Pedestrian-vehicle Crashes Research Articles

Integrating complex systems science into road safety research and practice, Part 2: applying systems tools to the problem of increasing pedestrian death rates

ObjectivesTo provide a specific example of how systems dynamics tools can increase understanding of stakeholder ‘mental models’ and generate robust systems-based hypotheses about the escalating problem of rising pedestrian death...

Elderly pedestrians’ self-regulation failures and crash involvement: The development of typologies

The present study aims to identify, study, and develop typologies based on cases of elderly pedestrian collisions with vehicles where the pedestrians subjectively ascribe the collision at least in part to their own self-regulation failures. Semistructured interview surveys were conducted with 18 elderly people who had experienced a crash with a vehicle as a pedestrian aged 65 years or older. Personal construct theory is adopted as the theoretical underpinning, and it is assumed that pedestrians have their own subjective ways of making sense of the crashes they are involved in. It was found that 11 of the 18 participants ascribed the crashes at least in part to their own self-regulation failures. Cognitive maps of the 11 participants had a common structure, and the associated 11 incidents were classified with respect to the following dimensions: (a) self-regulation type, (b) self-regulation motivation, (c) cause of self-regulation failure, and (d) characteristics of the collisions that occurred after the self-regulation failure. Based on these findings, practical implications are found, and corresponding interventions that may reduce elderly pedestrian–vehicle crashes of this type are discussed. Specifically, this study demonstrates the necessity of education or other intervention that goes beyond informing elderly pedestrians of what is right and wrong in traffic environments. Another critical result—the need to motivate elderly pedestrians to respect and adhere to their own highly personal self-regulation, even if it is not against the social norms—is also presented and discussed.

Flashing yellow arrows for right turn applications: A driving simulator study and static evaluation analysis

Pedestrians continue to be considered among the most vulnerable road users, as they are directly exposed to the impact of vehicular crashes. Due to the variety of turning movements that can be, and are, performed at signalized intersections, the vulnerability of pedestrians becomes more apparent upon entering a crosswalk at an intersection. At signalized intersections, it is common for pedestrians to cross an intersection parallel to the vehicular traffic who are provided with a circular green, or right-of-way, indication. A consequence of this approach is the conflict that arises between turning vehicles and crossing pedestrians. As a potential countermeasure, this study analyzed the effectiveness of flashing yellow arrows for right turn applications at modifying driver behaviour (visual attention, intersection approach speed, and direct perspective) through a two-step evaluation, including a large-scale static evaluation and direct driving simulator study. Over 200 respondents completed the large-scale static evaluation and 24 participants responded to 144 simulated right turn scenarios with various pedestrian presence, half of which had a flashing yellow arrow display. This research revealed that drivers have a strong comprehension of the meaning of the flashing yellow arrow and did not behave unsafely when presented with the indication during the simulator portion of the study. This study contributes in the area of pedestrian safety through the evaluation of the applicability of flashing yellow arrows for right turn applications as a countermeasure to mitigate vehicle-pedestrian crashes at intersections with parallel pedestrian crossings.

Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina

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

Development, testing, and evaluation of road safety poster to reduce jaywalking behavior at intersections

Jaywalking is a traffic violation that contributes significantly to vehicle–pedestrian crashes at intersections. Although public education campaigns have been widely used in road safety, relatively little research has been conducted to understand the underlying process or mechanisms that influence their effectiveness for pedestrian safety, especially jaywalking behavior. This study aims to develop, test, and evaluate on-road pedestrian safety posters that are based on proven behavioral change approaches with demonstrated evidence to reduce jaywalking at intersections. A pedestrian safety poster targeting jaywalking was developed using several theoretical constructs from well-established behavioral change models and a questionnaire survey was administered to pedestrians to gauge their perceptions of the poster. Results showed that the communication had a significant positive effect in changing respondents’ adaptive intentions. The poster was subsequently installed at two signalized intersections in Melbourne, Australia, and the before (without) and after (with) proportions of jaywalkers were observed. Our results showed a significant decrease in the proportion of jaywalkers after the installation of the poster. This study showed that properly designed on-site road safety communication could change pedestrian behavior. It further highlighted the importance of using a well-established conceptual framework in the development and testing of road safety messages.

