Pedestrian Crash Severity Research Articles

Unconstrained and partially constrained temporal modelling of pedestrian injury severities

This study aims to explore the phenomenon of pedestrian crash severity by investigating how pedestrian injury levels have evolved in incidents occurring prior to (2019), during (2020), and after (2021) the COVID-19 lockdowns. Using Louisiana crash data, distinct annual models for pedestrian injury severity (categorised as severe (fatal and severe), minor (moderate and minor), and no injury) were developed using a random parameters logit approach, accounting for potential heterogeneity in means and variances of random parameters. Likelihood ratio tests were employed to assess the overall stability of model estimates across the studied years, and a comparison was made between partially constrained and unconstrained temporal modelling approaches. The results reveal statistically significant differences in injury severity before, during, and after the COVID-19 pandemic.

A Comparative Study Using Generalized Ordered Probit, Stacking Ensemble, and TabNet: Application to Determinants of Pedestrian Crash Severity

A Comparative Study Using Generalized Ordered Probit, Stacking Ensemble, and TabNet: Application to Determinants of Pedestrian Crash Severity

Predicting pedestrian-involved crash severity using inception-v3 deep learning model

This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016–2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into three categories: fatal, injury, and no injury. The Boruta algorithm was applied to determine the importance of variables and investigate contributing factors to pedestrian crash severity, revealing several associated aspects, including pedestrian gender, pedestrian and driver impairment, posted speed limits, alcohol involvement, pedestrian age, visibility obstruction, roadway lighting conditions, and both pedestrian and driver conditions, including distraction and inattentiveness. To address data imbalance, the study employed Random Under Sampling (RUS) and the Synthetic Minority Oversampling Technique (SMOTE). The DeepInsight technique transformed numeric data into images. Subsequently, five crash severity prediction models were developed with Inception-v3, considering various scenarios, including original, under-sampled, over-sampled, a combination of under and over-sampled data, and the top twenty-five important variables. Results indicated that the model applying both over and under sampling outperforms models based on other data balancing techniques in terms of several performance metrics, including accuracy, sensitivity, precision, specificity, false negative ratio (FNR), false positive ratio (FPR), and F1-score. This model achieved prediction accuracies of 93.5%, 77.5%, and 85.9% for fatal, injury, and no injury categories, respectively. Additionally, comparative analysis based on several performance metrics and McNemar’s tests demonstrated that the predictive performance of the Inception-v3 deep learning model is statistically superior compared to traditional machine learning and statistical models. The insights from this research can be effectively harnessed by safety professionals, emergency service providers, traffic management centers, and vehicle manufacturers to enhance their safety measures and applications.

Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

In 2015, over 17% of pedestrians were killed during vehicle crashes in Hong Kong while it raised to 18% from 2017 to 2019 and expected to be 25% in the upcoming decade. In Hong Kong, buses and the metro are used for 89% of trips, and walking has traditionally been the primary way to use public transportation. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. Most studies on crash severity ignored the severity correlations between pedestrian-vehicle units engaged in the same impacts. The estimates of the factor effects will be skewed in models that do not consider these within-crash correlations. Pedestrians made up 17% of the 20,381 traffic fatalities in which 66% of the fatalities on the highways were pedestrians. The motivation of this study is to examine the elements that pedestrian injuries on highways and build on safety for these endangered users. A traditional statistical model's ability to handle misfits, missing or noisy data, and strict presumptions has been questioned. The reasons for pedestrian injuries are typically explained using these models. To overcome these constraints, this study used a sophisticated machine learning technique called a Bayesian neural network (BNN), which combines the benefits of neural networks and Bayesian theory. The best construction model out of several constructed models was finally selected. It was discovered that the BNN model outperformed other machine learning techniques like K-Nearest Neighbors, a conventional neural network (NN), and a random forest (RF) model in terms of performance and predictions. The study also discovered that the time and circumstances of the accident and meteorological features were critical and significantly enhanced model performance when incorporated as input. To minimize the number of pedestrian fatalities due to traffic accidents, this research anticipates employing machine learning (ML) techniques. Besides, this study sets the framework for applying machine learning techniques to reduce the number of pedestrian fatalities brought on by auto accidents.

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.

