Understanding the interaction between cyclists and motorized vehicles at unsignalized intersections: Results from a cycling simulator study.

INTRODUCTION: With cycling gaining more popularity in urban areas, it is vital to obtain accurate knowledge of cyclists' behavior to develop behavioral models that can predict the cyclist's intent. Most conflicts between cyclists and vehicles happen at crossings where the road users share the path, especially at unsignalized intersections. However, few studies have investigated and modeled the interaction between cyclists and vehicles at unsignalized intersections. METHOD: A bike simulator experiment was conducted to scrutinize cyclists' response process as they interacted with a passenger car at an unsignalized intersection. An existing unsignalized intersection in Gothenburg was simulated for test participants. Two independent variables were varied across trials: the difference in time to arrival at the intersection (DTA) and intersection visibility (IV). Subjective and quantitative data were analyzed to model the cyclists' behavior. RESULTS: When approaching the intersection, cyclists showed a clear sequence of actions (pedaling, braking, and head turning). The distance from the intersection at which cyclists started braking was significantly affected by the two independent variables. It was also found that DTA, looking duration, and pedaling behavior significantly affected cyclists' decisions to yield. Finally, the questionnaire outputs show that participants missed eye contact or communication with the motorized vehicle. CONCLUSIONS: The kinematic interaction between cyclists and vehicles, along with the cyclist's response process (visual and kinematic), can be utilized to predict cyclists' yielding decision at intersections. From the infrastructural perspective, enhancing visibility at intersections has the potential to reduce the severity of interactions between cyclists and vehicles. The analysis of the questionnaire emphasizes the significance of visual communication between cyclists and drivers to support the cyclist's decision-making process when yielding. PRACTICAL APPLICATIONS: The models can be used in threat assessment algorithms so that active safety systems and automated vehicles can react safely to the presence of cyclists in conflict scenarios.

Identifying factors related to pedestrian and cyclist crashes in ACT, Australia with an extended crash dataset.

As vulnerable road users, pedestrians and cyclists are facing a growing number of injuries and fatalities, which has raised increasing safety concerns globally. Based on the crash records collected in the Australian Capital Territory (ACT) in Australia from 2012 to 2021, this research firstly establishes an extended crash dataset by integrating road network features, land use features, and other features. With the extended dataset, we further explore pedestrian and cyclist crashes at macro- and micro-levels. At the macro-level, random parameters negative binomial (RPNB) model is applied to evaluate the effects of Suburbs and Localities Zones (SLZs) based variables on the frequency of pedestrian and cyclist crashes. At the micro-level, binary logit model is adopted to evaluate the effects of event-based variables on the severity of pedestrian and cyclist crashes. The research findings show that multiple factors are associated with high frequency of pedestrian total crashes and fatal/injury crashes, including high population density, high percentage of urban arterial road, low on-road cycleway density, high number of traffic signals and high number of schools. Meanwhile, many factors have positive relations with high frequency of cyclist total crashes and fatal/injury crashes, including high population density, high percentage of residents cycling to work, high median household income, high percentage of households with no motor vehicle, high percentage of urban arterial road and rural road, high number of bus stops and high number of schools. Additionally, it is found that more severe pedestrian crashes occur: (i) at non-signal intersections, (ii) in suburb areas, (iii) in early morning, and (iv) on weekdays. More severe cyclist crashes are observed when the crash type is overturned or struck object/pedestrian/animal; when more than one cyclist is involved; and when crash occurs at park/green space/nature reserve areas.

Vulnerable road user injury trends following the COVID-19 pandemic in Toronto, Canada: An interrupted time series analysis.

