Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning.

Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian-vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features' local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle-pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.

Injury patterns in motor vehicle collision-youth pedestrian deaths.

OBJECTIVE: The objective of this study was to describe fatal pedestrian injury patterns in youth aged 15 to 24 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using data from medicolegal death investigations of MVCs occurring in the current Canadian motor vehicle (MV) fleet. METHODS: Based on a systematic literature review, MVC-pedestrian injuries were collated in an injury data collection form (IDCF). The IDCF was coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3-6) that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario (OCCO) database of postmortem examinations done at the Provincial Forensic Pathology Unit (PFPU) in Toronto, Canada, and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs and MV dynamics and pedestrian kinematics.The study was approved by the Western University Health Science Research Ethics Board (Project ID: 113440; Lawson Health Research Institute Approval No. R-19-066). RESULTS: There were 88 youth, including 54 (61.4%) males and 34 (38.6%) females. Youth pedestrians comprised 13.1% (88/670) of all autopsied pedestrians. Cars (n = 25/88, 28.4%) were the most frequent type of vehicle in single-vehicle impacts, but collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) were in the majority. Forward projection (n = 34/88, 38.6%) was the most frequent type of pedestrian kinematics. Regardless of the type of vehicle, there was a tendency in most cases for the median MAISBR ≥ 3 to involve the head and thorax. A similar trend was seen in most of the pedestrian kinematics involving the various frontal impacts. Of the 88 cases, at least 63 (71.6%) were known to be engaged in risk-taking behaviors (e.g., activity on roadway). At least 12 deaths were nonaccidental (8 suicides and 4 homicides). Some activities may have been impairment related, because 26/63 (41.3%) pedestrians undertaking risk-taking behavior on the roadway were impaired. Toxicological analyses revealed that over half of the cases (47/88, 53.4%) tested positive for a drug that could have affected behavior. Ethanol was the most common. Thirty-one had positive blood results. CONCLUSION: A fatal dyad of head and thorax trauma was observed for pedestrians struck by cars. For those pedestrians hit by vehicles with high hood edges, which were involved in the majority of cases, a fatal triad of injuries to the head, thorax, and abdomen/retroperitoneum was observed. Most deaths occurred from frontal collisions and at speeds more than 35 km/h.

Temporal instability of the determinants of fatal/severe elderly pedestrian injury outcomes in intersections and non-intersections before, during, and after the COVID-19 pandemic.

This study examines the variability in the impacts of factors influencing injury severity outcomes of elderly pedestrians (age >64) involved in vehicular crashes at intersections and non-intersections before, during, and after the COVID-19 pandemic. To account for unobserved heterogeneity in the crash data, a random parameters logit model with heterogeneity in the means approach is utilized to analyze vehicle-elderly pedestrian crash data from Seoul, South Korea, occurring between 2018 and 2022. Preliminary transferability tests revealed instability in factor impacts on injury severity outcomes, highlighting the need to estimate individual models across various road segments and time periods. Thus, the dataset was segregated by crash location (intersection/non-intersection) and period (before, during, and after COVID-19), with individual models estimated for each group. Results obtained from the analyses revealed that back injuries positively influenced fatalities at non-intersections after the pandemic and was negatively associated with fatalities at intersections before the pandemic. Additionally, several indicators demonstrated significant instability in their impact magnitudes across different road segments and crash years. During the pandemic, head injuries increased the probability of fatalities higher at non-intersections. After the pandemic, crosswalk locations decreased the possibility of fatalities more at intersections. Compared to intersection segments, the female indicator reduced the likelihood of fatal injuries at non-intersections more before, during, and after the pandemic. Before the pandemic, much older pedestrians experienced a greater decline in fatalities at intersections than non-intersections. This instability could be attributed to altered mobility patterns stemming from the COVID-19 pandemic. Overall, the study findings highlight the variability of determinants of fatal/severe injury outcomes among elderly pedestrians across various road segments and years, with the underlying cause of this fluctuation remaining unclear. Furthermore, the findings revealed that accounting for heterogeneity in the means of random parameters enhances model fit and provides valuable insights for safety professionals. The factor impact variability in the estimated models carries significant implications for elderly pedestrian safety, especially in scenarios where precise projections of the effects of alternative safety measures are essential. Road safety experts can leverage these findings to refine or update current policies to enhance elderly pedestrian safety at intersections and non-intersections.

