Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic.

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

The self-reported driving and pedestrian behaviour of adults with developmental coordination disorder.

BACKGROUND: Developmental coordination disorder (DCD) affects movement coordination, but little is known about how the condition impacts the behaviours of car drivers and pedestrians. AIMS: This study examined the self-reported driving and pedestrian behaviours of adults with Developmental Coordination Disorder (DCD). METHODS AND PROCEDURES: One hundred and twenty-eight participants (62 adults with DCD vs. 66 TD adults) responded to an online survey asking them about their perceptions of confidence and self-reported driving and pedestrian behaviours in the real-world. OUTCOMES AND RESULTS: Results suggested that adults with DCD felt less confident and reported more lapses in attention (e.g., forgetting where their car was parked) and errors (e.g., failing to check their mirrors prior to a manoeuvre) when driving compared to typically developed (TD) adults. Adults with DCD also reported feeling less confident and reported less adherence to road traffic laws (e.g., not waiting for a green crossing signal before crossing the road) when walking as pedestrians. CONCLUSIONS AND IMPLICATIONS: These results offer some much-needed insight into the behaviours of those with DCD outside of the laboratory environment and underline the need for research investigating the driving and pedestrian behaviours of individuals with DCD in 'real-world' contexts.

The efficacy of hazard perception training and education: A systematic review and meta-analysis.

BACKGROUND: Hazard perception (HP) has been argued to improve with experience, with numerous training programs having been developed in an attempt to fast track the development of this critical safety skill. To date, there has been little synthesis of these methods. OBJECTIVE: The present study sought to synthesise the literature for all road users to capture the breadth of methodologies and intervention types, and quantify their efficacy. DATA SOURCES: A systematic review of both peer reviewed and non-peer-reviewed literature was completed. A total of 57 papers were found to have met inclusion criteria. RESULTS: Research into hazard perception has focused primarily on drivers (with 42 studies), with a limited number of studies focusing on vulnerable road users, including motorcyclists (3 studies), cyclists (7 studies) and pedestrians (5 studies). Training was found to have a large significant effect on improving hazard perception skills for drivers (g = 0.78) and cyclists (g = 0.97), a moderate effect for pedestrians (g = 0.64) and small effect for motorcyclists (g = 0.42). There was considerable heterogeneity in the findings, with the efficacy of training varying as a function of the hazard perception skill being measured, the type of training enacted (active, passive or combined) and the number of sessions of training (single or multiple). Active training and single sessions were found to yield more consistent significant improvements in hazard perception. CONCLUSIONS: This study found that HP training improved HP skill across all road user groups with generally moderate to large effects identified. HP training should employ a training method that actively engages the participants in the training task. Preliminary results suggest that a single session of training may be sufficient to improve HP skill however more research is needed into the delivery of these single sessions and long-term retention. Further research is also required to determine whether improvements in early-stage skills translate to improvements in responses on the road, and the long-term retention of the skills developed through training.

Emergency Department Visits for Pedestrians Injured in Motor Vehicle Traffic Crashes - United States, January 2021-December 2023.

Traffic-related pedestrian deaths in the United States reached a 40-year high in 2021. Each year, pedestrians also suffer nonfatal traffic-related injuries requiring medical treatment. Near real-time emergency department visit data from CDC's National Syndromic Surveillance Program during January 2021-December 2023 indicated that among approximately 301 million visits identified, 137,325 involved a pedestrian injury (overall visit proportion = 45.62 per 100,000 visits). The proportions of visits for pedestrian injury were 1.53-2.47 times as high among six racial and ethnic minority groups as that among non-Hispanic White persons. Compared with persons aged ≥65 years, proportions among those aged 15-24 and 25-34 years were 2.83 and 2.61 times as high, respectively. The visit proportion was 1.93 times as high among males as among females, and 1.21 times as high during September-November as during June-August. Timely pedestrian injury data can help collaborating federal, state, and local partners rapidly monitor trends, identify disparities, and implement strategies supporting the Safe System approach, a framework for preventing traffic injuries among all road users.

Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-µRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

Deciding when to cross in front of an autonomous vehicle: How child and adult pedestrians respond to eHMI timing and vehicle kinematics.

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.

Experimental Evaluation of Pedestrian-Induced Multiaxial Gait Loads on Footbridges: Effects of the Structure-to-Human Interaction by Lateral Vibrating Platforms.