Using geographically weighted logistic regression (GWLR) for pedestrian crash severity modeling: Exploring spatially varying relationships with natural and built environment factors

Although a large number of studies have tried to explore the relationship between built environment and pedestrian crash severity in developed countries, there is a lack of similar studies in the context of developing countries. Methodologically, the contributory factors influencing pedestrian crash severity are commonly identified through global logistic regression (GLR) models. However, these models are unable to capture the spatial variation in the relationships between the dependent and independent variables. The local logistic regression model, such as geographically weighted logistic regression (GWLR), can potentially overcome this issue. The application of local logistic regression to model pedestrian crash severity is absent in the literature. Therefore, this study aimed to apply the GWLR technique to explore spatially heterogeneous relationships between natural and built environment-related factors and pedestrian crash severity in Dhaka, the capital city of a developing country: Bangladesh. First, using secondary pedestrian crash data, a GLR model was developed to identify significant contributory factors influencing pedestrian crash severity. Results of the model showed that the probability of fatal pedestrian crash occurrence increased at night, in unlit locations, and during adverse weather conditions. In addition, the likelihood of a fatal crash decreases when medians exist on roads and around institutional land use. Also, the chance of fatal crashes increased on straight and flat roads and at locations with more bus stops. Finally, this study explored spatial variation in the effect intensity of these significant variables across the study area using the GWLR technique. High intensity variation across the study area was found for road geometry and institutional land use factors. On the other hand, low intensity variation was found for light conditions and the presence of median factors. This technique can be applied in any area, and the results would help provide insights into the spatial dimension of traffic safety.

Predicting pedestrian crash occurrence and injury severity in Texas using tree-based machine learning models

ABSTRACT This study investigates the frequency and injury severity of pedestrian crashes across Texas using tree-based machine learning models. Ten years of police records are used along with roadway inventory and other sources to map 78,000 + pedestrian crashes over 700,000 road segments. Methods like random forests (RF), gradient boosting (LightGBM and XGBoost), and Bayesian additive regression trees (XBART) are applied and compared. The crash frequency models indicate that highway design variables have significant impacts on crash frequencies. Severity models show how higher speed limits significantly increase the likelihood of pedestrian fatalities and severe injuries, and how intoxication (of drivers or pedestrians) lead to more severe injuries. The 4 specifications perform similarly in predicting crash counts, with LightGBM having much faster computing times. Across the crash-severity models, XBART achieved greater precision values but with significantly higher computating times.

Developing Pedestrian Fatality Prediction Models Using Historical Crash Data: Application of Binary Logistic Regression and Boosted Tree Mechanism

Pedestrian fatality rate plays a key role in examining effectiveness of the road safety. The present study attempts to examine the effect of various categories of accused vehicles and the average 85th percentile speed at accident location on the pedestrian crash fatality. The study also attempts to develop pedestrian crash severity models using the binary logistic regression and boosted trees technique. Historical crash data, along with the video recording technique at accident sites, have been utilized for the present study. From regression equations, it is observed that when the heavy vehicle (HV) hits a pedestrian as compared to two-wheeler (2W), the average chance of death increases 2.44 times. According to the Boosted tree model, the contribution of speed is 60 %, whereas the contribution of category of accused vehicle is 40 % for pedestrian fatality prediction. The study should help in planning better strategies like all red time at intersections or pedestrian foot over bridge at critical locations.

Do factors associated with older pedestrian crash severity differ? A causal factor analysis based on exposure level of pedestrians

Objective Older pedestrians are more likely to have severe or fatal consequences when involved in traffic crashes. Identifying the factors contributing to the severity and possible interdependencies between factors in specific exposure areas is the first step to improving safety. Therefore, examining the causal factors’ impact on pedestrian-vehicle crash severity in a given area is vital for formulating effective measures to reduce the risk of pedestrian fatalities and injuries. Methods This study implements the Thiessen polygon algorithm deployed to define older pedestrians’ exposure influence area. Enabling trip characteristics and built environment information as exposure index settings for the background of the pedestrian severity causal analysis. Then, structural equation modeling (SEM) was applied to conduct a factor analysis of the crash severity in high- and low-exposure areas. The SEM evaluates latent factors such as driver risk attitude, risky driving behavior, lack of risk perception among older pedestrians, natural environment, adverse road conditions for driving or walking, and vehicle conditions. The SEM crash model also establishes the relationship between each latent factor. Results In total, drivers’ risky driving behavior (0.270, p < 0.05) in low-exposure areas significantly impacts older pedestrian crash severity more than in high-exposure areas. Lack of risk perception among older pedestrians (0.232, p < 0.05) is the most critical factor promoting crash severity in high-exposure areas. The natural environment (0.634, p < 0.05) in high-exposure areas positively influences older pedestrians’ lack of risk perception more than in low-exposure areas. Conclusions Significant group differences (p-values ∼ 0.001-0.049) existed between the causal factors of the high-exposure risk areas and the low-exposure risk factors. Different exposure intervals require detailed scenarios based on the critical risks identified. The crash severity promotion measures in different exposure areas can be focused on according to the critical causes analyzed. Those clues, in turn, can be used by transportation authorities in prioritizing their plans, policies, and programs toward improving the safety and mobility of older pedestrians.