Estimated benefit of automated emergency braking systems for vehicle–pedestrian crashes in the United States

Objective: The objective of this research study is to estimate the benefit to pedestrians if all U.S. cars, light trucks, and vans were equipped with an automated braking system that had pedestrian detection capabilities.Methods: A theoretical automatic emergency braking (AEB) model was applied to real-world vehicle–pedestrian collisions from the Pedestrian Crash Data Study (PCDS). A series of potential AEB systems were modeled across the spectrum of expected system designs. Both road surface conditions and pedestrian visibility were accounted for in the model. The impact speeds of a vehicle without AEB were compared to the estimated impact speeds of vehicles with a modeled pedestrian detecting AEB system. These impacts speeds were used in conjunction with an injury and fatality model to determine risk of Maximum Abbreviated Injury Scale of 3 or higher (MAIS 3+) injury and fatality.Results: AEB systems with pedestrian detection capability, across the spectrum of expected design parameters, reduced fatality risk when compared to human drivers. The most beneficial system (time-to-collision [TTC] = 1.5 s, latency = 0 s) decreased fatality risk in the target population between 84 and 87% and injury risk (MAIS score 3+) between 83 and 87%.Conclusions: Though not all crashes could be avoided, AEB significantly mitigated risk to pedestrians. The longer the TTC of braking and the shorter the latency value, the higher benefits showed by the AEB system. All AEB models used in this study were estimated to reduce fatalities and injuries and were more effective when combined with driver braking.

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

BackgroundPedestrians 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. MethodsFour 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. ResultsFifty-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. ConclusionsThe 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.

Evaluation of Injury Severity for Pedestrian–Vehicle Crashes in Jordan Using Extracted Rules

AbstractPedestrian safety is a major concern throughout the world because pedestrians are considered to be the most vulnerable roadway users. This paper sought to identify the main factors in pedes...

Pedestrian Injury Severities in Pedestrian-Vehicle Crashes and the Partial Proportional Odds Logit Model: Accounting for Age Difference

This study investigates factors that significantly contribute to the severity of pedestrian injuries resulting from pedestrian-vehicle crashes. Multinomial logit (MNL) models, mixed logit (ML) models, and ordered logit/probit models have been widely used in modeling crash injury severity, including pedestrian injury severity in pedestrian-vehicle crashes. However, both MNL and ML models treat injury severity levels as non-ordered, ignoring the inherent hierarchical nature of crash injury severities, and the data used in ordered logit models need to be strictly subjected to the proportional odds (PO) assumption. In this study, a partial proportional odds (PPO) logit model approach is employed to explore the issues of pedestrian safety associated with each age group: young (aged under 24), middle-aged (aged 25–55), and older pedestrians (aged over 55). Data used in this study are police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, and roadway characteristics are inspected. Results from likelihood ratio tests statistically show the better performance of developing separate injury severity models for each age group compared with estimating a single model utilizing all data. Relevant parameter estimates and associated marginal effects are used to interpret the results, followed by recommendations made in the concluding section.

Modeling Pedestrian Injury Severity in Pedestrian-Vehicle Crashes in Rural and Urban Areas: Mixed Logit Model Approach

Pedestrian-vehicle crashes are more likely to result in severe pedestrian incapacitating injuries and fatalities than other types of crashes. In this study, mixed logit models are developed to investigate and identify significant contributing factors to the pedestrian injury severity in pedestrian-vehicle crashes in both rural and urban areas in North Carolina, United States. Pedestrian-vehicle crash data from the Highway Safety Information System database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: fatality; injury class 1 (disabling injury); injury class 2 (evident injury); injury class 3 (possible injury); and no injury (property damage only). The estimation results show that factors such as a bad driver’s physical condition, heavy trucks, dark light condition, speed limit between 35 and 50 mph and speed limit above 50 mph will significantly increase pedestrian injury severities in both rural and urban areas. The developed model and analysis results provide insights on developing effective countermeasures to reduce pedestrian injury severities in pedestrian-vehicle crashes and improve traffic system safety performance.

