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• 1778 valid pedestrians' street-crossing behaviors on 12 sidewalks across 9 urban road intersections were collected. • Five types of mobile use while crossing street were categorized: No use, voice call, screen gaze, screen gaze with operating, and listening to music with headphones. • Binomial logistic and Relogit regression models are used to analyze impact and safety risks of distraction on crossing behaviors. • The potential risk safety related to mobile phone usage during street crossing. The growing public concern over traffic safety hazards caused by pedestrians' distracted behavior, particularly related to mobile phone usage at pedestrian crossings. Through video recording of pedestrians' street-crossing behaviors on 12 sidewalks across 9 urban road intersections in Nanjing city, 1778 valid pedestrian samples were collected. The study categorizes mobile phone use during the crossing into five distinct types: no use, voice call, screen gaze, screen gaze with operation, and listening to music with headphones. Then, the effects of gender, age and companion conditions on the mobile phone during the crossing were examined by chi-square tests. Utilizing binomial logistic and Relogit regression models, the study analyzed the impact and safety risks of distraction on crossing behavior. Additionally, a random parameters (RP) logit model with heterogeneity in means was used to investigate the determinants affecting mobile phone usage. Notably, individuals aged 18 to 30 years were identified as the random parameter, while factors such as being under 18 years old, having a companion without interaction, having a companion with interaction, weekdays, sidewalk length, and the total number of other pedestrians were identified as fixed parameters. The study also presented significant variables affecting the probability of mobile phone usage through marginal effects, highlighting the potential safety risks associated with mobile phone usage during street crossing. These findings emphasize the need for heightened pedestrian safety awareness and a reduction in distracted behaviors to enhance overall traffic safety. [ABSTRACT FROM AUTHOR]

• A novel technique (Visual Hazardous Models) developed to identify the contributory factors causing the hazardous events. • Used feature selection models to find the key features. • Applied latent class clustering along with random parameter models to generate crash scenario(s) from homogeneous classes. • Marginal effects were calculated to know the magnitude of exploratory variable impact towards generated crash scenario(s) • Pedestrian Crosswalk Crashes happened on Tamil Nadu state during 2009–18 was used as use case to understand the model performance. Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009–2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 82% more chance of fatal/grievous injuries on expressways (posted speed limit: 100 km per hour) in annual hazardous zone locations. Working pedestrian age group (25–64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety. [ABSTRACT FROM AUTHOR]

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84–165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety. • A new approach for generating case-specific vehicle models from a single-view vehicle image. • Parameterized shape-matched buck model for car-to-pedestrian collision FE simulations. • Head injury analysis based on 12 vehicle models representing four vehicle categories. • Emphasized the necessity of using case-specific vehicle models in crash simulations. • High potential for vehicle structure optimization aiming at reducing pedestrian head injury. [ABSTRACT FROM AUTHOR]

• Pedestrian crash decrease within two years after implementing VZ corridor upgrades. • The impact of gradual implementation is estimated using project completion dates. • A before-and-after treatment and control spatial point process analysis is conducted. • VZ corridor upgrades contribute to significant crash reductions on treated sites. • Greater crash dispersion is observed at the local level along treated corridors. The long-term effects of the Vision-Zero (VZ) approach in Scandinavia are well documented. In contrast, information regarding the immediate effects of VZ at the starting phase upon gradual implementation is scarce. Taking New York City as the case study, we analyzed both the local and global effects of the Vision-Zero gradual implementation on pedestrian crashes in the early stage of implementation starting from 2014. The data analysis comprised 8,165 pedestrian injury crashes. Using location data, the crashes were matched to VZ infrastructure improvement location, start and completion dates. The experimental design included a treatment and two types of control conditions, and we controlled for well-known covariates including traffic exposure, land use, and risk-prone areas. We estimated a Geyer Saturation model and kernel density function for modeling the effect of Vision-Zero on crash intensity and dispersion two years before and after the implementation of Vision-Zero. The results reveal a significant global decrease of 6.1 % (p = 0.004) in pedestrian crash incidence in the treated sections compared with the control group two years after the treatment, and a greater dispersion of pedestrian injuries following the policy implementation. [ABSTRACT FROM AUTHOR]

• Investigates the pedestrian-vehicle crashes on the Louisiana interstate. • A Random Parameter Binary Logit (RPBL) model is used to address unobserved heterogeneity. • Pedestrian impairment and their action, weekend, driver aged 35–44 years, spring season increases fatal-severe injury risk. • Safety professionals can utilize the findings of this investigation. In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve 'unintended pedestrians', drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not 'typical pedestrians', a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014–2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35–44 years, and spring season. The interaction of 'pedestrian impairment' and 'weekend' was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the 'unintended pedestrians' about the crash scenarios on the interstate and reduce these unexpected incidents. [ABSTRACT FROM AUTHOR]

• Experimental study investigates inter-individual interactions in road crossings. • Multi-pedestrian VR simulator examines social interaction effects in road crossings. • Collective behavior diverts attention, influencing road safety. • Implications for preventive strategies and pedestrian safety awareness. The utility maximization theory, based on the rationality of human beings, has proven effective in modeling pedestrians' decision-making processes while crossing roads. However, there are still unexplained variations in crossing behavior, and deviations from the rational utility model frequently occur in real-life scenarios. This experimental study sheds new light on the presence of inter-individual interactions among pedestrians and the nature of collective behaviors during road crossings. The present study develops a multi-pedestrian virtual reality simulator specifically designed to investigate the impact of social interaction on pedestrians' eye-scanning patterns, perceived responses, crossing behaviors, and the associated crash risk. Our findings indicate that the collective behavior significantly influences pedestrians' behaviors by diverting their attention from essential eye-scanning patterns that reflect their cognitive processes. Pedestrians in pairs exhibit a higher tendency to fixate on each other, spend less time in the decision phase, walk at a slower pace during the crossing phase, and consequently face a higher degree of exposure to dangerous situations compared to when crossing alone. Encouraged by these findings on the effects of social interaction, we discuss preventive strategies to mitigate the negative impacts of collective behavior and foster pedestrians' safety awareness. [ABSTRACT FROM AUTHOR]

• Novel study of pedestrian crash severity based on traveled distance. • 25 % of severe crashes near homes; 50 % within 0.8434 miles. • Insights into spatial patterns of pedestrian crashes. Pedestrian safety remains a significant concern, with the growing number of severe pedestrian crashes resulting in substantial human and economic costs. Previous research into pedestrian crashes has extensively analyzed the influences of weather, lighting, and pedestrian demographics. However, these studies often overlook the critical spatial variables that contribute to pedestrian crashes. Our study aims to explore these overlooked spatial variables by examining the distance pedestrians travel before encountering a severe crash. This approach provides a supplementary perspective in safety analysis, emphasizing the importance of pedestrian movement patterns. The model considers various factors that may influence pedestrian traveled distance before being involved in a severe crash, such as weather conditions, lighting conditions, and pedestrian demographics. Ohio's pedestrian-involved crashes were gathered and analyzed as a case study. The results indicated that 50 % of fatal pedestrian crashes occurred within 0.84 miles of the pedestrians' residences. Moreover, it was shown that factors including lighting condition, pedestrian age, drug toxication, and the location at impact significantly influence the pedestrians traveled distance. These findings provide valuable insights into the spatial distribution of pedestrian crashes and shed light on the factors contributing to their severity. By understanding these relationships, policymakers and urban planners can design targeted interventions such as improving street lighting, implementing traffic calming measures, and developing safety awareness campaigns for specific age groups, to enhance pedestrian safety and reduce the incidence of severe crashes. [ABSTRACT FROM AUTHOR]

• The study devised a multidimensional indicator, "pedestrian exposure intensity," quantifying exposure to traffic hazards. • The VT-NET deep learning model was developed to forecast pedestrian crash risks using temporal and non-temporal risk features. • Concentrated high-risk areas, primarily located in the city center, show persistence for several hours and dissipate during late-night and early-morning hours. • These high-risk areas exhibited clustering on weekends, aligning with commercial zones, public spaces, and educational and medical facilities. Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities. [ABSTRACT FROM AUTHOR]

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian's intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning. • Using virtual reality technology to explore key factors in decision-making • Identifying distance and vehicle acceleration as key factors in pedestrian decisions • Discovering a mathematical link between pedestrian intent and kinematic variables • Analyzing driver-yielding behavior based on pedestrian intent and time-to-arrival [ABSTRACT FROM AUTHOR]

• Vehicle-pedestrian crash risk analysis in heterogeneous and disordered traffic conditions was analysed. • Compared of block maxima and peak over threshold sampling approaches for pedestrian crash risk analysis. • Post encroachment time was found to be adequate conflict measure for analysing pedestrian safety. • Group crossing, vehicle category, and speeds were influencing factors. Facilitating proactive pedestrian safety management, the application of extreme value theory (EVT) models has gained popularity due to its extrapolation capabilities of estimating crashes from their precursors (i.e., conflicts). However, past studies either applied EVT models for crash risk analysis of autonomous vehicle–pedestrian interactions or human-driven vehicle–pedestrian interactions at signalised intersections. However, our understanding of human-driven vehicle–pedestrian interactions remains elusive because of scant evidence of (i) EVT models' application for heterogeneous traffic conditions, (ii) appropriate set of determinants, (iii) which EVT approach to be used, and (iv) which conflict measure is appropriate. Addressing these issues, the objective of this study is to investigate pedestrian crash risk analysis in heterogeneous and disordered traffic conditions, where drivers do not follow lane disciplines. Eleven-hour video recording was collected from a busy pedestrian crossing at a midblock location in India and processed using artificial intelligence techniques. Vehicle-pedestrian interactions are characterised by two conflict measures (i.e., post encroachment time and gap time) and modelled using block maxima and peak over threshold approaches. To handle the non-stationarity of pedestrian conflict extremes, several explanatory variables are included in the models, which are estimated using the maximum likelihood estimation procedure. Modelling results indicate that the EVT models provide reasonable estimates of historical crash records at the study location. From the EVT models, a few key insights related to vehicle–pedestrian interactions are as follows. Firstly, a comparison of EVT models shows that the peak over threshold model outperforms the block maxima model. Secondly, post encroachment time conflict measure is found to be appropriate for modelling vehicle–pedestrian interactions compared to gap time. Thirdly, pedestrian crash risk significantly increases when they interact with two-wheelers in contrast with interactions involving buses where the crash risk decreases. Fourthly, pedestrian crash risk decreases when they cross in groups compared to crossing individually. Finally, pedestrian crash risk is positively related to average vehicle speed, pedestrian speed, and five-minute post encroachment time counts less than 1.5 s. Further, different block sizes are tested for the block maxima model, and the five-minute block size yields the most accurate and precise pedestrian crash estimates. These findings demonstrate the applicability of extreme value analysis for heterogeneous and disordered traffic conditions, thereby facilitating proactive safety management in disordered and undisciplined lane conditions. [ABSTRACT FROM AUTHOR]