The introduction of resistant and lightweight materials in the construction industry has led to civil structures being vulnerable to excessive vibrations, particularly in footbridges exposed to human-induced gait loads. This interaction, known as Human-Structure Interaction (HSI), involves a complex interplay between structural vibrations and gait loads. Despite extensive research on HSI, the simultaneous effects of lateral structural vibrations with fundamental frequencies close to human gait frequency (around 1.0 Hz) and wide amplitudes (over 30.0 mm) remain inadequately understood, posing a contemporary structural challenge highlighted by incidents in iconic bridges like the Millennium Bridge in London, Solferino Bridge in Paris, and Premier Bridge in Cali, Colombia. This paper focuses on the experimental exploration of Structure-to-Human Interaction (S2HI) effects using the Human-Structure Interaction Multi-Axial Test Framework (HSI-MTF). The framework enables the simultaneous measurement of vertical and lateral loads induced by human gait on surfaces with diverse frequency ranges and wide-amplitude lateral harmonic motions. The study involved seven test subjects, evaluating gait loads on rigid and harmonic lateral surfaces with displacements ranging from 5.0 to 50.0 mm and frequency content from 0.70 to 1.30 Hz. A low-cost vision-based motion capture system with smartphones analyzed the support (Tsu) and swing (Tsw) periods of human gait. Results indicated substantial differences in Tsu and Tsw on lateral harmonic protocols, reaching up to 96.53% and 58.15%, respectively, compared to rigid surfaces. Normalized lateral loads (LL) relative to the subject's weight (W0) exhibited a linear growth proportional to lateral excitation frequency, with increased proportionality constants linked to higher vibration amplitudes. Linear regressions yielded an average R2 of 0.815. Regarding normalized vertical load (LV) with respect to W0, a consistent behavior was observed for amplitudes up to 30.0 mm, beyond which a linear increase, directly proportional to frequency, resulted in a 28.3% increment compared to rigid surfaces. Correlation analyses using Pearson linear coefficients determined relationships between structural surface vibration and pedestrian lateral motion, providing valuable insights into Structure-to-Human Interaction dynamics.

The association between health literacy and pedestrian safety behavior among adults: a cross-sectional study.

INTRODUCTION: Pedestrians are considered the most vulnerable and complex road users as human behavior constitutes one of the fundamental reasons for traffic-related incidents involving pedestrians. However, the role of health literacy as a predictor of Pedestrian safety behavior remains underexplored. Therefore, the current study was designed to examine the level of health literacy and its association with the safety behavior of adult pedestrians in the city of Tabriz. METHODS: This cross-sectional analytical study was conducted among individuals aged 18 to 65 years in the metropolitan area of Tabriz from January to April 2023. Data were collected using the HELIA standard questionnaire (Health Literacy Instrument for adults), comprising 33 items across 5 domains (access, reading, understanding, appraisal, decision-making and behavior), as well as the Pedestrian Behavior Questionnaire (PBQ) consisting of 29 items. Data were analyzed using descriptive and analytical statistics (independent t-tests, ANOVA, and Pearson correlation coefficient) via SPSS-22 software. RESULTS: Based on the results, 94% (376 individuals) had excellent health literacy levels, and their safety behavior scores were at a good level. Health literacy and safety behavior were higher among the age group of 31 to 45 years, women, married individuals, those who read books, and individuals with higher education. However, safety behavior showed no significant association with education level (P > 0.05). There was a significant and positive relationship between health literacy and all its domains and pedestrian safety behavior (r = 0.369, P < 0.001). CONCLUSION: This study underscores the significant impact of health literacy on pedestrians' safety behavior. The findings reveal that higher levels of health literacy are associated with better safety behavior among individuals aged 18 to 63. Demographic factors such as age, gender, marital status, and education level also play a role in shaping both health literacy and safety behavior. By recognizing these relationships, interventions can be tailored to improve health literacy levels and promote safer pedestrian practices, ultimately contributing to a healthier and safer community in Tabriz city.

On the crashworthiness analysis of bio-inspired DNA tubes.

This study presents a thorough numerical evaluation of the crashworthiness properties of a new bio-inspired DNA tubes (BIDNATs) with circular, elliptical, and rectangular cross-sections. Deformation and crashworthiness behaviors are evaluated using axial quasi-static crushing simulations by ABAQUS/Explicit (Abaqus 6.14, https://www.3ds.com/products-services/simulia/products/abaqus/ ). The study compares the performance of conventional tubes with rectangular and elliptical cross-sections to DNA-inspired tubes. Increasing the rotation angle leads to more helices and a pronounced helix angle, resulting in lower initial peak force (IPF). However, lower cross-section aspect ratios generally have higher IPF and specific energy absorption (SEA) values. BIDNATs with rectangular cross-sections and a 540° rotation angle have the lowest SEA and IPF values across all aspect ratios. Notably, for the 110/100 aspect ratio, the SEA of E110/100 is 71% higher than the conventional tube. Overall, BIDNATs with elliptical cross-sections and a 360° rotation angle exhibit higher SEA values and lower IPF values, particularly for a width (W) of 100 mm. Conventional circular and elliptical tubes generally have SEA values exceeding 6 J/g, with only E110/100 surpassing this among DNA-inspired tubes. The NE110/100 tube has the highest SEA, surpassing E110/100 by 54%, while its IPF is 10% greater than DNA-inspired E110/100. It's worth noting that conventional circular and elliptical tubes have higher IPF values compared to their DNA-inspired counterparts. These findings offer valuable insights for engineers and researchers in the design of crash tubes to improve overall vehicle safety for both occupants and pedestrians.