Factors affecting injury severity at pedestrian crossing locations with Rectangular RAPID Flashing Beacons (RRFB) using XGBoost and random parameters discrete outcome models

This paper evaluates the effectiveness of Rectangular Rapid Flashing Beacons (RRFB) on crash severity. The study used and compared XGBoost and Random Parameters Discrete Outcome Models (RPDOM) respectively. The dataset comprises of 312 pedestrian crossing locations, among which 154 treatment locations were provided with the Rectangular Rapid Flashing Beacons (RRFB) and 158 control locations without RRFB. These control locations have similar roadway, traffic, and land use characteristics of that of the treatment locations but are not treated with RRFB or other pedestrian crossing countermeasures. This study shows the impact of RRFB and other factors on severity of nighttime, pedestrian, total and rear-end crashes. Crash severity data was compiled from driver, vehicle, and event level data of each crash. Due to availability of larger number of observations for total (35,553), rear-end (15,675) and nighttime crashes (8,144) XGBoost was used, and due to less observations for pedestrian crashes (369), it was modeled using RPDOM. The results showed positive impact of RRFB for the reduction of nighttime crashes. It was noted that RRFB reduces the K and A nighttime crashes according to the SHAP values from the XGBoost model but does not have the desired significance for rear end and overall total crashes in the study area. From the RPDOM, it was seen that RRFB showed statistically significant reduction in injury severity of pedestrian crashes and nighttime crashes. To compare the two models, nighttime crashes were modeled using both the techniques, the prediction accuracy of XGBoost Model was 97% which was much greater than that of the RPDOM at 73.8% prediction accuracy. Thus, both XGBoost and the RPDOM model for showed positive impact of installing RRFB in reducing the severity of nighttime crashes.

Severity of Pedestrian Crashes in Developing Countries: Analysis and Comparisons Using Decision Tree Techniques

Severity of Pedestrian Crashes in Developing Countries: Analysis and Comparisons Using Decision Tree Techniques

Partial proportional odds model for analyzing pedestrian crashes, threshold heterogeneity by scale and proportional odds factor

Despite low traffic in Wyoming, pedestrian crash severity accounts for a high number of fatalities in the state. Thus this study was conducted to highlights factors contributing to those crashes. The results highlighted that drivers under influence, type of vehicle, location of crashes, estimated speed of vehicles, driving over the recommended speed are some of factors contributing to the severity of crashes. In this study, we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values. However, it has been argued that this assumption might be unrealistic, especially at the presence of extreme values. Thus, the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes, or proportional odds effects. In addition, we accounted for the spread rate, or scale, of the model's latent distribution of pedestrian crashes. The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model. Comparisons were also made across standard normal, simple partial ordinal model, and partial ordinal accounting for scale heterogeneity. In addition, various potential threshold structures such as symmetric and flexible were considered, but similar goodness of fits were observed across all those models. Extensive discussion has been made regarding the formulation of the implemented methodology, and its implications. • More flexibility is given to the model by relaxing the assumption of proportional odds and scale, or spread rate. • The results highlight a significant improvement in the model fit, compared with the standard model. • Some of factors contributing to the severity of pedestrian crashes are discussed. • Almost all identified results linked to the lack of attention and caution of the drivers.

Factors affecting severe pedestrian crash percentages at intersections in Colorado 2006–2018

This article investigates factors associated with fatal and severe injury pedestrian crash percentages at intersections in Colorado. Many published studies associate road and traveler characteristics with the frequency or severity of pedestrian crashes without reference to specific locations. The objective of this study is to determine whether road and traveler characteristics, aggregated by intersection, partly explain differences in severe crash percentages at intersections. From 2006 to 2018, there were a total of 17,047 reported crashes involving pedestrians and motor vehicles in all of Colorado. This study analyzes 3,015 of these crashes that had the GPS coordinates needed to identify their locations at intersections and included the information needed to identify the pedestrian outcomes of the crash. The results of logistic and linear regressions found that lighting condition, vehicle speed, turning movement of vehicle, vehicle type, pedestrian age, and driver or pedestrian impairment by drugs or alcohol were most associated with severe crash percentages at intersections. These findings identify crash characteristics at intersections with higher severe crash proportions that can potentially be addressed to improve safety.