• Automatically collected conflict data can be used for safety analysis of vulnerable road users. • Reliability of automatically generated surrogate indicators in predicting confirmed conflicts is evaluated. • Synthetic data augmentation techniques are used to address class imbalance in data, enhancing model robustness. • Significant variables that contribute to automatic detection of bicycle and pedestrian conflicts is analyzed. Vulnerable Road Users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex dynamics, emphasizing the need to understand how these road users interact with motor vehicles and deploy evidence-based safety countermeasures. Given the infrequency of VRU-related crashes, identifying conflicts between VRUs and motorized vehicles as surrogate safety indicators offers an alternative approach. Automatically detecting these conflicts using a video-based system is a crucial step in developing smart infrastructure to enhance VRU safety. However, further research is required to enhance its reliability and accuracy. Building upon a study conducted by the Pennsylvania Department of Transportation (PennDOT), which utilized a video-based event monitoring system to assess VRU and motor vehicle interactions at fifteen signalized intersections in Pennsylvania, this research aims to evaluate the reliability of automatically generated surrogates in predicting confirmed conflicts without human supervision, employing advanced data-driven models such as logistic regression and tree-based algorithms. The surrogate data used for this analysis includes automatically collectable variables such as vehicular and VRU speeds, movements, post-encroachment time, in addition to manually collected variables like signal states, lighting, and weather conditions. To address data scarcity challenges, synthetic data augmentation techniques are used to balance the dataset and enhance model robustness. The findings highlight the varying importance and impact of specific surrogates in predicting true conflicts, with some surrogates proving more informative than others. Additionally, the research examines the distinctions between significant variables in identifying bicycle and pedestrian conflicts. These findings can assist transportation agencies to collect the right types of data to help prioritize infrastructure investments, such as bike lanes and crosswalks, and evaluate their effectiveness. [ABSTRACT FROM AUTHOR]

The interactions of motorised vehicles with pedestrians have always been a concern in traffic safety. The major threat to pedestrians comes from the high level of interactions imposed in uncontrolled traffic environments, where road users have to compete over the right of way. In the absence of traffic management and control systems in such traffic environments, road users have to negotiate the right of way while avoiding conflict. Furthermore, the high level of movement freedom and agility of pedestrians, as one of the interactive parties, can lead to exposing unpredictable behaviour on the road. Traffic interactions in uncontrolled mixed traffic environments will become more challenging by fully/partially automated driving systems' deployment, where the intentions and decisions of interacting agents must be predicted/detected to avoid conflict and improve traffic safety and efficiency. This study aims to formulate a game-theoretic approach to model pedestrian interactions with passenger cars and light vehicles (two-wheel and three-wheel vehicles) in uncontrolled traffic settings. The proposed models employ the most influencing factors in the road user's decision and choice of strategy to predict their movements and conflict resolution strategies in traffic interactions. The models are applied to two data sets of video recordings collected in a shared space in Hamburg and a mid-block crossing area in Surat, India, including the interactions of pedestrians with passenger cars and light vehicles, respectively. The models are calibrated using the identified conflicts between users and their conflict resolution strategies in the data sets. The proposed models indicate satisfactory performances considering the stochastic behaviour of road users – particularly in the mid-block crossing area in India – and have the potential to be used as a behavioural model for automated driving systems. • Video analysis of interactions between pedestrians and different vehicle types. • Formulating a methodology to predict road users' strategies to avoid conflicts. • Considering various factors affecting users' decisions in traffic interactions. • Implications for designers of automated driving systems are discussed. [ABSTRACT FROM AUTHOR]

• Children and adults waiting longer to enter the roadway when vehicles decelerated abruptly than gradually. • When vehicles decelerated gradually, both children and adults in the early eHMI onset condition entered the roadway well before vehicles came to a stop. • Children and adults in the late eHMI onset condition waited to enter the roadway until vehicles came to a stop. • Children were heavily influenced by the late eHMI signal, crossing later when the eHMI signal appeared late and the vehicle decelerated either gradually or abruptly compared to the control condition. • Without an eHMI signal, children show a concerning overreliance on gradual vehicle deceleration to judge yielding intent. How autonomous vehicles (AVs) communicate their intentions to vulnerable road users (e.g., pedestrians) is a concern given the rapid growth and adoption of this technology. At present, little is known about how children respond to external Human Machine Interface (eHMI) signals from AVs. The current study examined how adults and children respond to the combination of explicit (eHMI signals) and implicit information (vehicle deceleration) to guide their road-crossing decisions. Children (8- to 12-year-olds) and adults made decisions about when to cross in front of a driverless car in an immersive virtual environment. The car sometimes stopped, either abruptly or gradually (manipulated within subjects), to allow participants to cross. When yielding, the car communicated its intent via a dome light that changed from red to green and varied in its timing onset (manipulated between subjects): early eHMI onset, late eHMI onset, or control (no eHMI). As expected, we found that both children and adults waited longer to enter the roadway when vehicles decelerated abruptly than gradually. However, adults responded to the early eHMI signal by crossing sooner when the cars decelerated either gradually or abruptly compared to the control condition. Children were heavily influenced by the late eHMI signal, crossing later when the eHMI signal appeared late and the vehicle decelerated either gradually or abruptly compared to the control condition. Unlike adults, children in the control condition behaved similarly to children in the early eHMI condition by crossing before the yielding vehicle came to a stop. Together, these findings suggest that early eHMI onset may lead to riskier behavior (initiating crossing well before a gradually decelerating vehicle comes to a stop), whereas late eHMI onset may lead to safer behavior (waiting for the eHMI signal to appear before initiating crossing). Without an eHMI signal, children show a concerning overreliance on gradual vehicle deceleration to judge yielding intent. [ABSTRACT FROM AUTHOR]

• Inception-v3 deep learning model has been developed to predict pedestrian-involved crash severity using data collected over five years (2016–2021) from Louisiana. • The Boruta algorithm was applied to determine the importance of variables and investigate the contributing factors to pedestrian-involved crash severity. • Factors associated with pedestrian-involved crash severity include pedestrian gender, pedestrian and driver impairment, posted speed limits, alcohol involvement, pedestrian age, visibility obstruction, roadway lighting conditions, and both pedestrian and driver conditions, including distraction and inattentiveness. • Deep learning models with data balancing using both over and under sampling can achieve superior prediction performance compared to models without data balancing. • Comparative analysis and McNemar's tests revealed the superiority of the Inception-v3 deep learning model over statistical and conventional machine learning models. This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016–2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into three categories: fatal, injury, and no injury. The Boruta algorithm was applied to determine the importance of variables and investigate contributing factors to pedestrian crash severity, revealing several associated aspects, including pedestrian gender, pedestrian and driver impairment, posted speed limits, alcohol involvement, pedestrian age, visibility obstruction, roadway lighting conditions, and both pedestrian and driver conditions, including distraction and inattentiveness. To address data imbalance, the study employed Random Under Sampling (RUS) and the Synthetic Minority Oversampling Technique (SMOTE). The DeepInsight technique transformed numeric data into images. Subsequently, five crash severity prediction models were developed with Inception-v3, considering various scenarios, including original, under-sampled, over-sampled, a combination of under and over-sampled data, and the top twenty-five important variables. Results indicated that the model applying both over and under sampling outperforms models based on other data balancing techniques in terms of several performance metrics, including accuracy, sensitivity, precision, specificity, false negative ratio (FNR), false positive ratio (FPR), and F1-score. This model achieved prediction accuracies of 93.5%, 77.5%, and 85.9% for fatal, injury, and no injury categories, respectively. Additionally, comparative analysis based on several performance metrics and McNemar's tests demonstrated that the predictive performance of the Inception-v3 deep learning model is statistically superior compared to traditional machine learning and statistical models. The insights from this research can be effectively harnessed by safety professionals, emergency service providers, traffic management centers, and vehicle manufacturers to enhance their safety measures and applications. [ABSTRACT FROM AUTHOR]

• A framework for assessing corridor-wide pedestrian safety is developed. • Autonomous vehicle sensor data was utilized to extract pedestrian-vehicle conflicts. • Extreme value theory models with block maxima and peak over threshold sampling approaches have been tested. • The proposed framework provides reliable estimates of pedestrian crash risks. Pedestrians are a vulnerable road user group, and their crashes are generally spread across the network rather than in a concentrated location. As such, understanding and modelling pedestrian crash risk at a corridor level becomes paramount. Studies on pedestrian crash risks, particularly with the traffic conflict data, are limited to single or multiple but scattered intersections. A lack of proper modelling techniques and the difficulties in capturing pedestrian interaction at the network or corridor level are two main challenges in this regard. With autonomous vehicles trialled on public roads generating massive (and unprecedented) datasets, utilising such rich information for corridor-wide safety analysis is somewhat limited where it appears to be most relevant. This study proposes an extreme value theory modelling framework to estimate corridor-wide pedestrian crash risk using autonomous vehicle sensor/probe data. Two types of models were developed in the Bayesian framework, including the block maxima sampling-based model corresponding to Generalised Extreme Value distribution and the peak over threshold sampling-based model corresponding to Generalised Pareto distribution. The proposed framework was applied to autonomous vehicle data from Argoverse—a Ford Motors subsidiary. This autonomous vehicle fleet of Agro AI (owner of Argoverse dataset) is equipped with two 64 beams synchronised LiDAR sensors, a cluster of seven high-resolution cameras, and a pair of stereo-vison high-resolution cameras to capture surrounding road users' information within a range of 200 meters. A subset of the Argoverse dataset, focussing on an arterial corridor in Miami, USA, was used to extract pedestrian and vehicle trajectories. From these trajectories, vehicle–pedestrian conflicts were identified and measured using post encroachment time. The non-stationarity of extremes was captured by vehicle volume, pedestrian volume, average vehicle speed, and average pedestrian speed in the extreme value model. Both block maxima and peak over threshold sampling-based models were found to provide a reasonable estimate of historical pedestrian crash frequencies. Notably, the block maxima sampling-based model was more accurate than the peak over threshold sampling-based model based on mean crash estimates and confidence intervals. This study demonstrates the potential of using autonomous vehicle sensor data for network-level safety, enabling an efficient identification of pedestrian crash risk zones in a transport network. [ABSTRACT FROM AUTHOR]