BACKGROUND: The COVID-19 pandemic altered traffic patterns worldwide, potentially impacting pedestrian and bicyclists safety in urban areas. In Toronto, Canada, work from home policies, bicycle network expansion, and quiet streets were implemented to support walking and cycling. We examined pedestrian and bicyclist injury trends from 2012 to 2022, utilizing police-reported killed or severely injured (KSI), emergency department (ED) visits and hospitalization data. METHODS: We used an interrupted time series design, with injury counts aggregated quarterly. We fit a negative binomial regression using a Bayesian modeling approach to data prior to the pandemic that included a secular time trend, quarterly seasonal indicator variables, and autoregressive terms. The differences between observed and expected injury counts based on pre-pandemic trends with 95% credible intervals (CIs) were computed. RESULTS: There were 38% fewer pedestrian KSI (95%CI: 19%, 52%), 35% fewer ED visits (95%CI: 28%, 42%), and 19% fewer hospitalizations (95%CI: 2%, 32%) since the beginning of the COVID-19 pandemic. A reduction of 35% (95%CI: 7%, 54%) in KSI bicyclist injuries was observed, but However, ED visits and hospitalizations from bicycle-motor vehicle collisions were compatible with pre-pandemic trends. In contrast, for bicycle injuries not involving motor vehicles, large increases were observed for both ED visits, 73% (95% CI: 49%, 103%) and for hospitalization 108% (95% CI: 38%, 208%). CONCLUSION: New road safety interventions during the pandemic may have improved road safety for vulnerable road users with respect to collisions with motor vehicles; however, further investigation into the risk factors for bicycle injuries not involving motor vehicles is required.

A data-mining study on the prediction of head injury in traffic accidents among vulnerable road users with varying body sizes and head anatomical characteristics.

Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.

Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users.

Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC15). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario.

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.

How do bicyclists respond to vehicles with adaptive headlamp systems? A nighttime study in an immersive virtual environment.

INTRODUCTION: The risk of motor vehicle-bicyclist crashes and fatalities is greater during nighttime than daytime lighting conditions, even though there are fewer cyclists on roadways at night. Vehicle Adaptive Headlamp Systems (AHS) aim to increase the visibility of bicyclists for drivers by directing a spotlight to illuminate bicyclists on or near the roadway. AHS technology also serves to alert bicyclists to the approaching vehicle by illuminating the road beneath the rider and by projecting a warning icon on the roadway. METHOD: Here, we examined how bicyclists respond to different AHS designs using a large screen, immersive virtual environment. Participants bicycled along a virtual road during nighttime lighting conditions and were overtaken by vehicles with and without an AHS system. The experiment included five treatment conditions with five different AHS designs. In each design a box of white light was projected beneath the rider; in four of the designs an icon was also projected on the road that varied in color (white or red) and position (to the left of the rider at midline or to the left of the front wheel). Participants in the control condition experienced only non-AHS vehicles. RESULTS: We found that riders in all AHS treatment conditions moved significantly farther away from overtaking vehicles with AHS systems, whereas riders in the control condition did not significantly move away from overtaking vehicles without AHS systems. PRACTICAL APPLICATIONS: The experiment demonstrates that AHS has potential to increase bicycling safety by influencing riders to steer away from overtaking vehicles.

Development of a head-weighted injury criterion for evaluation of multiple types of AIS 4+ injuries for vulnerable road users.

Vulnerable Road users (VRUs) often suffer multiple fatal head injury types simultaneously in road accidents. In this study, a head-weighted injury criterion (HWIC4) was proposed for assessing the risk of head AIS 4+ injuries considering multiple injury types. Firstly, the kinematic characteristics of VRUs in the 50 in-depth accidents were reconstructed by using multi-body system models, and head injuries were reconstructed using eight head kinematic-based injury criteria and eight brain tissue injury criteria via the THUMS (Ver. 4.0.2) head finite element model. The predictive capability of each injury criterion to predict head AIS 4+ injuries was assessed and four better predictors (HIC15, angular acceleration, coup pressure, and maximum principal strain) were selected. The different head injury types and the weighting parameters for each injury type were taken into account in the development of HWIC4. Finally, the effectiveness and evaluation of HWIC4 for head AIS 4+ injury was validated based on the area under of receiver operating characteristic (AUROC) curve and reconstruction results from 10 additional selected accident cases. The results showed that HWIC4 has a good predictive capability for head AIS 4+ injuries with an AUROC of 0.983, which means that HWIC4 is superior and more reliable than a single head injury criterion. This knowledge further improves the capability of head injury criteria to predict head AIS 4+ injuries.