Left-turning vehicle-pedestrian conflicts at signalized intersections with traffic lights: Benefit or harm? A two-stage study

PurposeVehicle-pedestrian conflicts are common at road intersections when traffic lights change. However, the impact of traffic light on transportation safety and efficiency remains poorly understood. MethodsA two-stage study was used to survey the proportion of intersections with conflicting traffic lights and the related transportation efficiency and safety were evaluated as well. First, a cross-sectional study estimated the proportion of signalized intersections with conflicting left-turning vehicle-pedestrian traffic lights in Changsha city, China. Second, a natural experiment compared transportation efficiency and safety between intersections with and without conflicting left-turning vehicle-pedestrian traffic lights. Risky conflicts, where motor vehicles violated laws and failed to yield to pedestrians in crosswalk were used as a surrogate for transportation safety. The number of motor vehicles and pedestrians passing through the intersections per second and per meter were used to estimate transportation efficiency. Data were collected and analyzed in 2015 (from March to December). A search of online news from domestic media sources was also conducted to collect pedestrian injury data occurring at the intersections. ResultsAbout one-fourth (57/216) intersections had conflicting left-turning traffic lights (95% CI: 20.5%, 32.3%). Risky vehicle-pedestrian conflicts were more frequently observed at intersections with conflicting lights compared to those without (incidence rate ratio (IRR) = 3.13; pedestrians: IRR = 4.02), after adjusting for type of day (weekday vs. weekend), the time period of observation, and motor vehicles traffic flow. Intersections without conflicting vehicle-pedestrian traffic lights had similar transportation efficiency to those with conflicting lights after controlling for covariates (p > 0.05). The systematic review of news media reports yielded 10 left-turning vehicle-pedestrian crash events between 2011 and 2017, involving 11 moderate or severe pedestrian injuries and 3 fatal pedestrian injuries. ConclusionOver one-fourth of road intersections in Changsha city, China have conflicting left-turning traffic lights. Conflicting traffic lights cannot improve transportation efficiency, but increase risky conflicts between vehicles and pedestrians.

School-Aged Pedestrian–Vehicle Crash Vulnerability

The analysis of pedestrian–vehicle crashes makes a significant contribution to sustainable pedestrian safety. Existing research is based mainly on the statistical analysis of traffic crashes involving pedestrians and their causes, without the identification of areas vulnerable to traffic crashes that involve pedestrians. The main aim of this paper is to identify areas vulnerable to school-aged pedestrian–vehicle crashes at a local level to support the local authorities in implementing new urban traffic safety measures. The vulnerable areas were determined by computing the severity index (SI) based on the number of fatal, serious, and slight casualties throughout the 2011–2016 period in a large urban agglomeration (Bucharest). As well as the vulnerable areas, the triggering factors and the time intervals related to school-aged pedestrian–vehicle crashes were identified. The outcomes of the study showed that the vulnerable areas were concentrated only in districts 2 and 4 of Bucharest, and they were associated with high vehicle speed and pedestrians’ unsafe crossing behavior. The findings revealed that speed and age are triggering factors in generating school-aged pedestrian–vehicle crashes. The identified time peaks with a high number of traffic crashes correspond to the afternoon time intervals, when scholars go home from school. The identification of the areas vulnerable to school-aged pedestrian crashes may help local authorities in identifying and implementing measures to improve traffic safety in large urban agglomerations.

Pedestrian-vehicle crashes: Risk perception and responsibility attribution among children, adolescents and adults.