Safety is a critical concern for autonomous vehicles (AVs). Current testing approaches face challenges in simultaneously meeting the requirements of being valid, safe, and fast. To address these challenges, the silent testing approach that tests functions or systems in the background without interfering with driving is motivated. Building upon our previous research, this study first extends the method to specifically address the validation of AV perception, utilizing a lane marking detection algorithm (LMDA) as a case study. Second, field experiments were conducted to investigate the method's effectiveness in validating AV systems. For both studies, an architecture for describing the working principle is presented. The efficacy of the method in evaluating the LMDA is demonstrated through the use of adversarial images generated from a dataset. Furthermore, various scenarios involving pedestrians crossing a road under different levels of criticality were constructed to gain practical insights into the method's applicability for AV system validation. The results show that corner cases of the LMDA are successfully identified by the given evaluation metrics. Furthermore, the experiments highlight the benefits of employing multiple virtual instances with different initial states, enabling the expansion of the test space and the discovery of unknown unsafe scenarios, particularly those prone to false-positive objects. The practical implementation and systematic discussion of the method offer a significant contribution to AV safety validation. • Architectures for evaluating AV perception and systems at runtime are introduced. • The study shows the method's efficacy in identifying AV perception corner cases. • The method's real-world application in AV safety validation is investigated. [ABSTRACT FROM AUTHOR]

• Vehicle-pedestrian crashes were extracted from CIDAS database. • Crash causation chains were constructed by DREAM framework. • Crash contributing factors and active safety requirements were discovered. • An active safety system with sensors and the AEB algorithm was designed. • The three-stage AEB was calibrated to reduce pedestrian crashes by more than 90%. Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers' own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g , 0.3 g , and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle–pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems. [ABSTRACT FROM AUTHOR]

Road safety has become a global concern but its impact in low- and middle-income countries is widespread mainly due to lack of appropriate crash database system and under-reporting. In this context, the primary objective of this paper is to provide a scalable framework for unveiling pedestrians' perceived road safety that can also be applied in regions where accessible crash data are limited or near-crashes are left unreported. In the first step of our methodology, a deep learning architecture-based semantic segmentation model (HRNet+OCR) is trained using labeled Google Street View (GSV) images from specific study areas in Dhaka, Bangladesh, which facilitates the identification of both man-made components (such as roads, sidewalks, buildings, and vehicles) and natural elements (including trees and sky). The developed model showed excellent performance in identifying different features in an image by achieving high precision (0.95), recall (0.97), F1-score (0.96), and intersection over union (IoU) (91.86). Secondly, a group of trained raters scored the perceived road safety on an ordinal scale from 0 to 10 (extremely unsafe to extremely safe to walk in terms of road crashes) by assessing the GSV images. Then, several regression models have been used on features extracted from GSV images, and socio-demographic factors (i.e., population density, and relative wealth index) to estimate the perceived road safety, and random forest regression model was found to perform the best. Further, Shapley Additive Explanations (SHAP), a model-agnostic technique has been used for examining feature importance by computing the contribution of each feature to the random forest regression model output. The results show that sidewalk, road, population density, wall, and relative wealth index have higher impact on determining the perceived road safety rating. Additionally, the results of t-tests between the average perceived road safety scores for crash-prone and non crash-prone areas revealed the existence of significant differences. This study also provides perceived road safety rating map on a neighborhood scale, which can be a useful visualization tool for policy-makers and practitioners to identify the road safety deficiencies at specific locations, and formulate appropriate and strategic countermeasures to improve pedestrians' road safety. • A scalable framework for assessing pedestrian's road safety perception. • Unveiling natural and built-environment factors affecting road safety perception. • Semantic segmentation model performed well to identify man-made and natural features. • Differences exist between perceived road safety and reported crash data. • Perceived road safety rating map will aid traffic safety stakeholders. [ABSTRACT FROM AUTHOR]

• Introduces "Safe Space" concept: validates with real-world data for pedestrians and e-bicyclists in urban crosswalks. • Explores impact of the number and speed of individuals on the morphology of the safe space. • Proposes novel methods to identify crowd congestion levels and traffic conflicts using safe space. Despite numerous theoretical and empirical studies exploring the spatial needs of road users, a significant gap remains in validating these findings with extensive real-world data sets. This study presents the idea of "safe spaces," outlining the buffer zones that both walkers and e-bicyclists maintain when crossing streets, while also taking safety and psychological well-being into consideration. We used drones to gather the study's trajectory data at three significant crossings in Xi'an, China. Multi-coordinate system transformation enabled us to determine the relative positions of individuals and moving objects within their visual domain. Relative position frequency heat maps were generated to explore safe distance demands in different directions. The safety space was then fitted using the least squares method. Our research demonstrates that whereas e-bicyclists maintain semi-elliptical safe spaces at street crossings, walkers maintain semi-circular safe spaces, and the sizes of these zones increase in direct proportion to their relative speeds. However, the safe space size oscillates within a defined range above a particular speed threshold. Notably, e-bicyclists require larger safety distances forward and are more sensitive to speed variations. Our knowledge of the dynamics of safe spaces for walkers and e-bicyclists at street crossings is improved by this work, with emphasis on the role of speed and pedestrian numbers in shaping these spaces. The incorporation of real-world data from drone footage significantly strengthens the validity and reliability of our findings, bridging a crucial empirical gap in the existing literature. Additionally, this paper introduces a crowding coefficient based on safe space and proposes a new method using safe space to aid traffic conflict metrics PET, providing valuable insights for identifying crowd congestion levels and capturing traffic conflict events. The practical implications of our findings extend to urban planning, traffic management, and safety of vulnerable road users. Ultimately, this research contributes to the development of safer and more efficient urban environments for all road users. [ABSTRACT FROM AUTHOR]

• A real-road environment experiment was designed. • Smartwatch jeopardizes children pedestrians' ability to safely cross the road. • Street-crossing behaviors, visual attention allocation, visual data visualization were evaluated. • Visual distractions have greatest effects on the crossing behavior of children pedestrians. • Children showed more cautious street-crossing behavior at signalized intersection compared to unsignalized intersection. The use of smartwatches is facilitating the lives of children. However, smartwatch usage while crossing a street may lead to distractions and traffic safety hazards. The purpose of this study was to investigate the effects of smartwatch usage on the street-crossing behavior and visual strategies of children. Twenty-two children were recruited, and 220 street-crossing experiments were performed in real road environments, including at signalized and unsignalized intersections. ANOVA and Kruskal–Wallis H test calibrations were used to analyze the street-crossing behavior between the control group (who performed a task without smartwatch distraction) and the experimental group (who performed four different tasks with smartwatch distraction), and to visualize and compare the visual behavior data of children crossing the street. The results showed that children wearing smartwatches crossed the street more slowly and had a relatively narrow range of visual search, focusing on only a small area of the zebra crossing around them and the crosswalk area within their sight, whereas children who did not wear smartwatches additionally focused on areas with dangerous oncoming traffic on the left and right sides. Children wearing smartwatches were under various mental workloads and distraction levels as they performed the various distracting tasks, with listening to stories being the least distracting and least intensive, followed by answering phone calls and viewing images; a simple arithmetic operation was the most distracting and intensive task. Children while performing the distracting tasks showed a lower street-crossing speed and scanned left and right fewer times; viewing images and performing the simple arithmetic task also increased their fixation time on the smartwatches, further affecting their street-crossing behavior and thus increasing the risk of collision. At signalized intersections, children paid more attention to hazardous oncoming traffic areas and exhibited cautious crossing behaviors compared with that at unsignalized intersections. This study elucidated the characteristics of the effects of smartwatch usage on the street-crossing behavior of children and the influence pattern of the visual attention allocation and revealed the hazards of smartwatch usage on the street-crossing safety. It can contribute to improving the safety cognition of children distracted by smartwatch usage and reducing the occurrence of distracting behaviors while crossing streets. The results can provide a theoretical basis for scientific interventions and safety improvement measures. [ABSTRACT FROM AUTHOR]