How does street environment affect pedestrian crash risks? A link-level analysis using street view image-based pedestrian exposure measurement.

Street space plays a critical role in pedestrian safety, but the influence of fine-scale street environment features has not been sufficiently understood. To analyze the effect of the street environment at the link level, it is essential to account for the spatial variation of pedestrian exposure across street links, which is challenging due to the lack of detailed pedestrian flow data. To address these issues, this study proposes to extract link-level pedestrian exposure from spatially ubiquitous street view images (SVIs) and investigate the impact of fine-scale street environment on pedestrian crash risks, with a particular focus on pedestrian facilities (e.g., crossing and sidewalk design). Both crash frequency and severity are analyzed at the link level, with the latter incorporating two distinct aggregation metrics: maximum severity and medium severity. Using Hong Kong as a case study, the results show that the link-level pedestrian exposure extracted from SVIs can lead to better model fit than alternative zone-level measurements. Specifically, higher pedestrian exposure is found to increase the total pedestrian crash frequency, while reducing the risk of serious injuries or fatalities, confirming the "safety in numbers" effect for pedestrians. Pedestrian facilities are also shown to influence pedestrian crash frequency and severity in different ways. The presence of crosswalks can increase crash frequency, but denser crosswalk design mitigates this effect. In addition, two-side sidewalks can increase crash frequency, while the absence of sidewalks leads to higher risks of crash severity. These findings highlight the importance of fine-scale street environment and pedestrian facility design for pedestrian safety.

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.

Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?

INTRODUCTION: Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved. METHOD: In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country. RESULTS: The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.

Pedestrian safety on the road to net zero: cross-sectional study of collisions with electric and hybrid-electric cars in Great Britain.

BACKGROUND: Plans to phase out fossil fuel-powered internal combustion engine (ICE) vehicles and to replace these with electric and hybrid-electric (E-HE) vehicles represent a historic step to reduce air pollution and address the climate emergency. However, there are concerns that E-HE cars are more hazardous to pedestrians, due to being quieter. We investigated and compared injury risks to pedestrians from E-HE and ICE cars in urban and rural environments. METHODS: We conducted a cross-sectional study of pedestrians injured by cars or taxis in Great Britain. We estimated casualty rates per 100 million miles of travel by E-HE and ICE vehicles. Numerators (pedestrians) were extracted from STATS19 datasets. Denominators (car travel) were estimated by multiplying average annual mileage (using National Travel Survey datasets) by numbers of vehicles. We used Poisson regression to investigate modifying effects of environments where collisions occurred. RESULTS: During 2013-2017, casualty rates per 100 million miles were 5.16 (95% CI 4.92 to 5.42) for E-HE vehicles and 2.40 (95%CI 2.38 to 2.41) for ICE vehicles, indicating that collisions were twice as likely (RR 2.15; 95% CI 2.05 to 2.26) with E-HE vehicles. Poisson regression found no evidence that E-HE vehicles were more dangerous in rural environments (RR 0.91; 95% CI 0.74 to 1.11); but strong evidence that E-HE vehicles were three times more dangerous than ICE vehicles in urban environments (RR 2.97; 95% CI 2.41 to 3.7). Sensitivity analyses of missing data support main findings. CONCLUSION: E-HE cars pose greater risk to pedestrians than ICE cars in urban environments. This risk must be mitigated as governments phase out petrol and diesel cars.

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 crash risk analysis using extreme value models: New insights and evidence.

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.

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.

Clustering pedestrians' perceptions towards road infrastructure and traffic characteristics.

