Pedestrian fatality risk in accidents at unsignalized zebra crosswalks in Poland

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.

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.

The Effect of Changing the Angle of the Passenger Car Seat Backrest on the Head Trajectories of the 50th Percentile Male Dummy

The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver's position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver's injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy's head showed a high risk of whiplash injury in people sitting in a fully reclined seat.

Factors influencing the road accidents in low and middle-income countries: a systematic literature review

This paper studies the main factors affecting road traffic accidents (RTAs) using a systematic review. The primary focus is on factors related to road characteristics and driver behaviours. This review also addresses the socioeconomic and demographic factors to provide a clear overview of which groups suffer the most from RTAs. Several factors were found to affect RTAs, notably road characteristics: highways, high-speed roads, unplanned intersections and two-way roads without dividers; driver behaviours: reckless/aggressive driving and riding, excessive speeding, unawareness of traffic laws, and not using safety equipment; and vehicle types: four and two-wheeled. This review found that male and economically productive people with less education were mostly associated with RTAs. In addition, for most of the low and middle-income countries analyzed, there is a lack of quality data relating to RTAs. Nevertheless, this review provides researchers and policy makers with a better understanding of road accidents for improving road safety.

Comparative analysis of pedestrian crash severity at United Kingdom rural road intersections and Non-Intersections using latent class clustering and ordered probit model

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

Modeling effects of roadway lighting photometric criteria on nighttime pedestrian crashes on roadway segments

INTRODUCTION

Nighttime crashes account for 74% of pedestrian fatalities in the United States, and reduced visibility is a significant cause of nighttime pedestrian crashes. Maintaining sufficient and uniform roadway lighting is an effective countermeasure to improve pedestrian visibility and prevent nighttime pedestrian crashes and injuries. Previous studies have not quantified the safety effects of roadway photometric patterns (i.e., average lighting level and uniformity) on nighttime pedestrian crashes on roadway segments.

METHOD

This study investigated the association between two roadway photometric criteria (horizontal illuminance mean representing average lighting level and horizontal illuminance standard deviation representing lighting uniformity) and nighttime pedestrian crash occurrence in Florida roadway segments. The matched case-control method was used to decouple the confounding effects between the illuminance mean and standard deviation. Statistically-significant crash modification factors (CMFs) were developed to quantify the safety effects of the mean and standard deviation of horizontal illuminance on nighttime pedestrian crashes.

RESULTS

The results show that if the average lighting level on a roadway segment is increased from a low illuminance mean (<0.2 foot-candle [fc]) to a medium illuminance mean [0.2 fc, 0.5 fc], a medium-high illuminance mean (0.5 fc, 1.0 fc], and a high illuminance mean (>1.0 fc), the relative likelihood of nighttime pedestrian crashes on midblock segments in Florida tends to be reduced by 77.5% (CMF = 0.225), 81.2% (CMF = 0.188), and 85.5% (CMF = 0.145), respectively.

PRACTICAL APPLICATIONS

A poor uniformity (illuminance standard deviation ≥ 0.52 fc) is likely to increase the relative likelihood of nighttime pedestrian crashes on midblock segments in Florida by 80.3% (CMF = 1.803) compared to good uniformity (illuminance standard deviation < 0.52 fc).

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.

Using ordered and unordered logistic regressions to investigate risk factors associated with pedestrian crash injury severity in Victoria, Australia

INTRODUCTION

The safety of pedestrians is a major concern in Victoria, Australia. Despite the considerable number of pedestrian fatalities and injuries in traffic crashes, a limited number of studies focused on pedestrian crash severity in Victoria.

METHODS

This study investigates and identifies the influential factors determining the severity of pedestrian injuries in traffic crashes in Victoria by using crash data from 2010 to 2019. An unordered multinomial logit model and an ordered logit model are developed for this purpose.

RESULTS

The results indicate that pedestrian crashes on weekends, in the period of 10 a.m. to 10 p.m., on dark streets, at intersections, in areas with a speed limit above 50 km/h, and on medians or footpaths are associated with a higher probability of severe and fatal injuries. Male pedestrians, children, and older adults (>59) were more likely to sustain a higher level of injury in crashes. Concerning the driver characteristics, no significant relationship was found between pedestrian injury severity and driver gender and license status, but older drivers were more likely to cause severe and fatal injuries. Pedestrian collisions with motorcycles, heavy vehicles, light commercial vehicles, bus/minibus/coach, and trams increase the probability of more severe injuries compared to cars. Moreover, older vehicles are associated with a higher probability of severe pedestrian injuries. Comparison of the model results illustrated that the MNL model was slightly better fitted on the data than the ordered logit model, but the conclusions inferred from these two models were generally similar.

PRACTICAL APPLICATION

To reduce the injuries of pedestrian crashes, we recommend improving lighting conditions and sidewalk design, implementing speed reduction strategies at high pedestrian activity areas, introducing more pedestrian crossings at midblock, installing warning signs to drivers, and discouraging the use of vehicles that are more than 20 years old.