• This study aims to identify the crash scenarios involving distracted pedestrians. • The narratives of crash reports were reviewed to introduce distracting activities. • Association rule mining was applied to reveal the grouping of crash risk factors. • Intersections were found to be vulnerable for pedestrians using electronic devices. • Distracted pedestrians were at a higher risk of serious injuries in rural settings. The concept of distracted pedestrians and its impact on highway safety has gained increasing attention in recent years. However, studies focusing exclusively on distracted pedestrian crashes are less pervasive than distracted driving. In addition, most prior studies investigate the harmful effect of cellphone usage while walking, without considering other forms of pedestrian distraction. Also, the existing literature provides limited knowledge on comprehending the affinities between pedestrian distraction and safety consequences. This study aims to reveal the chain of contributing factors involved in distracted pedestrian crashes, considering both pedestrian severity levels and specific distraction-related tasks. Ten years (2010–2019) of related crashes were extracted from the Louisiana Department of Transportation and Development (LADOTD) database, and association rule mining (ARM) was applied to identify the meaningful crash patterns. Different distracting activities of pedestrians were introduced from the narratives of police-investigated crash reports. The study findings exhibit the complex nature of distracted pedestrian crashes by highlighting the intricate relationships between risk factors. On road segments, distracted male pedestrians aged 41–64 were more likely to be fatal/severely injured in dark-not-lighted conditions. Crashes involving pedestrians using electronic devices were often found at intersections. Distractions caused by pets, persons, or objects were strongly associated with crossing segments in rural settings. In-person conversation while standing on roadways in urban residential locations without traffic controls was found to increase vulnerability. Working on vehicles while wearing dark clothes and in dark-not-lighted conditions was identified as an influential factor in crash occurrence. Moreover, careless or inattentive actions of pedestrians while playing on the road segments were associated with a high likelihood of crashes. These study outcomes are crucial in uncovering the coexisting crash characteristics related to distracted pedestrians, which can be helpful in targeting and developing effective educational, design, and enforcement strategies to improve pedestrian safety. [ABSTRACT FROM AUTHOR]

• Injury severity of pedestrian walking-along-the-road crashes in different lighting conditions is explored. • Correlated and uncorrelated random parameters logit models are applied for the analysis. • Out-of-sample predictions underscored the shifts in factor effects between different lighting conditions. • Significant variation in factor effects on injury severity outcomes in different lighting conditions was established. Pedestrians walking along the road's edge are more exposed and vulnerable than those on designated crosswalks. Often, they remain oblivious to the imminent perils of potential collisions with vehicles, making crashes involving these pedestrians relatively unique compared to others. While previous research has recognized that the surrounding lighting conditions influence traffic crashes, the effect of different lighting conditions on walking-along-the-road pedestrian injury severity outcomes remains unexplored. This study examines the variations in the impact of risk factors on walking-along-the-road pedestrian-involved crash injury severity across various lighting conditions. Preliminary stability tests on the walking-along-the-road pedestrian-involved crash data obtained from Ghana revealed that the effect of most risk factors on injury severity outcomes is likely to differ under each lighting condition, warranting the estimation of separate models for each lighting condition. Thus, the data were grouped based on the lighting conditions, and different models were estimated employing the random parameter logit model with heterogeneity in the means approach to capture different levels of unobserved heterogeneity in the crash data. From the results, heavy vehicles, shoulder presence, and aged drivers were found to cause fatal pedestrian walking-along-the-road severity outcomes during daylight conditions, indicators for male pedestrians and speeding were identified to have stronger associations with fatalities on roads with no light at night, and crashes occurring on Tuesdays and Wednesdays were likely to be severe on lit roads at night. From the marginal effect estimates, although some explanatory variables showed consistent effects across various lighting conditions in pedestrian walking-along-the-road crashes, such as pedestrians aged < 25 years and between 25 and 44 years exhibited significant variations in their impact across different lighting conditions, supporting the finding that the effect of risk factors are unstable. Further, the out-of-sample simulations underscored the shifts in factor effects between different lighting conditions, highlighting that enhancing visibility could play a pivotal role in significantly reducing fatalities associated with pedestrians walking along the road. Targeted engineering, education, and enforcement countermeasures are proposed from the interesting insights drawn to improve pedestrian safety locally and internationally. [ABSTRACT FROM AUTHOR]

• Need to further study the interaction between cyclists, pedestrians, and automated vehicles. • Understand various factors associated with bicyclists' and pedestrians' perception. • Associated with higher likelihood of AVs' safety. • AVs' safety potential to reduce traffic injuries and fatalities. • Analysis with higher likelihood of feeling safe sharing the road with AVs. Presently, technology innovations are disrupting the status quo and changing the way people travel. In an effort to enhance safety, ease driving tasks, and attract car buyers, automobile manufacturers are offering new vehicle automation technologies. As these vehicle technologies become more automated, navigation around and interactions with pedestrians and bicyclists in complex travel environments becomes more challenging. With people being less predictable and less identifiable than other machines, these technologies can pose safety concerns for all users. In light of this, there is a need to further study the interaction between cyclists, pedestrians, and automated vehicles. In 2019, Bike Pittsburgh (BikePGH) conducted a survey of autonomous vehicles (AVs) in Pittsburgh, Pennsylvania to understand the perception of bicyclists and pedestrians when sharing the road with AVs. This study used the data collected by BikePGH to understand various factors associated with bicyclists' and pedestrians' perception of safety when sharing the road with AVs. Bayesian Networks (BNs) were used to learn the probabilistic interrelationship among AVs' aspects. BN results revealed that familiarity with the technology behind AVs, feeling safe while sharing the road with AVs, and using Pittsburgh's public streets as a proving ground for AVs were associated with higher likelihood of AVs' safety potential to reduce traffic injuries and fatalities. On the other hand, feeling safe while sharing the road with human-driven cars was associated with lower likelihood of AVs' safety potential to reduce traffic injuries and fatalities. Furthermore, the BN model predicted that the experience of sharing the road with AVs while riding a bicycle or walking, familiarity with the technology behind AVs, and using Pittsburgh's public streets as a proving ground for AVs were associated with higher likelihood of feeling safe sharing the road with AVs. The joint analysis of the variable showed the highest predicted probabilities of 95% and 86%, respectively for AVs' potential to reduce traffic injuries and fatalities and for feeling safe sharing the road with AVs. The practical application of this study is presented along with recommendations to operators, city engineers, and planner. [ABSTRACT FROM AUTHOR]

• Vehicle-pedestrian conflicts were clustered into different risk levels using a K-means algorithm based on spatial/temporal proximity and safety surrogate measures (SSMs). • The study developed an algorithm utilizing critical thresholds of SSMs to suggest safe evasive actions for drivers in different space/time contexts, aiding in preventing pedestrian collisions. • Conflict patterns were observed between SUAs and UMCAs, as well as between UAs and MCAs, with varying severity levels of conflicts. • Road users in MCAs displayed more risk-averse behavior compared to UAs, and the likelihood of conflicts differed across different area types. • The findings contribute to policy development based on the Safe System Approach (SSA), improving driver attention and reducing non-motorist collisions. This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs). By determining critical thresholds of spatial/temporal proximity-based safety surrogate techniques, vehicle–pedestrian conflicts are clustered through a K-means algorithm into different risk levels based on drivers' evasive actions in different areas. The results of the data analysis indicate that changing lanes is the key evasive action employed by drivers to avoid pedestrian crashes in SUAs and UMCAs, while in UAs and MCAs, drivers rely on soft evasive actions, such as deceleration. Moreover, critical thresholds for several Safety Surrogate Measures (SSMs) reveal similar conflict patterns between SUAs and UMCAs, as well as between UAs and MCAs. Furthermore, this study develops and delivers a pseudo-code algorithm that utilizes the critical thresholds of SSMs to provide tangible guidance on the appropriate evasive actions for drivers in different space/time contexts, aiming to prevent collisions with pedestrians. The developed research methodology as well as the outputs of this study could be potentially useful for the development of a driver support and assistance system in the future. [ABSTRACT FROM AUTHOR]

• Vehicle-pedestrian conflict scenarios were extracted from SH-NDS data. • The characteristics of vehicle–pedestrian conflicts were analyzed based on SH-NDS. • The RSS model was calibrated for vehicle–pedestrian scenarios. • RSS-embedded AVs were compared with human driving vehicles and AEB-embedded AVs. • RSS-embedded AVs performed best in safety improvement. The severity of vehicle–pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle–pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB's Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle–pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle–pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians. [ABSTRACT FROM AUTHOR]

• Investigate the influence of adopting the target speed concept on pedestrian, bike, and speeding related crashes. • Probe-vehicle data, traffic characteristics, geometric features, and land use attributes were utilized to develop crash prediction models. • Quantify the impacts of target speed on traffic safety considering context categories and conclude the potential recommendations to lower crash frequency. • Suggested countermeasures to lower vulnerable road users' and speeding related crashes. • Improve the mobility and safety for all users in different context classifications. This research aims to investigate the influence of adopting the target speed concept on different types of crashes including pedestrian, bike, and speeding-related crashes. The Target speed is the highest speed that vehicles should operate on a roadway segment in a specific context. Based on the reviewed literature, this is the first study to investigate the relationship between target speed and crash frequency. Hence, big data including probe-vehicle data, traffic characteristics, geometric features, and land use attributes were utilized to develop crash prediction models. The main contributions of this research are to quantify the impacts of target speed on traffic safety considering context categories and to conclude the potential recommendations to lower different types of crashes. The 85th percentile speed was calculated and utilized in the developed models. Three crash prediction models were developed for pedestrian, bike, and speeding-related crashes. They were used in the analysis to quantify the impact of adopting target speed on different crash types. The results showed a significant reduction in the three crash types when using the target speed. Most of the improvements took place in three context categories: C3C: Suburban Commercial Segments, C3R: Suburban Residential Segments, and C4: Urban General Segments. Hence, this research recommends adopting target speed specifically in urban and suburban areas. Further, it suggests considering some measures to lower vulnerable road users' and speeding-related crashes. Following the recommendations of this research would help to reduce different types of crash frequency, hence, improving the mobility and safety for all users in different context classifications. [ABSTRACT FROM AUTHOR]