A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections.

Bicycle-motor vehicle (BMV) accidents hold paramount importance due to their substantial impact on public safety. Specifically, road intersections, being critical conflict points, demand focused attention to reduce BMV crashes effectively and mitigate their severity. The existing research on the severity analysis of these crashes appears to have certain gaps that warrant further contribution. To address the mentioned limitations, this study first integrates multiple pre-collision features of the bicycles and vehicles to classify crash types based on the mechanism of the crashes. Then, the correlated random parameters ordered probit (CRPOP) model is employed to examine the factors influencing injury severity among bicyclists involved in intersection BMV crashes in Pennsylvania from 2013 to 2018. To gain deeper insights, this study conducts a separate analysis of crash data from 3-leg intersections, 4-leg intersections, and their combined scenarios, followed by a comparative examination of the results. The findings revealed that the presented crash typing approach yields new insights regarding injury severity outcomes. Moreover, in addition to exhibiting a comparable statistical performance contrasting to the more restricted models, the CRPOP model identified hidden correlations between three random parameters. Furthermore, the study demonstrated that analyzing combined crash data from the two intersection types obscured certain factors that were found significantly influential in the injury outcomes through analyzing sub-grouped data. Consequently, it is recommended to implement tailored countermeasures for each type of intersection.

Road traffic mortality in Zunyi city, China: A 10 - year data analysis (2013-2022).

PURPOSE: The study aimed to examine the pattern of motorization and the mortality rate related to road traffic crashes in Zunyi (a city in northern Guizhou province of China) from 2013 to 2022, and to identify the epidemiological characteristics of these crashes with to provide insights that could help improve road safety. METHODS: Data were obtained from the Zunyi traffic management data platform, and the mortality rates were calculated. We deployed various analytical methods, including descriptive analysis, Chi-square test or Fisher's exact test of categorical variable, circular distribution map analysis, and Rayleigh test to characterize the traits of road traffic crashes in the region. RESULTS: During the 10-year study period, 7488 people died due to road traffic accidents, with males accounting for 70.4% and females 29.6% (χ2 = 101.97, p < 0.001). The mortality rate increased from 7.80 deaths per 100,000 people in 2013 to 10.70 deaths per 100,000 people in 2016, but then decreased to 9.54 deaths per 100,000 people in 2019. A notable finding was that the death rate per 10,000 vehicles declined from 16.09 deaths per 10,000 vehicles in 2013 to 5.48 deaths per 10,000 vehicles in 2022. The study also found that vulnerable road users represented nearly half (48.76%) of all accident fatalities, and unlicensed or inexperienced driving contributed significantly to the occurrence of road traffic accidents. CONCLUSION: Although the number of road traffic accidents in Zunyi has decreased, there are still some critical issues that need to be addressed, particularly for vulnerable road users and unlicensed drivers. Our results highlight the need of targeted interventions to address the specific risk factors of road traffic crashes, particularly those affecting vulnerable road users and drivers without sufficient experience or license.

Kinematic responses of child as second rider of electric-two-wheelers under lateral impact with vehicle.