:Background:Child pedestrian injuries in China result from crashes not just with cars. We considered how Chinese youth and young adults perceive pedestrian risk from four vehicle types-bicycles, electric bicycles, cars, buses—evaluating perceptions for two factors that may influence pedestrian behavior and risk-taking, perception of road environment risk and responsibility to avoid crashes. Understanding how pedestrians perceive risk, and how these perceptions change as children grow older, could guide prevention efforts.Methods:383 children (grades 3-4, 5-6, 8) and university students completed self-report surveys. We analyzed overall responses, plus age/gender differences in risk perception and responsibility attribution, across vehicle types and number of vehicles approaching, using multivariate analysis of variance (MANOVA) and generalized estimating equation (GEE) models.Results:Overall, larger vehicles were perceived as riskier (p less than .001). Compared tochildren, university students perceived bicycles and electric bicycles as less risky (Mean=2.66 vs. 3.69, 3.34 vs. 3.62, respectively, p less than .05). Cars and buses were perceived as equally risky across age groups. Across all vehicle types and number of vehicles traversing the road, both children and young adults perceived more pedestrian responsibility to avoid collisions relative to drivers (p less than .001). Children attributed less personal responsibility to avoid pedestrian-vehicle crashes than university students (e.g., buses odds ratio (OR)=0.20, p less than .001; OR=0.26, p less than .01; OR=0.28, p less than .01 for third/fourth, fifth/sixth, eighth graders, respectively). University students and fifth/sixth graders also identified greater pedestrian responsibility to avoid collisions with multiple vs. one vehicle approaching (e.g., university students/cars OR=4.17, p less than .001).Conclusions:We discuss cognitive and perceptual development factors in childhood, adolescence, and young adulthood that may contribute to differences in risk perception and responsibility attribution among Chinese pedestrians and suggest future research should explore those processes and subsequently develop evidence-based interventions to reduce pedestrian injury risk.

Analyzing driving environment factors in pedestrian crashes injury levels in Jakarta and the surrounding cities

Pedestrian-vehicle crashes are the results of a combination of influencing factors including the driving environment. This paper looks into the driving environment factors in pedestrian crashes injury levels on road links in Jakarta and the surounding cities which contribute to the city traffic generation. The vehicle-pedestrian accident data used were obtained from the 2016 Indonesian national police accident database covering 4,646 pedestrian accidents on road links from Jakarta, Depok, Tangerang Selatan, Tangerang and Bekasi, Indonesia. Various factors were analyzed including crash level severity, month of occurrence, weather condition, lighting condition, road function, road class, road type, road surface condition and road status. As injury levels were categorized into slight injury, severe injury and fatal injury and it was assumed that the dependent variables which were crash injury levels could not be perfectly predicted from the independent variables, Multinomial logistic regression (MNL) was used in the analysis to predict the probability of different categories of dependent variables. It was found that the relative risks of pedestrian accident risks factors changed with different categories both in terms of fatal and severe injuries. One of the findings shows that the risk of having severe injuries would decrease by 40.2% on national roads, by 70.5% on provincial roads and by 53.5% on urban roads. The results can be expected to be referred to in the improvement of pedestrian safety level and in the development of related measures.