Vehicle front-end geometry and in-depth pedestrian injury outcomes.

OBJECTIVE: Large passenger vehicles have consistently demonstrated an outsized injury risk to pedestrians they strike, particularly those with tall, blunt front ends. However, the specific injuries suffered by pedestrians in these crashes as well as the mechanics of those injuries remain unclear. The current study was conducted to explore how a variety of vehicle measurements affect pedestrian injury outcomes using crash reconstruction and detailed injury attribution. METHODS: We analyzed 121 pedestrian crashes together with a set of vehicle measurements for each crash: hood leading edge height, bumper lead angle, hood length, hood angle, and windshield angle. RESULTS: Consistent with past research, having a higher hood leading edge height increased pedestrian injury severity, especially among vehicles with blunt front ends. The poor crash outcomes associated with these vehicles stem from greater injury risk and severity to the torso and hip from these vehicles' front ends and a tendency for them to throw pedestrians forward after impact. CONCLUSIONS: The combination of vehicle height and a steep bumper lead angle may explain the elevated pedestrian crash severity typically observed among large vehicles.

GIS-based intelligent planning approach of child-friendly pedestrian pathway to promote a child-friendly city.

Pedestrian safety, particularly for children, relies on well-designed pathways. Child-friendly pathways play a crucial role in safeguarding young pedestrians. Shared spaces accommodating both vehicles and walkers can bring benefits to pedestrians. However, active children playing near these pathways are prone to accidents. This research aims to develop an efficient method for planning child-friendly pedestrian pathways, taking into account community development and the specific needs of children. A mixed-methods approach was employed, utilizing the Datang community in Guangzhou, China, as a case study. This approach combined drawing techniques with GIS data analysis. Drawing methods were utilized to identify points of interest for children aged 2-6. The qualitative and quantitative fuzzy analytic hierarchy process assessed factors influencing pathway planning, assigning appropriate weights. The weighted superposition analysis method constructed a comprehensive cost grid, considering various community elements. To streamline the planning process, a GIS tool was developed based on the identified factors, resulting in a practical, child-friendly pedestrian pathway network. Results indicate that this method efficiently creates child-friendly pathways, ensuring optimal connectivity within the planned road network.

Adversarial infrared blocks: A multi-view black-box attack to thermal infrared detectors in physical world.

Thermal infrared detectors have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. Recent works use bulb plate, "QR" suit, and infrared patches as physical perturbations to perform white-box attacks on thermal infrared detectors, which are effective but not practical for real-world scenarios. Some researchers have tried to utilize hot and cold blocks as physical perturbations for black-box attacks on thermal infrared detectors. However, this attempts has not yielded robust and multi-view physical attacks, indicating limitations in the approach. To overcome the limitations of existing approaches, we introduce a novel black-box physical attack method, called adversarial infrared blocks (AdvIB). By optimizing the physical parameters of the infrared blocks and deploying them to pedestrians from multiple views, including the front, side, and back, AdvIB can execute robust and multi-view attacks on thermal infrared detectors. Our physical tests show that the proposed method achieves a success rate of over 80% under most distance and view conditions, validating its effectiveness. For stealthiness, our method involves attaching the adversarial infrared block to the inside of clothing, enhancing its stealthiness. Additionally, we perform comprehensive experiments and compare the experimental results with baseline to verify the robustness of our method. In summary, AdvIB allows for potent multi-view black-box attacks, profoundly influencing ethical considerations in today's society. Potential consequences, including disasters from technology misuse and attackers' legal liability, highlight crucial ethical and security issues associated with AdvIB. Considering these concerns, we urge heightened attention to the proposed AdvIB. Our code can be accessed from the following link: https://github.com/ChengYinHu/AdvIB.git.

Comparative evaluation of soundscapes in human activities spatial contexts of pedestrian spaces adjacent to arterial roads.