Electric-two-wheeler (E2W) related accidents have become a major safety concern on road due to the growing prevalence and the high casualty rate. Most existing studies focus on drivers of the E2W, while ignore the second rider (usually a child) as passenger. This study aims at investigating the kinematic response of the child rider upon vehicle impact and analyzing how motion patterns are influenced by the geometric parameters of the vehicle and E2W. A computational framework was established for the intended task. We modeled the E2W-rider system in Madymo, including an E2W with parametric geometry and two riders, one adult and one child respectively. This study focuses on lateral impact in terms of the accident scenarios, as the case dominates in the field data reports. Vehicle types, seating height of the E2W and sitting position of the child rider were considered as variables in the simulation matrix. Results show that the relative height between child's sitting and vehicle hood front-edge, and the sitting position (back-seated or front-seated) are two main influencing parameters on kinematic responses of child rider. The child rider tends to bounce higher on hood upon impact when sitting above the hood front-edge, while might be laterally pushed away by the car-front when sitting below the hood front-edge. Meanwhile, back-seated child rider is more likely to rise higher and rotate faster upon impact compared to a front-seated one. These findings may guide safe riding and safety countermeasure development for child riders of E2W.

Seeing through eyes of Drivers: Space consideration in investigating visibility of Vulnerable road users involved in crashes from Driver's perspective.

Vulnerable Road User's (VRUs) invisibility by vehicle drivers hasn't been well explored despite having a substantial influence on crash involvement and resulting severity level. Additionally, obtaining comparison crashes for analysis of the VRU invisibility has been a challenge. For that reason, this study used crashes that occurred between 2017 and 2022 in Ohio to understand VRU invisibility from the driver's perspective. The study further proposes the comparison of crashes as those that occurred within 250 feet of the crashes involving drivers not seeing the VRU. Two logistic regression models, one for the entire dataset (full model) and the second for only crashes that occurred within 250 feet (space-constrained model), were developed. It was found that the results from the full model and space-constrained model differ significantly in terms of the magnitude and the direction of the effect. Using the space-constrained model, the topmost key factors associated with the highest likelihood of VRU invisibility are lighting conditions, pre-action of the driver, and senior VRU involvement. Further, text network analysis was performed to understand the key reasons for VRU invisibility. The text network revealed that the VRU invisibility related to left turning pre-action was due to the driver's failure to yield at an intersection's pedestrian crossing. Further, the most invisible VRUs in the dark conditions were on the side of the roadway. Additionally, drivers backing up were more likely to report that they did not see pedestrians walking behind them. Lastly, senior-related crashes were associated with crossing in front of turning vehicles. The findings can be utilized to enhance VRU visibility at various locations to improve safety.

Analyzing traffic conflicts and the behavior of motorcyclists at unsignalized three-legged and four-legged intersections in Cartagena, Colombia.

INTRODUCTION: The global motorcycle market has grown significantly, with over 770 million vehicles estimated to be in use worldwide. Motorcycle-related road traffic deaths in low and middle-income countries (LMICs) like Colombia are concerning, comprising 30% of all reported fatalities. Cartagena has an average of 70 motorcycle-related deaths annually between 2019 and 2022, making it a high-risk area for motorcyclists. OBJECTIVE: The study aimed to identify factors associated with motorcyclist safety at unsignalized three-legged and four-legged intersections in Cartagena by observing the behavior of the motorcyclists and the analysis of the potential traffic conflicts. The observational analysis focused on the access of motorcyclists from a secondary road to a main road since it is the behavior offered by the most significant road interaction and the potential risk of traffic conflicts due to crossing. METHODS: The observational process was consolidated at ten three-legged intersections and seven four-legged intersections. Thirty-six hours of videos were collected considering different time slots and weekdays randomly distributed during September 2019 and March 2020. The selection of the intersections included different vehicular flows and road safety conditions. The variables considered in the study were: interaction with other road users, motorcyclist behavior, vehicle handling, potential distractors, and safety elements. The study used the Swedish Traffic Conflict Technique to analyze conflict analysis, incorporating the Post Encroachment Time (PET) measurement. The analysis was developed with descriptive and inferential statistical techniques. The collected variables were analyzed individually (frequency analysis), and contrasts were conducted with the PET values. The study evaluated associations between motorcycles and other motorized road actors at intersections about behaviors and crossroads. RESULTS: In the Records, 10,281 motorcycle accesses at three and four-Legged Intersections were interactions with other road users, where 2417 and 1903 resulted in potential traffic conflicts, respectively. Average potential conflicts per hour were 115 and 127 at three and four-legged intersections. At the two intersections, the average PET values in motorcycles were between 2.09 and 2.10 s, while in the other motorized road users, it averaged around 2.67 to 2.71 s. In the road conditions, it was identified that intersections with a traffic flow of<10,000 vehicles/day and poor visibility to the left of the intersection lead to more unsafe conditions for motorcyclists. Motorcycle taxi drivers were the user group most frequently involved in traffic conflicts. Actions on the part of motorcyclists, such as risky behaviors, not using helmets, not using turn signals, and not waiting patiently for access, showed a relationship with the potential for traffic conflicts. Finally, turns to the left, particularly the indirect turn to the left on the opposite road, showed a greater risk of traffic conflicts. CONCLUSIONS: The study found that motorcycles exhibit more severe traffic conflicts than motorized vehicles at intersections. Infrastructure conditions significantly impact the risk of intersection conflicts. Individual behaviors such as not stopping at intersections and driving recklessly increased the risk of traffic conflicts. The study recommends improving infrastructure such as visibility and signaling and implementing separators to reduce travel speed and traffic conflicts for motorcycles.