• Conducted a video-based experiment to explore pedestrians' critical gap acceptance. • Investigated the impact of eHMI on pedestrians' crossing decisions. • Further compared the impact of eHMI on pedestrians engaged in simple vs. dual tasks. • Identifies potential inattentional blindness to eHMI. • Pedestrians are not impacted by eHMI when engaging in a secondary task while crossing. In road traffic, mental overload often leads to a failure to notice new and distinctive stimuli. Such phenomenon is known as 'inattentional blindness'. Safe and efficient interaction between automated vehicles (AVs) and pedestrians is expected to rely heavily on external human-machine interfaces (eHMIs), a tool AVs are equipped with to communicate their intentions to pedestrians. This study seeks to explore the phenomenon of 'inattentional blindness' in the context of pedestrian-AV interactions. Specifically, the aim is to understand the effects of a warning eHMI on pedestrians' crossing decisions when they are engaged in a secondary task. In an experiment study with videos of pedestrian crossing scenarios filmed from the perspective of the crossing pedestrian, participants had to decide the latest point at which they would be willing to cross the road in front of an AV with an eHMI vs. an AV without an eHMI. Participants were also asked to predict the future behavior of the AV. 125 female and 9 male participants aged between 18 and 25 completed the experiment and a follow-up questionnaire. It was found that the presence of a warning eHMI on AVs contributes to a clearer understanding of pedestrians' inferences about the intention of AVs and helps deter late and dangerous crossing decisions made by pedestrians. However, the eHMI fail to help pedestrians avoid such decisions when they face a high mental workload induced by secondary task engagement. [ABSTRACT FROM AUTHOR]

• Reduction of warnings immediately after activation of driver assistance system. • No habituation effect was found. • Buses had less significant reductions of warnings compared to HGVs. • Good correlation between conflict and crash data. • Up to 200 fatalities (pedestrians and cyclists) could be saved annually in the EU. The risk being severely or fatally injured in crashes with heavy goods vehicle and buses is much higher compared to other vehicles. Especially vulnerable road users such as pedestrian and cyclists are at high risk. In the European Union 4600 pedestrian and more than 2000 cyclists were killed in 2019. 18% of the fatalities were counted in crashes with heavy goods vehicles or buses. Blind spot situations and driver inattention or distraction are causing and contributing crash factors. Driver assistance systems are intended to support drivers and might have a positive effect on the crash avoidance. The objective of the study is the analysis of the change of driver behaviour in heavy goods vehicles and buses due to a aftermarket blind spot monitoring system. In a naturalistic driving study 15 heavy goods vehicles and five buses were equipped with a blind spot monitoring system and data were collected over a period of two years. The results revealed that the system would reduce the number of warnings with vulnerable road users by one third for heavy goods vehicles and 10% for buses. Up to 200 lives annually could potentially be saved with the analysed system, on the assumption that the collision warnings correlate directly with crashes. [ABSTRACT FROM AUTHOR]

• A driver-pedestrian encounter dataset with 348 interactions was extracted from a large naturalistic driving data collection effort. • Mutual information was used to quantify implicit communication between pedestrian behaviors and driver kinematics. • The patterns mutual information conveyed by vehicle kinematics differed across measures (acceleration, speed, and standard deviation of speed) reaching peak values (in bits of information) at different distances from the pedestrian path. • We find that the differences in mutual information are shaped by multiple factors including the type of infrastructure (i.e. stop sign, pedestrian crosswalk, or no designated crossing infrastructure) present during the encounter. Right-of-way negotiation between drivers and pedestrians often relies on explicit (e.g., waving) and implicit (e.g., kinematic) cues that signal intent. Since effective driver-pedestrian communication is important for reducing safety-relevant conflicts, this study uses information theory to identify vehicle kinematic behaviors that provide the greatest information gain and serve as cues for pedestrians to cross safely. A driver-pedestrian dataset with 348 interactions was extracted from a large naturalistic driving data collection effort. It includes 325 instances of a pedestrian crossing the vehicle's path and 23 instances in which the vehicle did not yield to a pedestrian. Kinematic data were collected from the vehicle's CAN. Pedestrian behaviors, driver cues, and contextual information were manually annotated from a forward-facing video. We used kernel density estimation to quantify the probabilities of vehicle acceleration, speed, and standard deviation of speed, for a given vehicle position and pedestrian behavior. Mutual information was then calculated between the estimated distributions given a pedestrian behavior (crossing/not crossing; walking/pausing) across intersection types (protected, e.g., stop signs; designated, e.g., crosswalks; and undesignated, e.g., jaywalking). The patterns mutual information conveyed by vehicle kinematics differed across measures (acceleration, speed, and standard deviation of speed) reaching peak values (in bits of information) at different distances from the pedestrian path. The mutual information conveyed by vehicle acceleration and pedestrian crossing behaviors peaked the farthest from the pedestrian path in the designated crossings, about 18 m away from the pedestrian path, with a difference in median deceleration of 1.01 m/s2 (p < 0.001) between pedestrian pausing and walking epochs. For protected crossings, the peak in mutual information occurred closer (10 m) to the pedestrian path, where median vehicle deceleration was significantly lower (0.55 m/s2; p < 0.05) in pausing epochs compared to walking epochs. For undesignated crossings, the peak in mutual information was the closest to the pedestrian crossing path, around 5 m, and was associated with a stronger deceleration behavior in pedestrian crossing epochs (-0.33 m/s2; p < 0.1). Vehicle speed demonstrated a similar sensitivity to distance from the pedestrian path across intersection types. Lastly, looking at the outcome of pedestrian behavior (i.e., crossing/not crossing), we find that the mutual information conveyed by acceleration, speed, and standard deviation of speed, peaked when the vehicle was at 30 m (stronger braking −0.37 m/s2; p < 0.1) and 10 m away, with greater acceleration (0.81 m/s2; p < 0.001) and faster speeds (2.41 m/s; p < 0.001) in pedestrian crossing epochs. This study examined driver-pedestrian information exchange using vehicle kinematic behavioral cues. We find that the differences in mutual information are shaped by multiple factors including the intersection type. In general, there was less mutual information gain in protected crossings which may be explained by unambiguous right-of-way rules guiding driver and pedestrian behavior, reducing the need for negotiation. Driver-pedestrian interactions in designated crossings seem to take place over a larger distance range compared to undesignated or protected crossings. These findings may support the design of automated driving and pedestrian safety systems that are able to consider the type, strength, and timing of kinematic cues to optimize driver-pedestrian negotiation. Eventually, such systems may enhance safe, efficient, and social interactions with pedestrians. [ABSTRACT FROM AUTHOR]

• Investigates the determinants of pedestrian crash severity on UK rural roads. • Considers five-year pedestrian-vehicle crashes at intersections and non-intersections. • Latent Class Analysis and Ordered Probit model account for heterogeneity in factors affecting crashes. • Certain factors are statistically significant to only intersections or non-intersections. • Some factors are statistically significant to both intersections and non-intersections. Pedestrian safety is a critical issue in the United Kingdom (UK) as pedestrians are the most vulnerable road users. Despite numerous studies on pedestrian-vehicle crashes globally, limited research has been conducted to explore the factors contributing to such incidents in the UK, especially on rural roads. Therefore, this study aimed to investigate the severity of pedestrian injuries sustained on rural roads in the UK, including crashes at intersections and non-intersections. We utilized the STATS19 dataset, which provided comprehensive road safety data from 2015 to 2019. To overcome the challenges posed by heterogeneity in the data, we employed a Latent Class Analysis to identify homogeneous clusters of crashes. Additionally, we utilized the Ordered Probit model to identify contributing factors within each cluster. Our findings revealed that various factors had distinct effects on the severity of pedestrian injuries at intersections and non-intersections. Several parameters like the pedestrian location in footway and one-way roads are only statistically significant in the intersection section. Certain factors such as the day of the week, the pedestrian's location in a refuge, and minor roads (class B roads) were found to be significant only in the non-intersection section. Parameters including pedestrians aged over 65 years and under 15 years, drivers under 25 years, male drivers and pedestrians, darkness, heavy vehicles, speed limits exceeding 96 km/h (60 mph), major roads (class A roads), and single carriageway roads are significant in both sections. The study proposes various measures to mitigate the severity of pedestrian-vehicle crashes, such as improving lighting conditions, enhancing pedestrian infrastructure, reducing speed limits in crash-prone areas, and promoting education and awareness among pedestrians and drivers. The findings and suggested measures could help policymakers and practitioners develop effective strategies and interventions to reduce the severity of these incidents and enhance pedestrian safety. [ABSTRACT FROM AUTHOR]

• Front crash prevention (FCP) system evaluations must evolve to address more crashes. • FCP systems can address 1.4 million additional police-reported crashes each year. • Evaluations should occur at higher speeds, with non-passenger vehicles, and in dark. • Targeting head-on crashes would address 9% of unaddressed fatal crashes. • Preventing vehicles from turning into bicyclists or pedestrians should be targeted. Government and consumer-information organizations can motivate automakers to address additional crash types through front crash prevention (FCP) testing programs. This study examined the current state of crashes potentially relevant to current and future FCP systems to provide a roadmap for the next crash types that vehicle testing programs in the United States should evaluate. Crash records from 2016 to 2020 were extracted from the Crash Report Sampling System (CRSS) and the Fatality Analysis Reporting System (FARS). Crashes were restricted to ones involving no more than two vehicles where the striking or path-intruding vehicle was a passenger vehicle and a vehicle defect was not coded. Percentages of police-reported crashes, nonfatal-injury crashes, and fatal crashes were computed for different crash types and circumstances. Rear-end and pedestrian crashes evaluated in existing FCP testing programs accounted for 27% of all police-reported crashes, 19% of nonfatal-injury crashes, and 18% of fatal crashes. The remaining crash types relevant to FCP accounted for 25% of police-reported crashes, 31% of nonfatal-injury crashes, and 23% of fatal crashes. A turning passenger vehicle crossing the path of an oncoming vehicle accounted for the largest proportion of the remaining police-reported (8%) and nonfatal-injury crashes (13%). Head-on crashes accounted for the largest proportion of remaining fatal crashes (9%). Most FCP-relevant police-reported crashes occurred on roads with a posted speed limit between 30 and 50 mph. Medium/heavy trucks were the crash partner in a disproportionate number of fatal head-on and rear-end crashes and motorcycles in a disproportionate number of fatal rear-end and turning crossing-path crashes. Fatal bicyclist and pedestrian crashes were overrepresented at night. The findings from this study indicate that testing organizations should evaluate FCP performance at higher speeds; with non-passenger vehicles and vulnerable road users; during the night; and in more complex head-on and turning crash scenarios to reduce crashes of all severities. Some of these conditions are currently assessed by other testing organizations and can be readily adopted by U.S. programs or possibly addressed with new approaches like virtual testing. [ABSTRACT FROM AUTHOR]