Pedestrian Safety at Midblock Crossings on Dual Carriageway Roads in Polish Cities

Road crossings across two or more lanes in one direction are particularly dangerous due to limited sight distance and high vehicle speeds. To improve their safety, road authorities should provide safety treatments. These may include additional measures to reduce speed and narrow the road cross-section and the introduction of active pedestrian crossings. Equipped with flashing lights activated automatically when a pedestrian is detected, the crossings are painted red and have an anti-skid surface on approaches. The article presents an analysis of road user behaviour at pedestrian crossings on dual carriageways with a varying provision of road safety measures in some Polish cities. It also evaluates the effectiveness of the measures over time. The study was conducted before, immediately after and one year after the additional signage was introduced. The evaluation is based on how vehicle speeds changed before the pedestrian crossing, how pedestrians behaved versus the vehicle and their readiness to cross the street. The number of conflicts on selected crossings was also evaluated. The safety treatments under analysis were found to be less effective than the traditional pedestrian safety measures such as speed cushions or roads narrowed to one lane. This suggests that if used on dual carriageways the measures should only be temporary and should ultimately be replaced with traffic lights or a grade separated solution (a footbridge or tunnel) on exits from urban areas. No clear-cut conclusions about pedestrian safety can be drawn based on the traffic conflicts in question. The article is divided into the following sections: introduction with a review of the literature on pedestrian and driver behaviour studies at pedestrian crossings, including midblock crossings and dual carriageways; a description of the research method and test sites, the results, discussion of the results and conclusion.

Vision-Based Pedestrian's Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users’ risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.

Driving behavior at midblock crosswalks with Rectangular Rapid Flashing Beacons: Hidden Markov model approach using naturalistic data

Pedestrian fatalities have increased in the U.S. with the largest increase being observed on urban arterials and away from intersections. Rectangular Rapid Flashing Beacon (RRFB) has been widely implemented as a safety countermeasure to improve driver awareness and visibility of pedestrians, especially for midblock crosswalks. Studies show that drivers are more likely to yield to pedestrians at crosswalks with an RRFB. These studies are often based on a binary outcome of whether or not drivers yield to pedestrians. Nevertheless, it is also important to consider the drivers' deceleration behavior as a dynamic process at these crosswalks and the impact of pedestrians being present or not. Understanding this dynamic behavior and the related circumstances can provide information on the design of alerting systems that help drivers make more appropriate decisions at these crosswalks to avoid a vehicle-pedestrian crash. This study examined this research topic using Hidden Markov Models (HMMs) and data from a naturalistic study. More specifically, four HMMs were applied to the naturalistic brake and jerk data from the Safety Pilot Model Deployment (SPMD) program given drivers' intention to slow down, the RRFB activation status, and the presence of pedestrians. The time-based data sequence was converted to distance-based through a moving window to enhance result comparison and interpretation. Grid-search was used to select the best moving window parameters and the optimal number of hidden states. This study confirmed the high compliance at an activated RRFB when pedestrians were present. Even without pedestrians, one in five traversals showed drivers slowing down to less than 8.94 m/s (20 mph) within 35 m of the crosswalk. Model results further indicate that drivers started braking as far back as 180 m before the crosswalk and stopped braking from 70 m before the crosswalk at an activated RRFB without pedestrians. When there were pedestrians, drivers would start braking 20 to 30 m later but would brake more firmly and for longer. Finally, drivers were not likely to brake or decelerate when RRFB was off and no pedestrians were present.

[Analysis of 5-year monitoring of road traffic injuries rates in Kazan]

The objective of the study is to establish a set of possible causes and patterns that influence the frequency and severity of road traffic accidents with fatalities. The results of the 5-year monitoring analysis of fatal road traffic injuries in Kazan are presented. We analyzed 662 cases. It was established that the main risk group for fatal road accidents includes subjects aged 20-39. The risk group for drivers is 30-39 years old, passengers 20-29 years old, and pedestrians 55-64 years old. Children account for 3% of all traffic fatalities; 80% of them are under 10 years old. Most children (70%) were in the vehicle at the time of the crash, and their parents may not have been using special equipment (car seats and seat belts). We identified the most traumatic months of the year, days of the week and times of the day for drivers, passengers and pedestrians. These patterns can be used for organizational measures to prevent road accidents.

Applying count time series to assess 13-year pedestrian mortality trend caused by traffic accidents in East-Azerbaijan province, Iran

In populated cities, pedestrian mortality is higher compared to other traffic mortalities. The current study aimed to describe the trend of pedestrian mortality in the East-Azerbaijan (Northwest of Iran) province from 2006 to 2019 and find the factors that affect the mortality number. Pedestrian mortality data from March 2006 to March 2019 was obtained from the Legal Medicine Organization database of Iran. Generalized Linear Auto Regressive Moving Average (GLARMA) models were used to assess the trend, and affecting factors of pedestrian mortality. According to the traffic accident data from 21 March 2006 until 20 March 2019 in East-Azerbaijan 24.11% of mortalities are related to pedestrians. Pedestrian mortality had a decreasing seasonal trend during 2006-2019. The result of the GLARMA model showed that age >65, being non-educated, cases with head trauma death cause, pre-hospital death, accident inside the city, vehicle type and self-employed jobs had a direct relation to pedestrian's mortality.

Fatal pedestrian crashes at intersections: Trend mining using association rules

In 2018, about 6,677 pedestrians were killed on the US roadways. Around one-fourth of these crashes happened at intersections or near intersection locations. This high death toll requires careful investigation. The purpose of this study is to provide an overview of the characteristics and associated crash scenarios resulting in fatal pedestrian crashes in the US. The current study collected five years (2014-2018) of fatal crash data with additional details of pedestrian crash typing. This dataset provides specifics of scenarios associated with fatal pedestrian crashes. This study applied associated rules mining on four sub-groups, which were determined based on the highest frequencies of fatal crash scenarios. This study also developed the top 20 rules for all four sub-groups and used 'a priori' algorithm with 'lift' as a performance measure. Some of the key variable categories such as dark with lighting condition, vehicle going straight, vehicle turning, local municipality streets, pedestrian age range from 45 years and above are frequently presented in the developed rules. The patterns of the rules differ by the pedestrian's position within and outside of crosswalk area. If the pedestrian is outside the crosswalk area, no lighting at dark is associated with high number of crashes. As lift provides quantitative measures in the form of the likelihood, the rules can be transferred into data-driven decision making. The findings of the current study can be used by safety engineers and planners to improve pedestrian safety at intersections.