Blood alcohol concentration as a measure of risk among pedestrian fatalities in the U.S., 2016-2020.

OBJECTIVE: In the United States, deaths among pedestrians have increased dramatically since 2009 relative to other vulnerable road users, with substance use described as an important risk factor. This study aimed to explore blood alcohol concentrations (BAC g/dL) among pedestrian fatalities in the United States between 2016 and 2020. Exploring the presence of alcohol among pedestrian cases will support targeted interventions designed to reduce risk. METHODS: This study utilized pedestrian fatality and alcohol screening data provided by the Fatality Analysis Reporting System (FARS). Logistic models were examined to identify statistical associations (ORs, 95% CI) by age, race, and sex relative to positive BAC exposure (BAC > 0.0 g/dL), mild exposure (BAC > 0.0 < .079 g/dL), moderate to severe alcohol exposure (BAC 0.08-.299 g/dL), and severe exposure (BAC ≥ 0.30 g/dL). RESULTS: Between 2016 and 2020, 33,375 pedestrian fatalities were reported to FARS with 75.1% of cases retained for analysis (n = 25,077). Fatalities were more likely to be White (69.3%), male (69.9%), and between 25-64 years of age (67.3%). 74.0% of fatalities were tested for alcohol, with 40.9% screening positive. Females, cases ≥ 75 years of age, and those identified as Asians reported the lowest odds of being positive for alcohol exposure. CONCLUSIONS: Results suggest an ongoing threat to pedestrians due to alcohol consumption and that exposure odds vary by demographic characteristics. Unfortunately, analytical approaches to understanding the roles played by drugs and alcohol among vulnerable road users tend to be marginalized in the literature. Analytical, evidence-based investigations are needed to curtail the risk of pedestrian fatalities in the U.S.

How do cyclists interact with motorized vehicles at unsignalized intersections? Modeling cyclists' yielding behavior using naturalistic data.

When a cyclist's path intersects with that of a motorized vehicle at an unsignalized intersection, serious conflicts may happen. In recent years, the number of cyclist fatalities in this conflict scenario has held steady, while the number in many other traffic scenarios has been decreasing. There is, therefore, a need to further study this conflict scenario in order to make it safer. With the advent of automated vehicles, threat assessment algorithms able to predict cyclists' (other road users') behavior will be increasingly important to ensure safety. To date, the handful of studies that have modeled the vehicle-cyclist interaction at unsignalized intersections have used kinematics (speed and location) alone without using cyclists' behavioral cues, such as pedaling or gesturing. As a result, we do not know whether non-verbal communication (e.g., from behavioral cues) could improve model predictions. In this paper, we propose a quantitative model based on naturalistic data, which uses additional non-verbal information to predict cyclists' crossing intentions at unsignalized intersections. Interaction events were extracted from a trajectory dataset and enriched by adding cyclists' behavioral cues obtained from sensors. Both kinematics and cyclists' behavioral cues (e.g., pedaling and head movement), were found to be statistically significant for predicting the cyclist's yielding behavior. This research shows that adding information about the cyclists' behavioral cues to the threat assessment algorithms of active safety systems and automated vehicles will improve safety.