Pedestrian safety at urban intersections: lighting conditions is the question

Sustainable and livable cities and non-motorized transportation modes are being increasingly promoted around the world. A considerable number of studies have investigated various factors associated with the severity of pedestrian crashes. However, little attention has been paid to the particular impacts of lighting conditions. This paper aims to differentiate between different lighting conditions based on solar altitude by developing separate Generalized Ordered Probit (GOP) models for the four distinct lighting conditions (i.e. daylight, twilight, dark, and lighted) analysing pedestrian injury severity at urban intersections. The models are tested using a set of log-likelihood ratio tests, which show there are key differences in the significance of the estimates, magnitude, and even the direction of the effects of explanatory variables, in which such differences may not be revealed by calibrating one aggregate model. Notably, built environments, age, gender of both pedestrian and driver, and speed limits were found to have different impacts on the severity of the crash.

Application of Emerging Data Sources for Pedestrian Safety Analysis in Charlotte, NC

Pedestrian safety is a growing concern for transportation planners and safety engineers at both local and state levels. Continued advancements in data availability, data integration abilities, and analysis methodologies offer new opportunities to identify factors influencing pedestrian safety and to quantify their effects to inform data-driven road safety management. The main objective of this study was to spatially integrate Highway Safety Information System data with multijurisdictional and emerging datasets to analyze two measures of pedestrian safety performance in Charlotte, NC: (1) the severity of a pedestrian crash that has occurred, and (2) the probability that a pedestrian crash will occur on a given roadway segment. To accomplish the objectives, the study explored several high-priority research topics in safety data and analysis, including pedestrian exposure analysis and probe data integration. The research team developed a pedestrian count model to predict pedestrian volumes at locations without pedestrian counts and integrated speed information from probe data to supplement other roadway and contextual transportation data available from several agencies. Pedestrian exposure at a given intersection was found to be significantly influenced by demographic and socioeconomic characteristics, employment, land use, sidewalk presence, transit access, and roadway and intersection characteristics. The project team identified numerous significant factors that influenced pedestrian crash severity and probability, including outputs from the pedestrian exposure model, observed vehicle speeds, traffic volumes, intersection proximity, and other crash-related factors. The results could be used to identify locations that are more susceptible to pedestrian safety issues.

How effective are pedestrian crash prevention systems in improving pedestrian safety? Harnessing large-scale experimental data

Over the past few years, the number of fatalities and severe injuries of vulnerable road users, particularly pedestrians, has risen substantially. Clearly, the safe mobility of pedestrians is critical in our transportation system. Technology can help reduce vehicle–pedestrian crashes, fatalities, and injuries. Emerging technologies such as pedestrian crash prevention (PCP) systems utilized in on-road vehicles have the potential to mitigate pedestrian crash severity or prevent crashes. However, the reliability and effectiveness of these technologies have remained uncertain. This study contributes toward understanding the effectiveness of PCP systems utilized in on-road vehicles with a low level of automation by investigating two crossing and one longitudinal scenarios. The Insurance Institute for Highway Safety field test data from 2018 to 2021 is harnessed, where several on-road vehicles and their PCP systems are evaluated in terms of safety. The large-scale experimental dataset is comprised of 3095 tests of 91 vehicles with different sizes, makes, and models. The empirical results indicate that in hazardous pedestrian-vehicle conflict situations, the performance of PCP systems has been improved during recent years. The test data shows that some pedestrians were undetected in some tests, but on average, in 70% of the tests, the PCP systems avoided pedestrian crashes. However, for the occurred crashes, PCP systems, on average, were able to mitigate impact speeds of >50%. In real-life situations, this could translate to substantial reductions in injury and fatality risk. Through rigorous analysis, the associations of key factors in the studied scenarios and the performance of PCP systems are explored and discussed in this paper. The modeling results show that increasing the maximum deceleration rate of the PCP system and lower weight of vehicles can significantly improve the performance of the PCP system by decreasing the speed at impact with pedestrians. The average maximum deceleration utilized in PCP systems has been increased over time from 7.48 m/s2 in 2018 to 9.36 m/s2 in 2021. This can be one of the reasons behind the improvement of PCP systems during recent years.