In India, over 25,000 pedestrian fatalities occur due to road crashes every year. While several studies have identified possible causative factors that contribute to these fatalities, little is known about how pedestrians perceive their surrounding environment. This study attempts to bridge this gap by analysing the pedestrian perception of the built environment and traffic-related aspects considering urban roads (arterial and sub-arterial). Fourteen parameters were selected to assess pedestrian perception, and four factors were derived through factor analysis. The obtained factor scores were then subjected to two-step cluster analysis to determine whether pedestrian perception is different for people from different socio-economic demographics with varying travel behaviour. Based on the results obtained from the descriptive analysis, the respondents were most satisfied with the 'quality of streetlights at sidewalks' and 'visibility/sight distances', while they were most dissatisfied with 'pedestrian volume at sidewalks' and 'lighting facilities at crossings'. From the cluster analysis, it can be summarized that female pedestrians walk less frequently than males and perceive a higher probability of collision or near-collision incidents against male pedestrians. The study findings can aid the policymakers in the assessment of the pedestrian perception of the existing road infrastructure and suggest improvements to ensure pedestrian safety.

Fatal Road Traffic Accidents and Associated Factors in North Shewa Zone, Central Ethiopia: A Cross-Sectional Study.

Background: Road traffic accidents (RTAs) are among the top three global causes of death among people aged 15 to 44 years. More importantly, it is the main cause of death and permanent disability among young people aged 15 to 29 years. This study aimed to assess the magnitude of fatal traffic accidents and the factors associated with them in the North Shewa Zone, Central Ethiopia, from 2013 to 2018. Method: An institution-based cross-sectional study was conducted in all registered RTAs from July 2013 to June 2018 that had full documentation. The data extraction tool was developed based on the daily RTA registration book format that was utilized. Data was entered into Epi-data version 3.1 and then exported to SPSS version 21 for analysis. Logistic regression analysis was used to assess the relationship between the factors and the fatality of RTA. P-values less than 0.05 were reported as statistically significant. Results: Among 846 RTAs studied, 351 (41.5%) were found fatal, while 495 (58.5%) caused non-fatal injuries. Failure to give priority to pedestrians was 2.8 times (AOR = 2.8, 95% CI: 1.3, 5.9) more likely to cause fatal RTAs than drivers who failed to maintain distance between vehicles. Pedestrians were 2.7 times (AOR = 2.7, 95% CI: 1.1, 6.7) more likely to die in RTAs than drivers. Conclusion: The fatality of RTA was high. Failure to give priority to pedestrians and being a pedestrian were strong predictors of death. The North Shewa Zone Traffic Police Department and police officers should focus on enforcing traffic safety laws.

Improving the safety of distracted pedestrians with in-ground flashing lights. A railway crossing field study.

INTRODUCTION: Current signage at intersections is designed for attentive pedestrians who are looking ahead. Such signage may not be sufficient when distracted by smartphones. Illuminated in-ground LED lights at crossings are an innovative solution to alert distracted pedestrians. METHOD: We conducted a field study at a railway crossing equipped with in-ground lights to assess whether distracted pedestrians (N = 34, Mean age 33.6 ±â€¯8.6 years) could detect these lights and how this impacted on their visual scanning and crossing behaviour. This involved a 2 × 3 repeated measures design exploring the impact of the presence (treatment) or absence (control) of in-ground lights (treatment) at a crossing, and a distractor task presented through a mobile device (none, visual, and audio) on eye movements recorded using an eye tracker, and verbal reporting of when participants detected the lights. RESULTS: Participants engaged in the distraction tasks as evidenced by their accuracy and reaction times in all conditions. With both the audio and visual distraction tasks, participants looked at the in-ground LEDs and detected their activation as accurately as when not distracted (95%). While most participants detected the lights at their activation, visual distraction resulted in 10% of the detections occurring as participants entered the rail corridor, suggesting effectiveness in gaining pedestrians' attention. Further, participants were significantly less likely to check for trains when visually distracted (70%), a 10% reduction compared to the no or audio distractor conditions (80% and 78% respectively). The introduction of the in-ground lights resulted in appropriate scanning of the rail tracks (77% and 78% for the visual and auditory distractor tasks respectively) similar to that of non-distracted participants for the crossing without lights (80%). CONCLUSIONS: Our findings indicate that illuminated in-ground lights could be useful in attracting the attention of distracted pedestrians at railway level crossings, and possibly at other road intersections. Practical Applications: Illuminated in-ground lights can be installed at rail and road intersections with known pedestrian distraction as a countermeasure. Further research is necessary to understand their long-term effects.