Road safety, health equity, and the built environment: perspectives of transport and injury prevention professionals in five Canadian municipalities.

BACKGROUND: Concerns regarding health equity (HE) and the built environment (BE) are well established in the Canadian urban context. Transport and injury prevention professionals across sectors, such as transportation and public health, are involved in designing and implementing BE interventions that enhance the safety of vulnerable road users (VRUs). Results from a larger study examining barriers and facilitators to BE change are used to illustrate how transport and injury prevention professionals perceive HE concerns in their work in five Canadian municipalities. Broadening our understanding of how HE influences the professional BE change context is crucial when advocating for modifications that enhance the safety of equity-deserving VRUs and groups who experience marginalization. METHODS: Interview and focus group data were gathered from transport and injury prevention professionals working in policy/decision-making, transport, police services, public health, non-profit organizations, schools/school boards, community associations, and private sectors across five Canadian urban municipalities: Vancouver, Calgary, Peel Region, Toronto, and Montréal. Data were analyzed using thematic analysis (TA) to illustrate how equity considerations were perceived and applied in participants' BE change work. RESULTS: The results of this study illustrate transport and injury prevention professionals' awareness of the varying needs of VRUs, as well as the inadequacies of current BEs in the Canadian urban context and consultation processes utilized to guide change. Participants emphasized the importance of equitable community consultation strategies, as well as specific BE changes that would support the health and safety of VRUs. Overall, the results highlight how HE concerns inform transport and injury prevention professionals' BE change work in the Canadian urban context. CONCLUSION: For professionals working in urban Canadian transport and injury prevention sectors HE concerns influenced their perspectives of the BE and BE change. These results illustrate a growing need for HE to guide BE change work and consultation processes. Further, these results contribute to ongoing efforts in the Canadian urban context to ensure that HE is at the forefront of BE policy change and decision-making, while promoting existing strategies to ensure that the BE, and related decision-making processes, are accessible and informed by a HE lens.

Sharing roads with automated vehicles: A questionnaire investigation from drivers', cyclists' and pedestrians' perspectives.

Despite the promised benefits, the introduction of Automated Vehicles (AVs) on roads will be confronted by many challenges, including public readiness to use those vehicles and share the roads with them. The risk profile of road users is a key determinant of their safety on roads. However, the relation of such risk profiles to road users' perception of AVs is less known. This study aims to address the above research gap by conducting a cross-sectional survey to investigate the acceptance of Fully Automated Vehicles (FAVs) among different non-AV-user groups (i.e., pedestrians, cyclists, and conventional vehicle drivers). A total of 1205 road users in Queensland (Australia) took part in the study, comprising 456 pedestrians, 339 cyclists, and 410 drivers. The Theory of Planned Behaviour (TPB) is used as the theoretical model to examine road users' intention towards sharing roads with FAVs. The risk profile of the participants derives from established behavioural scales and individual characteristics are also included in the acceptance model. The study results show that pedestrians reported lowest intention in terms of sharing roads with FAVs among the three groups. Drivers and cyclists in a lower risk profile group were more likely to report higher intention to share roads with FAVs than those in a higher risk profile group. As age increased, pedestrians were less likely to accept sharing roads with FAVs. Drivers who had more exposure time on roads were more likely to accept sharing roads with FAVs. Male drivers reported higher intention towards sharing roads than female drivers. Overall, the study provides new insights into public perceptions of FAVs, specifically from the non-AV-user perspective. It sheds light on the obstacles that future AVs may encounter and the types of road users that AV manufacturers and policymakers should consider closely. Specifically, groups such as older pedestrians and road users who engage in more risky behaviours might resist or delay the integration of AVs.