Active Signage of Pedestrian Crossings as a Tool in Road Safety Management

The main objective of the study was to verify the effectiveness of active pedestrian crossings equipped with flashing lights activated automatically by detected pedestrians. A pilot study was conducted in two sites, where speed profiles of vehicles over the distance of 30 m before the crossing were analyzed. The study produced promising results in terms of reducing vehicle speeds so the next study investigated four other unsignalized pedestrian crossings. They were video-recorded for 48 h each, before, after and a year after installation. The ANOVA test was used to check the statistical significance of changes in selected indicators. Even after a year from the installation, the effect of the active signage remained significant. The average percentage of drivers yielding to pedestrians was 77.4% higher and the average waiting time 25.2% lower than before the installation. The average speeds of vehicles were 3.53 km/h lower on collector and 2.60 km/h lower on arterial streets. A decline in the probability of a pedestrian being killed or severely injured (KSI) ranged from 6.3 pp (9.4%) on the arterial streets immediately after the installation up to 12.9 pp (31.7%) on the collector streets one year after.

Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA)

Background

Identifying hidden patterns and relationships among the features of the Fatal Pedestrian Road Traffic Injuries (FPRTI) can be effective in reducing pedestrian fatalities. This study is thus aimed to detect the patterns among the fatally injured pedestrians due to FPRTI in East Azerbaijan province, Iran.

Methods

This descriptive-analytic research was carried out based on the data of all 1782 FPRTI that occurred in East Azerbaijan, Iran from 2010 to 2019 collected by the forensic organization. Categorical Principal Component Analysis (CATPCA) was performed to recognize hidden patterns in the data by extracting principal components from the set of 13 features of FPRTI. The importance of each component was assessed by using the variance accounted for (VAF) index.

Results

The optimum number of components to fit the CATPCA model was six which explained 71.09% of the total variation. The first and most important component with VAF = 22.04% contained the demographic and socioeconomic characteristics of the killed pedestrians. The second-ranked component with VAF = 12.96% was related to the injury type. The third component with VAF = 10.56% was the severity of the injury. The fourth component with VAF = 9.07% was somehow related to the knowledge and observance of the traffic rules. The fifth component with VAF = 8.63% was about the quality of medical relief and finally, the sixth component with VAF = 7.82% dealt with environmental conditions.

Conclusion

CATPCA revealed hidden patterns among the fatally injured pedestrians in the form of six components. The revealed patterns showed that some interactions between correlated features led to a higher mortality rate.

Assessment of the Influence of Road Infrastructure Parameters on the Behaviour of Drivers and Pedestrians in Pedestrian Crossing Areas

Pedestrians are participants and, most likely, fatalities in every third road traffic accident in Poland. Over 30% of all fatalities on Polish roads are pedestrians. Accidents with pedestrians are very often the result of various factors related to the infrastructure and behaviour of pedestrians and drivers. The objective of the work was to assess driver and pedestrian behaviour in pedestrian crossing areas. The research also served as a pilot study for similar work to be conducted across Poland, and constituted the basis for monitoring the behaviour of road users in the area of pedestrian crossings. Parameters which must be analysed were identified on the basis of field studies. Principles of selecting test sites were adopted, and measurement methods for pedestrian crossing areas are presented. The influence of the location of the selected test cross-section infrastructure parameters on the behaviour of road users in pedestrian crossing areas is demonstrated. The results of the study will be used as a basis for new solutions involving pedestrian crossing infrastructure designed to improve pedestrian safety. The results were also used in formulating new regulations for the design and maintenance of pedestrian crossings and recommendations for road safety auditors.

SafetyCube: Framework for potential pedestrian risk analysis using multi-dimensional OLAP

In the past decade, the number of road traffic accidents and fatalities has remained about the same level. One of strategies to protect vulnerable road users (VRUs) is to analyze the factors that cause traffic accident and then to deploy safety facilities. However, most traffic safety systems currently in operation rely on historical data, which is post-facto approach. Thus, it is necessary to prevent accident in advance and to respond in proactive manner before the accident. In this study, we propose a framework for potential pedestrian risk analysis using a multi-dimensional on-line analytical processing (OLAP), called SafetyCube, which enables decision-makers to understand the situations by scrutinizing interactive behaviors between vehicle and pedestrian. First, we collect the behavioral features of traffic-related objects (e.g., vehicles and pedestrians) extracted from closed circuit televisions (CCTVs) deployed on crosswalks throughout the overall urban, and accumulate them in a data warehouse over an extended period in order to construct a data cube model. Then, we conduct comprehensive analyses in multi-dimensional perspective using OLAP operations by varying the abstraction levels. Our analytical experiments are based on three scenarios, and the results show that the vehicle's movement patterns before entering the crosswalk, patterns of changes in speed of vehicles approaching to pedestrians, and so on. Through these results from the proposed analytical system, decision-makers can gain a better understanding of how the vehicles and pedestrians behave near the crosswalk by visualizing their interactions. Further, these insights would be reflected to improve the road environment safer. In order to validate the feasibility and applicability of the proposed system, we apply it to various crosswalks in Osan city, South Korea.

