drivers of this challenging segment. Highway administrations around the world should maintain a SMD at the returning chicane of a bypass while roadworks last. [ABSTRACT FROM AUTHOR]

Studying the optimal profile shape and size of deceleration facilities suitable for low-speed environment roads under different speed control intervals. Simulation modeling of deceleration facilities with various profile shapes and sizes and for vehicles in different speed intervals was performed using the vehicle dynamics simulation software Carsim. The height jumped by a vehicle's wheels, the vertical force on the wheels, and the vertical acceleration of the vehicle were used as indicators of ride comfort and operational stability for the various deceleration facility profiles. stability and comfort were related to the contour of the deceleration facility. Vertical forces were positively related to vehicle jump height, but the jump heights of vehicles passing through deceleration mounds with different planes at the same speed were not significantly different with increasing height. When the vehicle is traveling slowly, the vertical impact force on the vehicle is not significantly related to the speed loss of the vehicle. Within the speed range of 20–60 km/h and profile heights of 3–10.5 cm, the effectiveness ratings of circular high width and parabolic were basically at level 2 and level 3, but the circular high width had a more stable jump height and was the best profile form, followed by sinusoidal and parabolic, then isosceles trapezoidal, and lastly conventional speed bumps. [ABSTRACT FROM AUTHOR]