Factors affecting the severity of pedestrian traffic crashes

Background: Considering the importance of pedestrian traffic crashes and the role of environmental and demographic factors in the severity of these crashes, this article aimed to review the published evidence and synthesize the results of related studies to determine any associations between demographic and environmental factors and the severity of pedestrian-vehicle crashes. Methods: All epidemiological studies published from 1970 to 2019 were searched in international electronic databases (PubMed [Medline], Scopus, Web of Science, Embase, ScienceDirect, and Ovid) and reference lists of the identified articles were also searched. Studies were included if they investigated the severity of pedestrian-vehicle crashes as outcome, measured any environmental and demographic factors for pedestrian-vehicular crashes as exposure, designed observational, and if they were written in all languages. Quality of included studies was evaluated using the strengthening the reporting of observational studies in epidemiology checklist for observational studies. Results: We found 3126 references among which 24 studies were included in this review. All retrieved studies were conducted between 1990 and 2019 and had a cross-sectional design. In most of these studies, the associations between environmental and demographic variables such as vehicle speed or speed limits, pedestrian age, lighting, type of road, type of vehicle, and alcohol intake with the severity of pedestrian traffic crashes were examined. Conclusion: This study showed that few studies were conducted in this area; in fact, most of the studies were carried out in metropolises of developed countries. As a result, studies which provide strong causal inferences by focusing on high-risk groups and a higher level of evidence such as cohort and case-control ones are needed in developing countries.

Neighborhood Environmental Characteristics Affecting Pedestrian-Vehicle Crashes in School Zones

Neighborhood Environmental Characteristics Affecting Pedestrian-Vehicle Crashes in School Zones

Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China

The improvement of pedestrian safety plays a crucial role in developing a safe and friendly walking environments, which can contribute to urban sustainability. A preliminary step in improving pedestrian safety is to identify hazardous road locations for pedestrians. This study proposes a framework for the identification of vehicle-pedestrian collision hot spots by integrating the information about both the likelihood of the occurrence of vehicle-pedestrian collisions and the potential for the reduction in vehicle-pedestrian crashes. First, a vehicle-pedestrian collision density surface was produced via network kernel density estimation. By assigning a threshold value, possible vehicle-pedestrian hot spots were identified. To obtain the potential for vehicle-pedestrian collision reduction, random forests was employed to model the density with a set of variables describing vehicle and pedestrian flows. The potential for crash reduction was then measured as the difference between the observed vehicle-pedestrian crash density and the prediction produced by the random forests models. The final hotspots were determined by excluding those with a crash reduction value of no more than zero. The method was applied to the identification of hazardous road locations for pedestrians in a district in Shanghai, China. The result indicates that the method is useful for decision-making support.

Mathematical Model for Velocity Calculation of Three Types of Vehicles in the Case of Pedestrian Crash

Abstract This paper treats influencing factors in the determination of vehicles speed on the pedestrian crash moment according to pedestrian throw distance and formulates a mathematical model for vehicle speed determination. Vehicle speed is one of the highest causes of accidents. The mathematical model formulation (as the target of this paper) for velocity calculation, in the case of pedestrian accidents, presents great help and guidance to experts of this field when dealing with accident analysis that through accurate determination of this parameter to find other circumstances as close as possible to the technical process of pedestrian accidents. The target of this paper is to define a mathematical model formulation for vehicle velocity calculation in pedestrian crash moment depending on relevant parameters. For the purpose of model formulating, we have selected three cases of real accidents that involved vehicles (“Peugeot 307”, “VW Golf ” and “Mercedes E 220”) with different geometrical parameters of the front profile and pedestrians with different heights and weights. For regression analysis we used “R” and “SPSS” software, which enables the statistical analysis of the data and mathematical model formulation. Also, for analysis of impact of relevant factors, model formulation and model testing have used “Virtual Crash” and “PC Crash” software, which enables pedestrian-vehicle crash simulation using vehicles with real technical characteristics and various pedestrian characteristics. Inductive, comparative, and deductive methods are part of the research methods in this paper.

Income and Racial Disparity and the Role of the Built Environment in Pedestrian Injuries

This study examined the impacts of built environments on pedestrian-vehicle crashes with different levels of severity and the income and racial disparities related to different crash injuries in Austin, Texas. Arterial roads and commercial areas were associated with more injurious crashes. The presence of schools had a positive association with both injurious and no-injury crashes but only in areas with high percentages of nonwhites and poverty. This implies the importance of planning that separates pedestrians from vehicles in mixed-traffic commercial environments. Schools in areas with high percentages of minorities and poverty should be given priority for improvements of pedestrian safety.