Flashing in-curb LEDs and beacons at unsignalized crosswalks and driver's visual attention to pedestrians during nighttime

Driver's visual attention (eye movements) and driving behaviour (kinematic data) were assessed when approaching an experimental crosswalk that included flashing white in-curb LED strips, flashing orange beacons, backlit 'Yield here to pedestrians' vertical signs, and enhanced lighting when a staged pedestrian attempted to cross. An experimental condition in which all devices were active was compared with a control condition in which only enhanced lighting and backlit vertical signs were active. The results showed a significant increase of motorists' yielding compliance, distance of first fixation to the pedestrian area, standard deviation for horizontal eye movements in the experimental condition. The introduction of flashing in-curb LED strips and flashing orange beacons proved to be very effective in increasing the night-time safety of the pedestrian crossing. Practitioner summary: The study investigated the effects of flashing in-curb LED strips and beacons on driver's visual attention (eye movements) and speed when approaching a crosswalk during night-time. The results showed that the combination of these flashing devices significantly increased yielding compliance and the distance of pedestrian detection. Abbreviations: ANOVA: analysis of variance; EU: European Union; HAWK: high intensity activated crosswalk system; LED: light-emitting diode; PHB: pedestrian hybrid beacons; ROI: region of interest; RRFB: rectangular rapid flashing beacons; UK: United Kingdom; US: United States.

Older adult pedestrian trauma: A systematic review, meta-analysis, and GRADE assessment of injury health outcomes from an aggregate study sample of 1 million pedestrians

This systematic review sought to assess older adult pedestrian injury severity, injury by anatomical location and incidence proportions, including comparisons to younger age groups when available and provide an analysis of the quality of the existing evidence. A structured search was conducted in PubMed, Embase, Scopus, CINAHL, PsycInfo, AMED, Web of Science, LILACS and TRID. STROBE was used to assess the reporting quality of the included studies. Random-effect model meta-analysis served to obtain pooled relative risk, incidence proportions and standardized mean differences for different outcomes due to pedestrian crashes comparing older and younger pedestrians, while meta-analyses could not be conducted for pedestrian falls. We screened 7460 records of which 60 studies (1,012,041 pedestrians) were included in the review. Injured pedestrians 60+ compared to those <60 were found to have a higher relative risk of severe injury (pooled relative risk RR 1.6, 95 % CI: 1.4-2.0 p < 0.001), critical care admission (pooled RR 1.5, 95 %CI: 1.3-1.8 p < 0.001), and fatality (pooled RR of 3.7, 95 % CI: 3.0-4.5 p < 0.001). Pedestrians 60+ also had higher incidence rates of pedestrian falls causing higher injury severity. GRADE was used to evaluate evidence quality, with the results suggesting that the overall quality of the evidence supporting these findings was low. Further research is needed to understand health risks associated with older pedestrian trauma and to develop effective risk management strategies.

A state-of-the-art review of factors that predict mortality among traumatic injury patients following a road traffic crash

BACKGROUND

Traffic related injuries are a major public health problem worldwide with millions of people dying every year. The objective of this state-of-the-art review was to identify the factors reported in the literature as being associated with mortality for trauma patients following road traffic crashes.

METHOD

A systematic search was undertaken of PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, and Cochrane Library databases to identify articles published in the past two decades (2000-2020). Of 8257 records, 4507 remained for title, abstract and full text screening after duplicates were removed. The level of evidence of selected studies was assessed using The National Health and Medical Research Council (NHMRC) guideline.

RESULTS

This review included eighty primary research studies examining mortality risk factors following a road traffic crash. The study identified factors in five categories; (i) demographic factors; (ii) behavioural factors; (iii) crash characteristics; (iv) environmental and timing factors; (v) injury severity and pre-injury/condition. The primary studies are summarised in a matrix. Included studies included level II to level IV levels of evidence based on the NHMRC criteria.

CONCLUSION

This study shows that there are a large number of factors associated with increased risk of mortality following diverse types of traffic crashes. Understanding these wide-ranging factors can strengthen injury and mortality prevention by guiding decision makers about where to focus strategy implementation.

"My right to walk, my right to live": pedestrian fatalities, roads and environmental features in Benin

Background

The implementation of road safety interventions in many developing countries usually focuses on the behavior of users. In order to draw more attention on the role of road infrastructure and physical environment in road safety interventions, this study aims to analyze the environmental and road factors associated with the pedestrians involved in traffic crashes in Benin.

Method

The method used was an analysis of national road crash statistics for the period 2008 to 2015. The information available included the circumstances surrounding the collision, the road infrastructure, the vehicles and the individuals involved. A multiple logistic regression was used to identify predictors of pedestrian mortality in traffic crashes.

Results

During the period studied, 3760 crashes involved at least one pedestrian. The death rate among these pedestrians was 27.74% (CI 95%: 26.31–29.20). The mortality predictors were the area in which the crash occurred (OR = 4.94; CI 95%: 4.10–5.94), the day of the crash (OR = 2.17; CI 95%:1.34–3.52), light levels (OR = 1.30; CI 95%: 1.06–1.59), road classification (OR = 1.79; CI 95%: 1.46–2.20), the condition of the road surface (2.04, CI 95%: 1.41–2.95) and the position of the pedestrian during the crash (OR = 1.69; CI 95%: 1.19–2.38).

Conclusions

These results support the need for a holistic approach to interventions aiming to tackle deaths on roads. Interventions should integrate environmental factors for greater pedestrian safety around roads with appropriate signs, roads in good condition and awareness campaigns for a proper use of road infrastructures.

Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and 79,664 Accidents

Predictive factors for fatal traffic accidents have been determined, but not addressed collectively through a predictive model to help determine the probability of mortality and thereby ascertain key points for intervening and decreasing that probability. Data on all road traffic accidents with victims involving a private car or van occurring in Spain in 2015 (164,790 subjects and 79,664 accidents) were analyzed, evaluating 30-day mortality following the accident. As candidate predictors of mortality, variables associated with the accident (weekend, time, number of vehicles, road, brightness, and weather) associated with the vehicle (type and age of vehicle, and other types of vehicles in the accident) and associated with individuals (gender, age, seat belt, and position in the vehicle) were examined. The sample was divided into two groups. In one group, a logistic regression model adapted to a points system was constructed and internally validated, and in the other group the model was externally validated. The points system obtained good discrimination and calibration in both the internal and the external validation. Consequently, a simple tool is available to determine the risk of mortality following a traffic accident, which could be validated in other countries.

Alcohol and Road Accidents Involving Pedestrians as Unprotected Road Users

According to the World Health Organization (WHO), more than half of all road fatalities involve vulnerable road users, i.e., pedestrians, cyclists, and motorcyclists. Poland is classified as one of the European Union (EU) countries marked by low road safety, with a higher frequency of accidents involving pedestrians compared to other EU countries (31% of all fatalities). Among unprotected road users, a significant group of victims are pedestrians, who are often under the influence of alcohol. This study aims to analyze the impact of alcohol on the risk of occurrence and consequences of road accidents among pedestrians. The source of data was the medical documentation of the Department of Forensic Medicine of the Medical University of Warsaw. In more than half of pedestrian deaths, the presence of alcohol was found; regardless of the place of the event and the place of death, among the victims under the influence of alcohol, males dominated; the average age of the victims under the influence of alcohol was significantly lower compared to the average age of sober victims, with younger victims being significantly more likely to die at the scene of the accident, especially in rural areas; significantly higher alcohol concentrations were found in males, in victims who died at the scene of the accident, and with victims of accidents in rural areas. Among pedestrian traffic accident fatalities, the most numerous group comprised young men under the influence of alcohol. In rural areas, a higher percentage of pedestrian victims died at the scene as a result of excessive alcohol consumption. These areas should be subject to intensive preventive measures to increase the safety of pedestrians as unprotected road users.

The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms

In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.

Vision-Based Potential Pedestrian Risk Analysis on Unsignalized Crosswalk Using Data Mining Techniques

Though the technological advancement of smart city infrastructure has significantly improved urban pedestrians’ health and safety, there remains a large number of road traffic accident victims, making it a pressing current transportation concern. In particular, unsignalized crosswalks present a major threat to pedestrians, but we lack dense behavioral data to understand the risks they face. In this study, we propose a new model for potential pedestrian risky event (PPRE) analysis, using video footage gathered by road security cameras already installed at such crossings. Our system automatically detects vehicles and pedestrians, calculates trajectories, and extracts frame-level behavioral features. We use k-means clustering and decision tree algorithms to classify these events into six clusters, then visualize and interpret these clusters to show how they may or may not contribute to pedestrian risk at these crosswalks. We confirmed the feasibility of the model by applying it to video footage from unsignalized crosswalks in Osan city, South Korea.

The injury epidemiology of adult riders in vehicle-two-wheeler crashes in China, Ningbo, 2011-2015

INTRODUCTION

We used road crashes between vehicles and two-wheelers from Yinzhou District Ningbo in 2011-2015 from the China In-depth Accident Study (CIDAS) as sample cases. The risk factors of different injury severity grades were analyzed.

METHOD

The classification tree model was used to screen the possible interaction items, and the corresponding regression model was constructed according to the results of the tree model to explore the influencing factors of cyclist injury.

RESULTS

The road types, weather types, gender, age of the riders, and vehicle speed were significantly correlated with the dependent variables. The interaction effect of gender*road type, weather*age, weather*speed and speed*age were obtained through a tree model.

CONCLUSIONS

The risk of male casualties at the crossroads was 3.31 times higher than that of female casualties at the straight roads. On sunny days, the risk of crash casualties in Ningbo was low, and the fatality risk when the speed reached 38 km/h was 10%. Under the interaction effect of weather and age, the fatality risk in cloudy/foggy and rainy days was almost coincident, and the serious risk in cloudy/foggy conditions was the highest. Practical applications: Through factor analysis, it is confirmed that there is interaction effect among the factors, and it can provide reference for relevant departments to formulate more targeted and effective governance strategies.

Evaluation of the Brake’s Performance Dependence Upon Technical Condition of Car Tires as a Factor of Road Safety Management

The social cost, as one of the factors determining sustainability of socio-economic development, is strongly dependent upon a number of casualties and mortality in road accidents. The condition of car tires appears to be one of the important factors determining the occurrence of accidents. The vast majority of vehicles are tested every year at vehicle inspection stations. One of the elements affecting the result of the technical condition test and basically the quality of vehicle braking is the technical condition of the tires. Their technical condition is a very important factor responsible for the quality of acceleration, braking, maintaining, or changing the direction of driving. As a consequence, it has a significant impact on road safety. The aim of the study is to examine the impact of tires on the results of tests performed at a vehicle inspection station. The study presents the results of bench measurements of the impact of selected features of tire condition of two vehicles during routine periodic inspections at a vehicle inspection station (VIS). The focus was on an attempt to assess the impact of inflation pressure, age, and tire tread wear on the braking process. The technical studies performed might be a source for legal steps assuring better management of road safety. It can also be expected that the tire choice and condition may affect fuel consumption, and therefore the amount of energy consumed by the road transport.