• Assessing the transferability of a crash modification factor is an example of assessing external validity. • Assessing external validity almost certainly requires understanding how a modification works. • A case study is presented developing and testing an explanation for a crash modification effect. • Guidance to others wanting to develop explanations is provided. Although the Highway Safety Manual (HSM) now provides empirical tools for predicting the safety consequences of highway engineering decisions, these tools represent the driver and vehicle conditions prevailing in the United States during the last few decades. As automated vehicles improve in capability and increase in market share these conditions will change, possibly reducing the accuracy of HSM predictions. Assessing the transferability of a crash modification factor to new situations almost certainly requires an explanation of how the modification achieves its effect, but at present there is little guidance on how such explanations might be posed and tested. This paper describes the use of micro-simulation to develop an explanation of how pedestrian hybrid beacons (PHB) modify pedestrian crash likelihood. Since the literature indicated that PHBs can affect both pedestrian and driver behavior it was necessary to include both possibilities in the model. To simulate injury severity distributions similar to those recorded in a crash database it was necessary to propose that almost all simulated drivers attempt to brake in pedestrian/vehicle encounters. Then changing the simulated fraction of careful pedestrians from between 0% and 30% to between 80% and 90% gave simulated crash modification factors similar to estimates reported in the literature. The resulting working hypothesis then is that PHBs achieve their crash reduction effect in large part by modifying pedestrian behavior. This is not so much a direct observation as it is an inference to the best explanation. That is, the support for the hypothesis comes from its ability to explain the data at hand. This hypothesis should be tested further, and additional tests are proposed. [ABSTRACT FROM AUTHOR]

• Pedestrian injury severities are modeled in pedestrian-vehicle crashes in North Carolina. • Latent class clustering approach is applied to segment the crash data for reducing the heterogeneity in data. • Partial proportional odds models are developed to further explore the contributing factors to pedestrian injury severities. • Differences in injury-severity probabilities are found between each latent class. • Key contributing factors are identified, and associated policy recommendations and future research directions are given. There are more than 2000 pedestrians reported to be involved in traffic crashes with vehicles in North Carolina every year. 10%–20% of them are killed or severely injured. Research studies need to be conducted in order to identify the contributing factors and develop countermeasures to improve safety for pedestrians. However, due to the heterogeneity inherent in crash data, which arises from unobservable factors that are not reported by law enforcement agencies and/or cannot be collected from state crash records, it is not easy to identify and evaluate factors that affect the injury severity of pedestrians in such crashes. By taking advantage of the latent class clustering (LCC), this research firstly applies the LCC approach to identify the latent classes and classify the crashes with different distribution characteristics of contributing factors to the pedestrian-vehicle crashes. By considering the inherent ordered nature of the traffic crash severity data, a partial proportional odds (PPO) model is then developed and utilized to explore the major factors that significantly affect the pedestrian injury severities resulting from pedestrian-vehicle crashes for each latent class previously obtained in the LCC. This study uses police reported pedestrian crash data collected from 2007 to 2014 in North Carolina, containing a variety of features of motorist, pedestrian, environmental, roadway characteristics. Parameter estimates and associated marginal effects are mainly used to interpret the models and evaluate the significance of each independent variable. Lastly, policy recommendations are made and future research directions are also given. [ABSTRACT FROM AUTHOR]

• A survey was conducted in Hanoi, Vietnam, in 2017 at ten pedestrian overpasses. • Pedestrian overpass usage varies significantly between 35.9% and 96.5%. • Risks are compensated by forming group and avoiding digital and social distractions. • Overpass usage decreases with taller overpasses, but increases with wider overpasses. • Mean illegal crossing speed is negatively associated with overpass usage. Pedestrian deaths and injuries are a major health issue in both developed and developing countries. In Vietnam, pedestrians account for about 10–11% of all road traffic deaths, while their travel distance contributes to approximately 2.4% of the total distance travelled by all modes. This paper aims to explore the use of pedestrian overpasses and identify influencing factors, particularly with regards to social and digital distractions, and overpass characteristics. An observational survey was conducted in Hanoi, Vietnam, in March 2017 at ten pedestrian overpasses. Behaviours of 608 pedestrians, including those who used an overpass to cross and those who illegally crossed, were observed. The rates of overpass usage varied significantly, between 35.9% and 96.5%. Modelling results suggest that pedestrians tended to compensate for the risks of illegal crossing by forming group and avoiding digital and social distractions (i.e., calling, operating a mobile phone's screen, listening to music, or talking to other pedestrians while crossing). In addition, overpass usage decreased with taller overpasses, but increased with wider overpasses. Effects of gender, weather, and illegal crossing speed on overpass use were also discussed. [ABSTRACT FROM AUTHOR]

• A questionnaire survey of 3423 individuals across six countries of varying cultural and economic characteristics. • The testing and validation of a Pedestrian Behaviour Questionnaire for the measurement of self-reported risk behaviours. • Three behavioural categories were found to be valid; Violations, Aggressions, and Lapses. • Country differences in self-reported pedestrian behaviours are explored, as well as age and gender effects. • Three factors of the PBQ are argued to be for the assessment of pedestrian behaviours across countries. The primary aim of this study was to validate the short version of a Pedestrian Behaviour Questionnaire across six culturally and economically distinct countries; Bangladesh, China, Kenya, Thailand, the UK, and Vietnam. The questionnaire comprised 20 items that asked respondents to rate the extent to which they perform certain types of pedestrian behaviours, with each behaviour belonging to one of five categories identified in previous literature; violations, errors, lapses, aggressive behaviours, and positive behaviours. The sample consisted of 3423 respondents across the six countries. Confirmatory factor analysis was used to assess the fit of the data to the five-factor structure, and a four-factor structure in which violations and errors were combined into one factor (seen elsewhere in the literature). For some items, factor loadings were unacceptably low, internal reliability was low for two of the sub-scales, and model fit indices were generally unacceptable for both models. As such, only the violations, lapses, and aggressions sub-scales were retained (those with acceptable reliability and factor loadings), and the three-factor model tested. Although results suggest that the violations sub-scale may need additional attention, the three-factor solution showed the best fit to the data. The resulting 12-item scale is discussed with regards to country differences, and with respect to its utility as a research tool in cross-cultural studies of road user behaviour. [ABSTRACT FROM AUTHOR]

• The environmental contributing factors to elderly pedestrian collision differ from ones to younger pedestrian collision. • Raised medians, 3-way intersections, and street trees make a positive contribution to the safety of elderly pedestrians. • Bus stops increase the odds of an intersection to be a hotspot of elderly pedestrian collisions. • The current road system designed for young, healthy users should be refurbished for older, vulnerable pedestrians. For the elderly, walking is an important, reliable mobility option, since the elderly frequently lose their physical and/or sensory ability to drive as their age increases. However, elderly pedestrians are vulnerable on the streets and are at great risk of injury or death, when involved in a collision. This is due to not only increased frailty but also such issues as reaction speed and confidence on the streets. Therefore, pedestrian safety for older adults is a growing concern. This paper comprehensively examines the relationship between physical conditions and elderly pedestrian safety at the intersection level. By constructing a multinomial logistic regression (MLR) model, this paper identifies the exclusive contributing factors to elderly pedestrian collisions rather than younger pedestrian collisions. The outputs from the model suggest that facilities such as raised median, three-way intersection, street tree, and park and recreational land use improve the safety of elderly pedestrians. They also imply that bus stops increase elderly pedestrian collisions, while the intersections with crosswalks or colored crosswalks do not contribute to elderly pedestrians' safety, but the safety of younger pedestrians. The findings of this paper provide insight to transportation policies like Complete Street and Vision Zero and help to improve the current road system that are designed for automobiles and young, healthy road users. [ABSTRACT FROM AUTHOR]

• Police-reported crashes compared with self-reported crashes and safety perceptions. • Most police-reported crashes involved a vehicle, with negligible on-campus reporting. • Self-reported crashes demonstrate high crash concentrations along campus boundaries. • Safety perception concentrations are positively correlated with actual crashes. • Indication of a saturation point in the crashes-safety perceptions relationship. In this paper, the non-motorized traffic safety concerns in and around three university campuses are evaluated by comparing police-reported crash data with traffic safety information sourced from the campus communities themselves. The crowdsourced traffic safety data comprise of both self-reported crashes as well as perceived hazardous locations. The results of the crash data analysis reveal that police-reported crashes underrepresent non-motorized safety concerns in and around the campus regions. The spatial distribution of police-reported crashes shows that police-reported crashes are predominantly unavailable inside the main campus areas, and the off-campus crashes over-represent automobile involvement. In comparison, the self-reported crash results report a wide variety of off-campus collisions not involving automobiles, while also highlighting the issue of high crash concentrations along campus boundaries. An assessment of the perceived hazardous locations (PHLs) reveals that high concentrations of such observations at/near a given location have statistically significant association with both survey-reported crashes as well as future police-reported crashes. Moreover, the results indicate the presence of a saturation point in the relationship between crashes and PHLs wherein beyond a certain limit, an increasing number of traffic safety concerns may not necessarily correlate with a proportional increase in the number of crashes. These findings suggests that augmenting our existing knowledge of traffic safety through crowdsourcing techniques can potentially help in better estimating both existing as well as emerging traffic safety concerns. [ABSTRACT FROM AUTHOR]