Pedestrian spaces adjacent to arterial roads are characterized by the dominance of traffic noise alongside various human activities. Research on the impact of traffic noise on the soundscape evaluation of pedestrian spaces has not considered human activities spatial contexts. To address this research gap, the present study constructed auditory environments for pedestrian spaces in the contexts of commuting, residential, and commercial activities. A total of seven auditory environments were subjected to laboratory auditory evaluations, including perceived dominance of sound source, acoustic comfort, and perceived affective quality of the soundscape. The results indicated that in pedestrian spaces with constant traffic noise, the presence of significant human activity sounds led to a decreased perceived dominance of traffic noise and an increased acoustic comfort, despite the higher acoustic energy. Thus, pedestrian spaces with a variety of human activity received better soundscape evaluations. The elements that reflected the human activities spatial contexts, including the types and intensity of human activities, played a crucial role in soundscape evaluations. Better acoustic comfort was reported in pedestrian spaces characterized by low-intensity residential activities and high-intensity commercial activities. Additionally, pedestrian spaces with more intense activities offered an actively engaging soundscape. The findings can provide reference for a more accurate evaluation of the soundscape in pedestrian spaces and guide the soundscape design of pedestrian environments.

A quantitative comparison of virtual and physical experimental paradigms for the investigation of pedestrian responses in hostile emergencies.

Modern experiments investigating human behaviour in emergencies are often implemented in virtual reality (VR), due to the increased experimental control and improved ethical viability over physical reality (PR). However, there remain questions regarding the validity of the results obtained from these environments, and no full validation of VR experiments has yet appeared. This study compares the results of two sets of experiments (in VR and PR paradigms) investigating behavioural responses to knife-based hostile aggressors. This study quantitatively analyses these results to ascertain whether the different paradigms generate different responses, thereby assessing the use of virtual reality as a data generating paradigm for emergencies. The results show that participants reported almost identical psychological responses. This study goes on to identify minimal differences in movement responses across a range of predictors, noting a difference in responses between genders. As a result, this study concludes that VR can produce similarly valid data as physical experiments when investigating human behaviour in hostile emergencies, and that it is therefore possible to conduct realistic experimentation through VR environments while retaining confidence in the resulting data. This has major implications for the future of this type of research, and furthermore suggests that VR experimentation should be performed for both existing and new critical infrastructure to understand human responses in hostile scenarios.

How Approaching Angle, Bottleneck Width and Walking Speed Affect the Use of a Bottleneck by Individuals.

Understanding pedestrian dynamics at bottlenecks and how pedestrians interact with their environment-particularly how they use and move in the space available to them-is of safety importance, since bottlenecks are a key point for pedestrian flow. We performed a series of experiments in which participants walked through a bottleneck individually for varying combinations of approaching angle, bottleneck width and walking speed, to investigate the dependence of the movement on safety-relevant influencing factors. Trajectories as well as 3D motion data were recorded for every participant. This paper shows that (1) the maximum amplitude of shoulder rotation is mainly determined by the ratio of the bottleneck width to the shoulder width of the participant, while the direction is determined by the starting angle and the foot position; (2) the 'critical point' is not invariant to the starting angle and walking speed; (3) differences between the maximum and minimum speed values arise mainly from the distribution of deceleration patterns; and (4) the position of crossing shifts by 1.75 cm/10 cm, increasing the bottleneck width in the direction of origin.

The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model.

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.

A ward level analysis of child pedestrian casualty frequencies in Greater London.

INTRODUCTION: Child pedestrian safety remains a challenge despite the remarkable progress that has been attained in recent years, particularly, in high income jurisdictions such as London. This study sought to identify and quantify the magnitude of the effects of various explanatory variables, from the domains of transport, built and natural environment, socio-demographic and economic factors, on ward level child pedestrian injury frequencies in Greater London. METHOD: We adopted a multilevel random parameters model to investigate the factors associated with child pedestrian injuries given the hierarchical nature of the data comprising of wards nested within boroughs. RESULTS: We found that crime, the Black, Asian, and Minority Ethnic (BAME) population, school enrollment, and the proportion of the population who walk five times a week had an increasing effect on the number of child pedestrian casualties. Conversely, the proportion of the population with a level 4 qualification and the number of cars per household had a decreasing effect. CONCLUSIONS: Our study identified high child pedestrian injury frequency wards and boroughs: Stratford and New Town had the highest expected child pedestrian injury frequencies followed by Selhurst, Westend, and Greenford Broadway. Some inner London boroughs are among the highest injury frequency areas; however, a higher number of high child pedestrian injury boroughs are in outer London. PRACTICAL APPLICATIONS: The paper provides recommendations for policy makers for targeted child pedestrian safety improvement interventions and prioritization to optimize the utilization of often constrained resources. The study also highlights the importance of considering social inequities in policies that aim at improving child traffic safety.

A dynamic traffic signal scheduling system based on improved greedy algorithm.

Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans.

Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics.

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle-pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle-pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am-6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.

Visual hazardous models: A hybrid approach to investigate road hazardous events.

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.