Predictors of fatal outcomes in pedestrian accidents in Tabriz Metropolis of Iran: Application of PLS-DA method

Objectives: Road traffic deaths in walking pedestrians are a global public health problem. Considering that in Iran pedestrians have a high proportion of deaths caused by traffic accidents, the objective of the present study was to investigate mortality rate and related factors of fatal injury in pedestrian crashes in Tabriz Metropolis of Iran as the largest and most populous city of the northwest of Iran.Methods: The design of this study is case-control based on police and Forensic Medicine Organization data. All registered fatal pedestrian crashes in Tabriz Metropolis from 2014 to 2015 (146 cases) were included in the study as the case group. Also, 292 pedestrians (the ratio of cases to controls was 1:2) with non-fatal crashes were considered as the control group. Due to high dimensional data and multicollinearity issue, Partial least squares discriminant analysis (PLS-DA) was used for data analysis. Importance of the variables was determined by the VIP (Variable Importance in the Projection) index. Performance of the model was assessed by using training and test set validation method. The area under the ROC curve (AUC) and classification error rates were calculated for the test set. R software version 3.5.1 (mixOmcs packages) was used for data analysis.Results: According to the results of PLS-DA, the most important variables related to fatal outcome in pedestrian crashes with VIP > 1 were: pedestrian age (positive effect), type of vehicle (light machinery with a negative effect), kind of vehicle plate (private plate with a negative effect), season of accident occurrence (winter season with a positive effect), type of driver's licenses (Class A with a positive effect), pedestrian gender (male with a positive effect) and Fault of Pedestrian (At-fault with a positive effect). The overall accuracy for the fitted model and AUC were 0.77 and 0.79, respectively.Conclusions: The results show that predictors of a fatal outcome in pedestrian accidents in Tabriz can be attributed to the pedestrian characteristics (which notably account for differences in vulnerability in case of an accident), the car and driver features, and weather (which may all notably influence the amount of energy involved in the collision, through the car mass, speed, and conditions delaying the braking response or reducing the braking effectiveness). Regarding the statistical method, the PLS-DA is a powerful method which can be used to analyze high dimension data with multicollinearity issue.

Walking against or with traffic? Evaluating pedestrian fatalities and head injuries in Taiwan

BACKGROUND

Allowing contraflow cycling on one-way streets has been reported to reduce crash risks in Belgium and the United Kingdom. Similarly, walking against traffic on roadways without sidewalks substantially improves pedestrian safety. This study examined fatalities and head injuries sustained by pedestrians in against-traffic and with-traffic crashes.

METHODS

Using police-reported crash data in Taiwan between 2011 and 2016, fatalities and head injuries were compared for pedestrians involved in against-traffic and with-traffic crashes.

RESULTS

Of the 14,382 pedestrians involved in crashes, 10,749 and 3633 pedestrians in with-traffic and against-traffic crashes, respectively, were reported. Compared with pedestrians involved in against-traffic crashes, those in with-traffic crashes were more likely to sustain fatalities and head injuries. Results of logistic regression models revealed several influential factors on pedestrian fatalities and head injuries, including elderly pedestrians, male drivers, intoxicated drivers, rural roadways, unlit streets in darkness, limited sight distance, adverse weather conditions, midnight hours, and a heavy vehicle as the crash partner.

CONCLUSIONS

Pedestrians in with-traffic crashes were more likely to sustain fatalities and head injuries compared with those in against-traffic crashes. Furthermore, the negative effect of walking with traffic on injuries was more pronounced in reduced-visibility conditions.

Assessing the Effects of the Road Surface and Weather Conditions on Road Safety

When transport is analysed for its safe delivery the focus is mainly on assessing the safety of roads and the risk of undesired events. These include road accidents, an occurrence which is relatively rare and random. Randomness means that accidents depend on several factors which are partly deterministic and partly. The authors conducted an assessment of how weather conditions affect the risk of an accident due to the factor’s significant contribution to road accidents. This case involves a combination of two sources of hazard: external conditions in the form of the weather which are beyond the driver’s control and anthropotechnical factors that address vehicle condition and driver behaviour controlled by the driver.

The relationship between impact speed and the probability of pedestrian fatality during a vehicle-pedestrian crash: A systematic review and meta-analysis

BACKGROUND

Pedestrians struck in motorised vehicle crashes constitute the largest group of traffic fatalities worldwide. Excessive speed is the primary contributory factor in such crashes. The relationship between estimated impact speed and the risk of a pedestrian fatality has generated much debate concerning what should be a safe maximum speed limit for vehicles in high pedestrian active areas.

METHODS

Four electronic databases (MEDLINE, EMBASE, COMPENDEX, and SCOPUS) were searched to identify relevant studies. Records were assessed, and data retrieved independently by two authors in adherence with the PRISMA statement. The included studies reported data on pedestrian fatalities from motorised vehicle crashes with known estimated impact speed. Summary odds ratios (OR) were obtained using meta-regression models. Time trends and publication bias were assessed.

RESULTS

Fifty-five studies were identified for a full-text assessment, 27 met inclusion criteria, and 20 were included in a meta-analysis. The analyses found that when the estimated impact speed increases by 1 km/h, the odds of a pedestrian fatality increases on average by 11% (OR = 1.11, 95% CI: 1.10-1.12). The risk of a fatality reaches 5% at an estimated impact speed of 30 km/h, 10% at 37 km/h, 50% at 59 km/h, 75% at 69 km/h and 90% at 80 km/h. Evidence of publication bias and time trend bias among included studies were found.