Pedestrians are considered the most vulnerable road users who are directly exposed to traffic crashes. With a view to addressing the growing concern of pedestrian safety, Federal and local governments aim at reducing pedestrian-involved crashes. Nevertheless, pedestrian volume data are rarely available even though they among the most important factors to identify pedestrian safety. Thus, this study aims at identifying surrogate measures for pedestrian exposure at intersections. A two-step process is implemented: the first step is the development of Tobit and generalized linear models for predicting pedestrian trips (i.e., exposure models). In the second step, negative binomial and zero inflated negative binomial models were developed for pedestrian crashes using the predicted pedestrian trips. The results indicate that among various exposure models the Tobit model performs the best in describing pedestrian exposure. The identified exposure-relevant factors are the presence of schools, car-ownership, pavement condition, sidewalk width, bus ridership, intersection control type and presence of sidewalk barrier. It was also found that the negative binomial model with the predicted pedestrian trips and that with the observed pedestrian trips perform equally well for estimating pedestrian crashes. Also, the difference between the observed and the predicted pedestrian trips does not appear as statistically significant, according to the results of the t -test and Wilcoxon signed-rank test. It is expected that the methodologies using predicted pedestrian trips or directly including pedestrian surrogate exposure variables can estimate safety performance functions for pedestrian crashes even though when pedestrian trip data is not available. [ABSTRACT FROM AUTHOR]

• A "traverse" is the passage over a level crossing by a road user. • New data on traverses in Britain are used to give fatality rates per traverse. • Fatality rates for pedestrians are ten times higher than for road vehicle users. • Fatality rates at passive crossings are 100 times higher than at controlled crossings. • The valuation of delays and of fatalities at active crossings are compared. This paper investigates relationships between traverses, delays and fatalities to road users at railway level crossings in Great Britain. A 'traverse' means a passage across a level crossing by a road user, who may be a pedestrian, cyclist, or occupant of a road vehicle. The paper finds that the road users with the highest fatality rate per traverse are pedestrians at passive crossings. Their rate is about three orders of magnitude higher than that of users with the lowest risk, who are road vehicle occupants at railway-controlled crossings. The paper considers the choice between automatic and railway-controlled crossings on public roads. Railway-controlled crossings are widely used in Britain. They are about one order of magnitude safer than automatic crossings, but they impose greater delays on users. A formula is developed to give the overall delay to road users at either type of crossing in terms of the numbers of road users and trains per day, and in terms of the length of time that the crossing must be closed to the road to allow the passage of one train. It is found that automatic level crossings cause substantially less delay than railway-controlled level crossings. The official monetary values of road user delay and of preventing a fatality were used to estimate the valuations of delays and fatalities at hypothetical but representative automatic and railway-controlled crossings. These valuations were then used to explore the effect of replacing representative railway-controlled with automatic crossings or vice-versa. It is found that the valuation of the reduced delays from adopting automatic crossings typically outweighs the valuation of the losses from the increased casualties. However, in practice Britain has chosen to retain a large number of railway-controlled crossings, which implies accepting the delays in return for a good level crossing safety record. Finally, an analysis is carried out to determine the additional risk of typical car and walk journeys that involve traversing a level crossing compared with similar journeys that do not. It is found that the additional risk is small for motor vehicle journeys, but substantial for walk journeys. [ABSTRACT FROM AUTHOR]

• Studies of safety-in-numbers are reviewed. • Results are summarised by means of meta-analysis. • Summary coefficients are close to 0.40 for all groups of road users. • Results of primary studies are very heterogeneous. Safety-in-numbers denotes the tendency for the number of accidents to increase less than in proportion to traffic volume. This paper updates a meta-analysis of estimates of safety-in-numbers published in 2017 (Elvik and Bjørnskau, Safety Science, 92, 274–282). Nearly all studies find safety-in-numbers, but the numerical estimates vary considerably. As virtually all studies are cross-sectional, it is not possible to determine if safety-in-numbers represents a causal relationship. Meta-regression analysis was performed to identify factors which may explain the large heterogeneity of estimates of safety-in-numbers. It was found that safety-in-numbers tends to be stronger for pedestrians than for cyclists, and stronger at the macro-level (e.g. citywide) than at the micro-level (e.g. in junctions). Recent studies find a stronger tendency towards safety-in-numbers than older studies. [ABSTRACT FROM AUTHOR]

• Meta-analysis included 15 studies to assess the risk of fatality for pedestrians. • Increment of 1 km/h impact speed increases the odds of a pedestrian fatality by 11%. • The risk a pedestrian fatality reaches 5% at an estimated impact speed of 30 km/h. • Speed limits of 30–40 km/h are recommended in areas with high pedestrian traffic. Pedestrians struck in motorised vehicle crashes constitute the largest group of traffic fatalities worldwide. Excessive speed is the primary contributory factor in such crashes. The relationship between estimated impact speed and the risk of a pedestrian fatality has generated much debate concerning what should be a safe maximum speed limit for vehicles in high pedestrian active areas. Four electronic databases (MEDLINE, EMBASE, COMPENDEX, and SCOPUS) were searched to identify relevant studies. Records were assessed, and data retrieved independently by two authors in adherence with the PRISMA statement. The included studies reported data on pedestrian fatalities from motorised vehicle crashes with known estimated impact speed. Summary odds ratios (OR) were obtained using meta-regression models. Time trends and publication bias were assessed. Fifty-five studies were identified for a full-text assessment, 27 met inclusion criteria, and 20 were included in a meta-analysis. The analyses found that when the estimated impact speed increases by 1 km/h, the odds of a pedestrian fatality increases on average by 11% (OR = 1.11, 95% CI: 1.10–1.12). The risk of a fatality reaches 5% at an estimated impact speed of 30 km/h, 10% at 37 km/h, 50% at 59 km/h, 75% at 69 km/h and 90% at 80 km/h. Evidence of publication bias and time trend bias among included studies were found. The results of the meta-analysis support setting speed limits of 30–40 km/h for high pedestrian active areas. These speed limits are commonly used by best practice countries that have the lowest road fatality rates and that practice a Safe System Approach to road safety. [ABSTRACT FROM AUTHOR]

• Right-turning vehicle-pedestrian conflict assessment at intersection crosswalks. • Various behavior models are integrated to a simulation platform. • The frequency and severity of conflict can be represented by SSMs. • Large dimensions and turning angles result in undesirable safety performance. Frequent vehicle-pedestrian conflicts deserve special attention for safety assessment at intersections. This study helps verify how the simulation as an innovative approach can be utilized for right-turning vehicle-pedestrian conflict assessment at intersection crosswalks prior to implementation. Various behavior models such as vehicle turning path, turning speed, gap acceptance model and pedestrian behavior model, have been established. Through integrating the calibrated models into one simulation platform, the stochastic behavior of vehicles and pedestrians under different geometric layouts and operational conditions can be reproduced. Based on the field data collected by an unmanned aerial vehicle (UAV) at two urban intersections in Beijing, China, it was demonstrated through validation of surrogate safety measures (SSMs), i.e., Post Encroachment Time (PET) and vehicle passing speed at conflict points, that the simulation model can reasonably represent the frequency and severity of conflict occurrence at signalized crosswalks. The sensitivity analysis results indicated that large dimensions and turning angles of intersections tend to result in undesirable safety performance. [ABSTRACT FROM AUTHOR]

• A systematic review of 49 articles examining hazard perception methodologies. • There was a high level of heterogeneity in the methodologies. • Temporal responses were a consistent measure of HP whilst spatial were inconsistent. • There were considerable inconsistencies in the development of the tests. Poor hazard perception, or the ability to anticipate potentially dangerous road and traffic situations, has been linked to an increased crash risk. Novice and younger road users are typically poorer at hazard perception than experienced and older road users. Road traffic authorities have recognised the importance of hazard perception skills, with the inclusion of a hazard perception test in most Graduated Driver Licensing (GDL) systems. This review synthesises studies of hazard perception tests in order to determine best practice methodologies that discriminate between novice/younger and experienced/older road users. Published studies available on PsychInfo, Scopus and Medline as at April 2018 were included in the review. Studies included a hazard perception test methodology and compared non-clinical populations of road users (car drivers, motorcyclists, bicyclists and pedestrians), based on age and experience, or compared methodologies. 49 studies met the inclusion criteria. There was a high degree of heterogeneity in the studies. However all methodologies – video, static image, simulator and real-world test-drive were able to discriminate road user groups categorised by age and/or experience, on at least one measure of hazard perception. Whilst there was a high level of heterogeneity of studies, video methodology utilising temporal responses (e.g. press a button when detecting the potential hazard) are a consistent measure of hazard perception across road user groups, whereas spatial measures (e.g. locate potential hazard in the scenario) were inconsistent. Staged footage was found to discriminate as well as unstaged footage, with static images also adding valuable information on hazard perception. There were considerable inconsistencies in the categorising of participants based on age and experience, limited application of theoretical frameworks, and a considerable lack of detail regarding post hoc amendments of hazardous scenarios. This research can guide further developments in hazard perception testing that may improve driver licensing and outcomes for road users. [ABSTRACT FROM AUTHOR]

• Pedestrian crossing behavior at unsignalized midblock crosswalk was analyzed. • A Logistic model was developed to estimate the gap acceptance probability. • Six external factors were considered. • Gap size, crossing distance, waiting time and pedestrian location have significant effect. • Recommendations on improving crossing safety were proposed. Gap acceptance represents a pedestrian's assessment of how safe it may be to use an available gap in traffic flow at a particular point in time. Though walking is a major component of urban mobility, the high rate of fatal interaction with motor vehicle traffic raises safety issues around how pedestrians decide to accept the available gap. This paper explored these interactions by modeling gap acceptance behavior at the midblock crosswalks. Unlike other pedestrian gap acceptance studies that focus on individual psychological and sociological factors that are difficult to control or manage, this study focused on six environmental factors that we considered important and as having the potential to affect the pedestrians' gap acceptance decision at the crosswalks, i.e. gap size, crossing distance, number of waiting pedestrians, waiting time, vehicle traffic volume and position of pedestrian (whether on street kerb or median). Video data was collected on pedestrian gap acceptance from 13 midblock crosswalk locations in Shanghai, China. A Logit model with 96% accuracy was developed to describe and predict the pedestrian gap acceptance behaviors. The results show that gap size and crossing distance have the highest effect on the pedestrian gap acceptance decision. Pedestrians waiting at the kerbside could confidently accept gaps (with a 95% probability) when the gap is longer than 2.2s, 5.9s, and 9.6s under the condition that the crossing distance is 4 m (one lane), 7.5 m (two lanes), and 11 m (three lanes), respectively while pedestrians waiting at the median could confidently accept gaps when the gap is longer than 1.6s, 5.3s, and 8.5s respectively under the same conditions. The recommendations on improving the crossing safety are proposed accordingly. [ABSTRACT FROM AUTHOR]