Built environment influence on the incidence of elderly pedestrian collisions in a medium-large city in southern Brazil: a spatial analysis.

Trauma disproportionately affects vulnerable road users, especially the elderly. We analyzed the spatial distribution of elderly pedestrians struck by vehicles in the urban area of Maringa city, from 2014 to 2018. Hotspots were obtained by kernel density estimation and wavelet analysis. The relationship between spatial relative risks (RR) of elderly run-overs and the built environment was assessed through Qualitative Comparative Analysis (QCA). Incidents were more frequent in the central and southeast regions of the city, where the RR was up to 2.58 times higher. The QCA test found a significant association between elderly pedestrian victims and the presence of traffic lights, medical centers/hospitals, roundabouts and schools. There is an association between higher risk of elderly pedestrians collisions and specific elements of built environments in Maringa, providing fundamental data to help guide public policies to improve urban mobility aimed at protecting vulnerable road users and planning an age-friendly city.

Effectiveness of interventions for mobile phone distracted pedestrians: A systematic review.

INTRODUCTION: Mobile phones are used universally due to their versatility and easy-to-use features; this includes when users are walking and when crossing streets. At intersections, using a mobile phone is a secondary task that can distract from the primary task of scanning the road environment and ensuring it is safe to traverse. Such a distraction has been shown to increase risky pedestrian behavior compared to non-distracted behavior. Developing an intervention to make distracted pedestrians aware of imminent danger is a promising approach to refocus pedestrians on their primary task and avoid incidents. Interventions have already been developed in different parts of the world, such as in-ground flashing lights, painted crosswalks, and mobile phone app-based warning systems. METHOD: A systematic review of 42 articles was performed to determine the effectiveness of such interventions. This review found that three types of interventions are currently developed, with differing evaluations. Interventions based on infrastructure tend to be evaluated based on behavioral change. Mobile phone-based apps tend to be evaluated on their ability to detect obstacles. Legislative changes and education campaigns are not currently evaluated. Further, technological development often occurs independently of pedestrians' needs, reducing the likely safety benefits of such interventions. The interventions related to infrastructure mainly focus on warning pedestrians without considering pedestrian mobile phone use, potentially leading to numerous irrelevant warnings and reduced user acceptance. The lack of a comprehensive and systematic approach to evaluating these interventions is also an issue requiring consideration. PRACTICAL APPLICATIONS: This review demonstrates that despite significant recent progress surrounding pedestrian distraction, more work is required to identify the most effective interventions to implement. Future studies with a well-designed experimental framework are necessary to compare the different approaches, and warning messages, and ensure the best guidance for road safety agencies.

Using Machine Learning on V2X Communications Data for VRU Collision Prediction.

Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equipped with communication capabilities, the generated Vehicle-to-Anything (V2X) data can be leveraged by Machine Learning (ML) mechanisms in order to implement such automatic systems. This work proposes a VRU (motorcyclist) collision prediction system, utilizing stacked unidirectional Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (coupling the Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3) tools). The proposed system performed well in two different scenarios: in Scenario A, it predicted 96% of the collisions, averaging 4.53 s for Average Prediction Time (s) (APT) and with a Correct Decision Percentage (CDP) of 41% and 78 False Positives (FPs); in Scenario B, it predicted 95% of the collisions, with a 4.44 s APT, while the CDP was 43% with 68 FPs. The results show the effectiveness of the approach: using ML methods on V2X data allowed the prediction of most of the simulated accidents. Nonetheless, the presence of a relatively high number of FPs does not allow for the usage of automatic safety features (e.g., emergency breaking in the passenger vehicles); thus, collision avoidance must be achieved manually by the drivers.