CONCLUSIONS

The results of the meta-analysis support setting speed limits of 30-40 km/h for high pedestrian active areas. These speed limits are commonly used by best practice countries that have the lowest road fatality rates and that practice a Safe System Approach to road safety.

A pedestrian serious injury risk prediction method based on posted speed limit

The purpose of this study was to develop a serious injury risk prediction algorithm for pedestrians, using data from the South Australian Traffic Accident Reporting System. Two algorithms were developed to estimate serious injury risk, using a logistic regression analysis of 6,868 vehicle-pedestrian crashes extracted from TARS data. In this study, an optimal model based on the best combination of risk factors according to the Akaike information criterion (AIC) was developed. Additionally, a secondary GPS model using only crash site characteristics that can be derived from GPS coordinates from the crash scene was also developed. The optimal model is based on site and environmental conditions that could be derived from GPS data (posted speed limit, distance from crash site, natural lighting conditions, road geometry, road horizontal alignment and road vertical alignment) as well as pedestrian age/gender, driver age/gender and vehicle model year. The second model only included features that could be derived from GPS data. The optimal model was reasonable in accuracy and gave an under-triage rate of 10% when the injury threshold was set to 15%, with a corresponding over-triage rate of around 60%. The GPS model, despite not being as accurate as the optimal model may be adequate in the absence of all the risk factors required for the optimal model, requiring an injury threshold of 20% to give an under-triage rate of 10%, with the corresponding over-triage rate being around 70%. Both models can potentially be used for serious injury risk prediction (SIRP) for pedestrians involved in a collision with a vehicle.

Road risk behaviors: Pedestrian experiences

Objective: The objective of this study was to identify pedestrian risk behaviors that result in traffic accidents and characterization of the accidents experienced by participants in this study to provide information for the generation of integral preventive strategies. Methods: The study was correlational, descriptive, and transversal and followed a quantitative approach divided into 2 stages. In the first stage, an observational study was performed to identify the manifested risk behaviors of pedestrians, which served as a basis for the construction and design of a questionnaire. In the second stage, the questionnaire was applied to a group of 1,536 participants. Pearson's correlation coefficient was applied to establish associations between gender and age with respect to risk behaviors. Results: The 3 behaviors that participants reported performing always or very often include using electronic gadgets (except mobile phones), not using a pedestrian crosswalk, and using a mobile phone. In addition, 18.5% were involved in at least one road accident as a pedestrian in the last 5 years. Of the total number of registered accidents, 21% resulted in pedestrian injuries, 48.3% of which were serious. These were due to external causes not related to human factors or unidentified (58.5%) and behavior factors as a whole (41.4%, 31.5% of which were caused by pedestrians). Pearson's correlation coefficient showed evidence of a correlation between age and risk behaviors. Conclusions: All subjects in this study performed several risky actions as pedestrians, at least occasionally, and at least one fifth had been involved in a road accident as a pedestrian. It is known that the surroundings can positively or negatively influence individuals' behaviors; therefore, once prevention measures are identified, it is possible to influence risk behaviors. Therefore, road safety education and the physical environment must be considered together, and efforts focused on optimum infrastructure also need to consider road safety education.

Risk indicators for road accident in Poland for the period 2004-2017

OBJECTIVE

The aim of the research was to assess the prevalence, effects and risk factors for motor vehicle accidents (MVA) in the years 2004-2017 in Poland.

METHOD

We merged secondary data from the Prevention and Analysis Office of Road Traffic Headquarters of Police and Central Statistical Office of Poland.

RESULTS

Our results suggest that several thousand people are killed on Polish roads annually, and tens of thousands are injured. Road crashes represent the leading cause of death for Polish men up to 44 years of age. The most common causes of road crashes in Poland include failure to comply with the road traffic rules and low driving skills. We also found drivers who poorly assessed road situations, roads characterized by a lack of adequate road infrastructure, and many vehicles in poor condition. Road crashes have become a significant public health and social problem globally. Drivers caused most MVA in Poland in the years 2004-2017, whereas the underlying cause was inadequate speed regarding the road traffic condition as well as not respecting the right-of-way. Despite various measures that are being taken to improve safety on Polish roads, the number of the dead and wounded as a result of road accidents is still high and the losses to the society are considerable as well.

CONCLUSION

It is necessary to continue multidirectional actions to improve safety on the roads in Poland resulting in a systematic increase in the level of road traffic security.

Investigating safety of vulnerable road users in selected EU countries

PROBLEM

Vulnerable road users comprise over half of all road accident victims in the EU and their safety situation is not improving as fast as for motorists. The paper examines factors affecting fatality risk of pedestrians, cyclists, motorcyclists, and moped riders in seven EU countries using data from CARE database.

METHOD

Comparing accident severity indicators between countries is problematic because of data quality issues, different degree of underreporting, and different exposure levels. To avoid bias arising from these issues, fatality risk is modeled with binary logistic regression. Risk factors considered include accident location by area type, junction type, and traffic control, as well as lighting condition. Results are presented as odds ratios of fatal accident outcome in different countries under specific circumstances compared to reference conditions. It is shown that the error in OR values due to underreporting is small.