Unveiling the relevance of traffic enforcement cameras on the severity of vehicle–pedestrian collisions in an urban environment with machine learning models

Purpose: One of the leading causes of violent fatalities around the world is road traffic collisions, and pedestrians are among the most vulnerable road users with respect to such incidents. Since walking is highly promoted in urban areas to alleviate motor-vehicle externalities, it is paramount to understand the causes associated with vehicle–pedestrian collisions and their severity to provide safe environments. Although traffic enforcement cameras can address vehicle-vehicle collisions, little is known about their effectiveness with respect to vehicle–pedestrian incidents. Methodology: In this study, we trained a set of machine learning models to forecast if a vehicle–pedestrian collision will turn into an injury or fatality, and the most suitable model was used to investigate the contributing features associated with such events with emphasis on the impact of traffic enforcement cameras. In addition to traffic enforcement camera proximity, features associated with the collision, weather, vehicle, victim, and infrastructure are included in the model to reduce unobserved heterogeneity. Results: Results show that a Linear Discriminant Analysis model surpasses other machine learning models considering the evaluation metrics. Results reveal that the age and gender of the victim, the involvement of larger vehicles in the collision, and the quality of the illumination are the causes associated with pedestrian fatalities. On the other hand, involvement of motorcycles and collisions that occurred in densely populated locations are the causes associated with pedestrian injuries. Conclusions: This investigation demonstrates how to articulate machine learning into a vehicle–pedestrian crash analysis to understand the direction and magnitude of covariates in the corresponding severity outcome. Furthermore, it highlights the remarkable effect that traffic enforcement cameras and other features have on vehicle–pedestrian crash severity. These results provide actionable guidance for educational campaigns, enhanced traffic engineering, and infrastructure improvements that could be implemented in the analyzed region to provide safer transportation.

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.

Semi and Nonparametric Conditional Probability Density, a Case Study of Pedestrian Crashes

Background: Kernel-based methods have gained popularity as employed model residual’s distribution might not be defined by any classical parametric distribution. Kernel-based method has been extended to estimate conditional densities instead of conditional distributions when data incorporate both discrete and continuous attributes. The method often has been based on smoothing parameters to use optimal values for various attributes. Thus, in case of an explanatory variable being independent of the dependent variable, that attribute would be dropped in the nonparametric method by assigning a large smoothing parameter, giving them uniform distributions so their variances to the model’s variance would be minimal. Objectives: The objective of this study was to identify factors to the severity of pedestrian crashes based on an unbiased method. Especially, this study was conducted to evaluate the applicability of kernel-based techniques of semi- and nonparametric methods on the crash dataset by means of confusion techniques. Methods: In this study, two non- and semi-parametric kernel-based methods were implemented to model the severity of pedestrian crashes. The estimation of the semi-parametric densities is based on the adoptive local smoothing and maximization of the quasi-likelihood function, which is similar somehow to the likelihood of the binary logit model. On the other hand, the nonparametric method is based on the selection of optimal smoothing parameters in estimation of the conditional probability density function to minimize mean integrated squared error (MISE). The performances of those models are evaluated by their prediction power. To have a benchmark for comparison, the standard logistic regression was also employed. Although those methods have been employed in other fields, this is one of the earliest studies that employed those techniques in the context of traffic safety. Results: The results highlighted that the nonparametric kernel-based method outperforms the semi-parametric (single-index model) and the standard logit model based on the confusion matrices. To have a vision about the bandwidth selection method for removal of the irrelevant attributes in nonparametric approach, we added some noisy predictors to the models and a comparison was made. Extensive discussion has been made in the content of this study regarding the methodological approach of the models. Conclusion: To summarize, alcohol and drug involvement, driving on non-level grade, and bad lighting conditions are some of the factors that increase the likelihood of pedestrian crash severity. This is one of the earliest studies that implemented the methods in the context of transportation problems. The nonparametric method is especially recommended to be used in the field of traffic safety when there are uncertainties regarding the importance of predictors as the technique would automatically drop unimportant predictors.

Macroscopic analysis on the frequency and severity of pedestrian crashes on National Roads in Metro Manila, Philippines

Pedestrians are the most vulnerable road users; thus, understanding the primary factors that lead to pedestrian crashes is a chief concern in road safety. However, owing to the limitations of crash data in developing countries, only a few studies have evaluated the comprehensive characteristics of pedestrian crashes, specifically on different road types. This study attempted to develop pedestrian crash frequency and severity models on national roads by using the road characteristics and built environment parameters, based on the road crash data (2016–2018) that involved pedestrians in Metro Manila, Philippines. Remarkable findings included primary roads, presence of footbridges, road sections with bad surface conditions, and increased fractions of commercial, residential, and industrial roads, which exhibited a greater likelihood of pedestrian crashes. Crashes involving elderly pedestrians, heavier vehicles, late-night hours, fair surface conditions, and open spaces were associated with increased likelihoods of fatal outcomes. Essentially, this study provides a macroscopic perspective in understanding the factors associated with the severity and frequency of pedestrian crashes, and it would aid the authorities in identifying proper countermeasures.