Using retro-reflective cloth to enhance drivers' judgment of pedestrian walking direction at night-time.

PURPOSE: Fatal pedestrian collisions are over-represented at night and poor conspicuity is believed to be a leading causative factor. Retro-reflective clothing enhances pedestrian conspicuity, particularly when placed in a biological motion or "biomotion" configuration. In this study, we explored how various retro-reflective clothing configurations affected the ability to judge the direction of a pedestrian walking across the road, which has important implications for collision avoidance. METHODS: Participants included 21 young drivers (mean age 21.6 ±â€¯2.0 years) with normal vision. A closed-road circuit was used to assess the accuracy of drivers' judgement of the direction of walking of a pedestrian at night-time wearing one of five different clothing configurations: four with retro-reflective materials placed in different locations (Biomotion, Legs + Torso, Torso Only, Legs Only), and a control wearing only black clothing (Street). Participants were seated in a stationary vehicle with low beam headlamps, 135 m from a pedestrian, who walked across the road from both sides, in different directions (towards the car, straight across the road, or away from the car). Outcome measures included drivers' response accuracy and confidence ratings for judging pedestrian walking direction. RESULTS: Accuracy in judging pedestrian walking direction differed significantly across the clothing configurations (p < 0.001). Response accuracy was significantly higher for the Biomotion configuration (80% correct), compared to the other retro-reflective (Legs + Torso 64%; Torso Only 53%; Legs Only 50%) and Street configurations (33%). Similar trends were noted for confidence ratings across the clothing conditions, yet the relationship between confidence ratings and response accuracy within each clothing configurations was poor. CONCLUSIONS: The use of retro-reflective clothing in a biomotion configuration facilitated the highest accuracy and confidence in drivers' judgment of pedestrian walking direction, compared to other configurations. These findings highlight the importance of using biomotion clothing for pedestrians at night, to not only facilitate drivers' earlier recognition of pedestrians, but also increase their accuracy in determining the walking direction of pedestrians as they cross the road. Practical applications: The use of clothing incorporating retro-reflective material in a biomotion configuration for pedestrians crossing roads at night provides enhanced cues for drivers regarding the presence and walking direction of pedestrians.

Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis.

INTRODUCTION: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. METHODS: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. RESULTS: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. CONCLUSIONS: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle-pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control.

Road traffic accident and management outcome among in Adama Hospital Medical College, Central Ethiopia.

INTRODUCTION: road traffic injuries represent accounts for significant cause of morbidity and mortality around the globe, particularly in developing countries like Ethiopia. Poor pre-hospital care system and delays in hospitals before getting aids added to the woes of mortality. However, there are no study that determine the types of injury, management and outcome of road traffic accidents and associated factors in this study area. METHODS: a hospital based retrospective cross sectional study was conducted among patients attending to Adama Hospital Medical College with accidental injuries from January to December 2015. Data were retrieved from 556 patients registry selected by systematic random sampling from 11,120 injuries visiting the hospital. Data were coded, cleaned and entered to SPSS version 20 for analysis. Factors associated with the management outcome of injury related to road traffic accident were analyzed and statistical significance was declared with p < 0.05 with CI of 95%. RESULTS: out of 556 trauma victims, 304 (54.7%) were due to road traffic accidents followed by personal violence (24%) and falling accident (10.3%). The majorities (74.8%) of patients were male and urban residents (55%). Soft tissue injury was the most frequent type of injury (51%) followed by extremity fracture and dislocation (26%). Delay to come to hospital (over 24 hours), severity of injuries and management types were factors influencing management outcome of injuries related to traffic accidents. About 90.1%, 4.6% and 5.3% of the patients were discharged without any prominent disability, permanent disability and died respectively. CONCLUSION: road traffic accidents are preventable causes of morbidity and mortality. Practices of strict road safety measures and appropriate use of roadways by pedestrians should be in place, while establishing and strengthening early access to hospital and pre-hospital care to save life of injuries.

A new model to estimate pedestrian deaths from speed-related interventions.