RESULTS AND DISCUSSION

Wide confidence intervals of the odds ratios in some countries confirm problems with accident data quality. Fatality risk is always higher for non-urban versus urban area and for darkness versus daylight conditions, but the odds ratios are different for different countries. Inconsistent results are obtained for accident location with respect to junction and its control type. Possible reasons for these differences are suggested and discussed. Practical applications: The proposed method avoids the data quality bias of accident severity indicators, thus, it can be used in international comparisons of vulnerable road user accidents. The article findings also support the concept of changes in legislation, such as reducing the speed limit in urban areas in Poland at night. Generally, the experience of countries with low VRU fatality risk identified in the article can be transferred to those with a higher risk.

A computational model of pedestrian road safety: The long way round is the safe way home

We propose a novel linear model of pedestrian safety in urban areas with respect to road traffic crashes that considers a single independent variable of pedestrian path safety. This variable is estimated for a given urban area by sampling pedestrian paths from the population of such paths in that area and in turn estimating the mean safety of these paths. We argue that this independent variable directly models the factors contributing to pedestrian safety. This contrasts previous approaches, which, by considering multiple independent variables describing the environment, traffic and pedestrians themselves, indirectly model these factors. Using data about 15 UK cities, we demonstrate that the proposed model accurately estimates numbers of pedestrian casualties.

Epidemiology of Pedestrian-Motor Vehicle Fatalities and Injuries, 2006-2015

INTRODUCTION

Pedestrian road safety remains a public health priority. The objective of this study is to describe trends in fatalities and injuries after pedestrian-motor vehicle collisions in the U.S. and identify associated risk factors for pedestrian fatalities.

METHODS

This is a cross-sectional study of U.S. pedestrian-motor vehicle collisions from 2006 to 2015 (performed in 2017). Pedestrian fatality and injury data were obtained from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System and National Automotive Sampling System General Estimates System. Frequencies of fatalities, injuries, and associated characteristics were calculated. Multivariable logistic regression was performed for risk of fatality, controlling for demographic and crash-related factors.

RESULTS

There were 47,789 pedestrian fatalities and 674,414 injuries during the 10-year study period. Fatality rates were highest among the elderly aged 85 years and older (2.95/100,000 population), whereas injury rates were highest for those aged 15-19 years (35.23/100,000 population). Predictors associated with increased risk for death include the following: male sex (AOR=1.36, 95% CI=1.15, 1.62), age ≥65 years (AOR=3.44, 95% CI=2.62, 4.50), alcohol involvement (AOR=2.63, 95% CI=1.88, 3.67), collisions after midnight (AOR=5.21, 95% CI=3.20, 8.49), at non-intersections (AOR=2.76, 95% CI=2.21, 3.45), and involving trucks (AOR=2.15, 95% CI=1.16, 3.97) and buses (AOR=5.82, 95% CI=3.67, 9.21).

CONCLUSIONS

Potentially modifiable factors are associated with increased risk of death after pedestrian-motor vehicle collisions. Interventions including elder-friendly intersections and increasing visibility of pedestrians may aid in decreasing pedestrian injuries and deaths.

Epidemiology and Outcome Determinants of Pedestrian Injuries in a Level I Trauma Center in Southern Iran; A Cross-Sectional Study

Objective

To epidemiologically assess the accidents and incidents in the injured pedestrians referred to Shahid Rajaee Hospital, Shiraz in order to provide basic preventive strategies and reduce injuries and fatalities caused by traffic accidents in pedestrians.

Methods

This cross-sectional study was conducted on 5840 injured pedestrians referred to Shahid Rajaee Hospital, Shiraz from 2009 to 2014. The baseline characteristic including the demographic and clinical information, the mechanism of injury, injury severity score (ISS) and outcome determinants. We also recorded the outcome measures and the mortality. Multivariate logistic regression analysis was performed to investigate the factors related to mortality rate and Length of Stay (LOS) in hospital.

Results

In our study, the history of 5840 injured pedestrians was analyzed. The mean age of the patients was 41.32 ± 19.21 years. Multivariate logistic regression indicated that mortality increased with age. Moreover, the odds of mortality was more in patients with Injury Severity Score (ISS) between 16 and 24 [OR: 12.94, 95% CI (3.78-32.66), p=0.001] and injuries in the head and neck [OR: 7.92, 95% CI (4.18-14.99), p=0.020]. LOS in hospital was also higher in patients with ISS > 25 [OR: 16.65, 95%CI (10.68-25.96), p=0.001].

Conclusion

Pedestrians have always been one of the most vulnerable road users. Our study indicated that the adverse consequences and mortalities in pedestrians increased with age. Hence, approaches are required to improve primary prevention programs and reduce deaths and injuries due to this major public health problem.

The effect of ambient light condition on road traffic collisions involving pedestrians on pedestrian crossings

Previous research suggests darkness increases the risk of a collision involving a pedestrian and the severity of any injury suffered. Pedestrian crossings are intended to make it safer to cross the road, but it is not clear whether they are effective at doing this after-dark, compared with during daylight. Biannual clock changes resulting from transitions to and from daylight saving time were used to compare RTCs in the UK during daylight and darkness but at the same time of day, thus controlling for potential influences on RTC numbers not related to the ambient light condition. Odds ratios and regression discontinuity analysis suggested there was a significantly greater risk of a pedestrian RTC at a crossing after-dark than during daylight. Results also suggested the risk of an RTC after-dark was greater at a pedestrian crossing than at a location at least 50m away from a crossing. Whilst these results show the increased danger to pedestrians using a designated crossing after-dark, this increased risk is not due to a lack of lighting at these locations as 98% of RTCs at pedestrian crossings after-dark were lit by road lighting. This raises questions about the adequacy and effectiveness of the lighting used at pedestrian crossings.

Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis

The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models.