• Injury statistics have identified older adults and children as high-risk road users. • To safely navigate, pedestrians must detect affordances or opportunities for action. • This study investigated age differences in using information specifying affordances. • Children and adults identified gap affordances using a time-based variable (tau). • Older adults had lower adherence to tau suggesting perceptual deterioration with age. Recent road-crossing literature has found that older adults show performance differences between estimation and perception-action tasks suggesting an age-related difficulty in accurately calibrating the information picked up from the surrounding environment to their action capabilities (Lobjois and Cavallo, 2009). The present study investigated whether participants could accurately perceive gap affordances via information that specifies the time-to-arrival of the approaching cars. To ensure the opportunities for action were the same across different age groups, independent of the actor's action capabilities, the action of crossing the road was standardised. A total of 45 participants (15 children, aged 10–12, 15 adults aged 19–39, 15 older adults aged 65+) were asked to judge, by pressing a button in a head-mounted display, whether the gap between oncoming cars afforded crossing. When the participant pressed the button, they moved across the road at a fixed speed. Adherence to a time-based variable (namely tau) explained 85% and 84% of the variance in both the children and adults' choices, respectively. Older adults tuned less into the time-based variable (tau) with it only accounting for 59% of the variance in road-crossing decisions. These findings suggest that, the ability to use tau information which specifies whether a gap affords crossing or not, deteriorates with age. [ABSTRACT FROM AUTHOR]

• We are the first to evaluate the potential protective effect of controlling vehicle braking during vehicle pedestrian impacts. • The severity of contact of the head and pelvis with the ground can frequently be reduced. • Controlled braking increases the proximity of the vehicle to the pedestrian at the instant of ground contact. • The increased stopping distance associated with controlled braking mostly poses no conflict with the available free space in front of the vehicle. Protecting struck pedestrians during the ground contact phase has been a challenge for decades. Recent studies have shown how ground related injury is influenced by pedestrian kinematics. In this paper we further developed this approach by assessing the potential of controlling vehicle braking to reduce pedestrian ground contact injuries. Applying a recently proposed Simulation Test Sample, a series of simulations were run using the MADYMO software environment. The approach considered 6 vehicle shapes, 4 pedestrian models, 3 impact velocities and 2 pedestrian gaits and each case was considered with two different vehicle braking approaches. The first was full braking, while the second applied controlled braking, for which a strategy based on pedestrian kinematics was applied. The effect of vehicle braking was evaluated using the Weighted Injury Cost (WIC) of overall pedestrian injuries and the pedestrian-ground impact velocity change. The proximity of the vehicle and pedestrian at the instant of ground contact was also evaluated to assess the potential of future vehicle based intervention methods to cushion the ground contact. Finally real-world videos of pedestrian collisions were analyzed to estimate the available free vehicle stopping distances. Results showed substantial median reductions in WIC and head impact velocity for all vehicle shapes except the Van. The proximity of the pedestrian to the vehicle front at the instant of ground contact under controlled braking is less than 1.5 m in most cases, and the required stopping distance for the vehicle under controlled braking was within the available stopping distance estimated from the video footage in about 74% of cases. It is concluded that controlled braking has significant potential to reduce the overall burden of pedestrian ground contact injuries, but future efforts are required to establish an optimized braking strategy as well as a means to handle those cases where controlled braking is not beneficial or even harmful. [ABSTRACT FROM AUTHOR]

• A detailed analysis of the occurrence of pedestrian head injury types categorised separately into skull and brain injuries. • GIDAS accident data, Spearman's rank correlation coefficient and logistic regression model are employed. • Interrelationship between different head injury types was investigated. • Influences of impact speed, pedestrian age and car front shape on different kinds of pedestrian head injuries were studied. Head injury is one of the most common injury types in vehicle-to-pedestrian collisions, which leads to death and long-term disabilities. However, detailed analysis of pedestrian head injuries in real world collisions is scarce. Thus the current study used two samples of 120 cases and 184 cases extracted from 1060 pedestrian collision cases captured during 2000–2015 from the GIDAS (German In-Depth-Accident Study) database to investigate the detailed characteristics of AIS2+ pedestrian head injuries. Firstly, the interrelationship between different head injury types (skull fracture, focal brain injury, concussion and diffuse axonal injury (DAI)) was analysed using the sample of 120 cases which each had at least one AIS2+ head injury. Then the influences of impact speed, pedestrian age and car front shape parameters on the injury risk of skull fracture, focal brain injury and concussion were assessed using the logistic regression method, based on the sample of 184 AIS1+ cases where the primary head contact location was within the windscreen glass area. The results show that: skull fractures and focal brain injuries dominate for AIS3+ head injuries and are generally associated with each other; concussion is the most important injury type for AIS2 head injuries and usually occurs in isolation. Further, for head impacts to the windscreen glass area a higher bonnet leading edge helps to reduce concussion odds, and none of the selected car front shape parameters are significant for the odds of skull fracture and focal brain injury, and vehicle impact speed and pedestrian age are insignificant for concussion. These detailed characteristics of pedestrian head injuries provide a basis for future pedestrian head injury prevention strategies with skull fractures and focal brain injuries being the most important injuries to address. [ABSTRACT FROM AUTHOR]

• Development of a cost effective conflict data collection approach. • Need for conflict data involving vulnerable transportation modes. • Provide a tool for rating conflict severity. • Test the effectiveness and practicality of crowd sourced data collection. Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-motor vehicle and other similar conflicts types may define a better performance measure for safety assessment. In the field of transportation safety, an absolute conflict occurs when two parties' paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver is required may also impact public perceptions of safety especially for vulnerable modes. Most of the existing literature focuses on vehicle conflicts. While in the past several years, more research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. A comprehensive analysis of conflicts appears critical. The major objective of this study is two fold: 1) Development of an innovative and cost effective conflict data collection technique to better understand the conflicts (and their severity) involving vulnerable road users (e.g. bicycle/pedestrian, bicycle/motor vehicle, and pedestrian/motor vehicle) and their severity. 2) Test the effectiveness and practicality of the approach taken and its associated crowd sourced data collection. In an endeavor to undertake these objectives, the researchers developed an android-based crowd-sourced data collection app. The crowd-source data collected using the app is compared with traditional fatality data for hot spot analysis. At the end, the app users provide feedback about the overall competency of the app interface and the performance of its features to the app developers. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, encouragement, enforcement, programs, policies, and infrastructure design and planning. [ABSTRACT FROM AUTHOR]

• Crossing the street is physically and cognitively demanding for older pedestrians. • Crossing the street can be seen as a dual task. • More street-crossing collisions were observed with aging. • Older participants placed priority on walking rapidly than on scanning traffic. • Lack of awareness of task demands among old participants particularly. Although several studies have sought to identify both gap-acceptance difficulties with aging and gait differences, few have examined the extent to which crossing the street is physically and cognitively demanding for older pedestrians, in such a way that street crossing can be seen as a dual task. To gain insight into this issue, this study reports an experiment with 15 young (ages 19–26), 19 younger-old (ages 60–72), and 21 older-old (ages 73–82) adults. The participants carried out three tasks: (i) a simple walking task, (ii) a dual task involving walking while scanning (walking while pressing a button as soon as a visual or sound stimulus appeared), and (iii) a street-crossing task with vehicles approaching from two directions. The results indicated more street-crossing collisions in older-old than in younger-old and young participants. Longer reaction times were observed in the dual walking-scanning task for both old groups, especially for visual stimuli. Walking-speed comparisons yielded nonsignificant differences between the dual task and the street-crossing task in young participants, suggesting a correspondence in terms of demands and task priority. In contrast, old participants walked significantly faster in the street-crossing task than in the dual task, suggesting that they placed priority on walking rapidly than on scanning traffic. Finally, whereas the participants estimated the perceived workload to be greater when they were crossing the street than while simply walking or responding to the dual task, young participants gave the highest rating to the mental and physical demands, perhaps due to a lack of awareness of task demands among old participants. [ABSTRACT FROM AUTHOR]

• Different pedestrian crash exposure proxies: population, walking frequency and walking time, were estimated based on the household travel survey data. • Three pedestrian crash prediction models were established to measure the association between pedestrian crash, exposure, travel purpose and other possible factors. • Model using walking frequency as the exposure proxy was superior to that using population and walking time. • Crash risk of back home walking trip was more profounding than that of other travel purposes. Pedestrian are vulnerable to severe injury and mortality in the road crashes. Understanding the essence of the pedestrian crash is important to the development of effective safety countermeasures and improvement of social well-being. It is necessary to measure the exposure for the quantification of pedestrian crash risk. The primary goals of this study are to explore the efficient exposure measure for pedestrian crash, and identify the possible factors contributing to the incidence of pedestrian crash. In this study, amount of travel was estimated based on the Travel Characteristic Survey (TCS) data in 2011, and the crash data were obtained from the Transport Information System (TIS) of the Hong Kong Transport Department during the period from 2011 to 2015. Total population, walking frequency and walking time were adopted to represent the pedestrian exposure to road crash. The effect of trip purpose on pedestrian crash was evaluated by disaggregating the pedestrian exposure proxies by purpose. Three random-parameter negative binomial regression models were developed to compare the performances of the three pedestrian exposure proxies. It was found that the model in which walking frequency was used as the exposure proxy provided the best goodness-of-fit. Frequency of walking back home, among other trip purposes, was the most sensitive to the increase in pedestrian crash risk. Additionally, increase in the frequency of pedestrian crash was correlated to the increases in the proportions of children and elderly people. Furthermore, household size, median household income, road density, number of non-signalized intersection as well as number of zebra crossings also significantly affected the pedestrian crash frequency. Findings of this study should be indicative to the development and implementation of effective traffic control and management measures that can improve the pedestrian safety in the long run. [ABSTRACT FROM AUTHOR]