OBJECTIVE: The S-shaped function of probability of pedestrian fatality with respect to impact speed of vehicle is well known in road safety literature. However, the implication of this evidence for changes in speed for a population of drivers has not been explored. METHOD: An integrated model has been developed to estimate pedestrian fatalities resulting from changes in speed of traffic. The model uses combined relative risks of injury crash rate of drivers and probability of pedestrian fatality resulting from an injury crash. Two approaches have been used-an individual approach using probability distribution of speed, and an aggregate approach, using only mean speed. The application of the model has been demonstrated using four case studies with speed of traffic before and after an intervention. RESULTS: It is found that even small reductions in mean speed translate to large reductions in pedestrian fatalities. The risk contributed by speeding vehicles is highly disproportionate to their share in vehicle population. In a case study from Delhi (India), in which at-grade junction was replaced with a grade-separated junction, 74% of risk of pedestrian fatalities is contributed by less than 5% speeding vehicles (>50km/h). Changes in mean speed is a poor indicator of changes in injury risk if the standard deviation of the distribution also changes significantly. A surprising finding was that large variation in the S-shape of pedestrian fatality risk function has relatively small effects on overall results. CONCLUSIONS: A new model has been developed to estimate changes in pedestrian fatalities resulting from changes in traffic speed. The application of the model using four speed-related interventions emphasize that deterrence of a small proportion of drivers using speed enforcement can have large implications for pedestrian safety.

Alcohol and drug use among road users involved in fatal crashes in Norway.

OBJECTIVE: To study the prevalence of alcohol and drugs in biological samples from drivers, motorcycle riders, bicyclists, and pedestrians involved in fatal road traffic crashes (RTCs) during 2016-2018 in Norway, both among fatally injured victims and those who survived fatal RTCs. METHODS: Anonymous information was extracted from police data. No personal data were recorded. RESULTS: There were 330 fatal RTCs with 349 killed road users and 384 survivors during the study period; this included 179 passengers who were excluded from the study. In total, 90% of the studied killed road users and 67% of the survivors were investigated for alcohol or drug use by analyzing biological samples. Alcohol or drugs in concentrations above the legal limits were detected in 21% of the analyzed samples. The proportion impaired by alcohol or drugs (blood alcohol concentrations equal to or greater than 0.05%, or drug concentrations above equivalent limits) was highest among killed bicyclists (43%), higher than among killed pedestrians (24%), car and van drivers (28%) and motorcyclists (20%), and significantly higher than among drivers who survived fatal crashes (4%). CONCLUSIONS: Impairment due to use of alcohol or drugs was often a contributing factor among bicyclists, pedestrians and motor vehicle drivers who died in RTCs. Driving under the influence of alcohol or drugs was more often a contributing factor in cases where the motor vehicle driver was killed than in cases where the driver survived.

Recent trends and demographics of pedestrians injured in collisions with cyclists.

INTRODUCTION: Despite extensive media coverage of pedestrians who are injured in collisions with cyclists, little systematic inquiry has been carried out on this topic. This study examines the incidence of pedestrian injuries due to collisions with cyclists in the United States and in New York State and New York City (NYC) from 2005 to 2018. METHOD: The study rests on national data derived from the Nationwide Emergency Department Sample (NEDS) and state and local data gathered by the Statewide Planning and Research Cooperative System (SPARCS). A negative binomial regression analysis was performed on the state and local data to measure the simultaneous effects of demographic variables on the incidence of pedestrian injuries. The study also mapped the incidence of injuries in NYC neighborhoods. RESULTS: Pedestrian injuries due to collisions with cyclists declined at both the national and state and local levels from 2005 to 2018. The decline was particularly pronounced among school-aged children. In NYC, the distribution of injuries was concentrated in certain neighborhoods. CONCLUSIONS: Possible explanations for the decline in injuries include the change in the age composition of NYC's population, the greater level of physical inactivity among school-aged children, stricter enforcement of traffic laws, and, importantly, improvements in the cycling infrastructure. Practical Applications: Cycling as a mode of transportation is continuing to grow in popularity, particularly in large cities in the United States and Europe. With this upsurge in popularity, it is important to create a safe environment for all road users. Improvements in the cycling infrastructure (especially the installation of protected bike lanes) reduce hazards not only to cyclists but to pedestrians as well.

Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach.

INTRODUCTION: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. METHOD: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. RESULTS: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.