A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics

A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne

The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne's zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.

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

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

Investigating built environment and traffic flow impact on crash frequency in urban road networks

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity

Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities.

Exploring associations between built environment and crash risk of children in school commuting

Understanding how built environment are associated with crash risk (CR) in school commuting is essential to improving travel safety through land use and transportation policies. Scholars often assume that this relationship is consistent across space, but this may lead to inconsistent estimates. To address this issue, using data in Shenzhen, China, the data covers traffic accident data of children taken from police incident reports and supplemented with local land use, transportation network and specific school information. The measurement model of crash scale was conducted to represent crash severity, and the CR was further quantified. The study applies three models, spatial dubin model (SDM), geographically weighted regression (GWR), and mixed GWR (MGWR), to explore spatio-temporal heterogeneity relationships between built environment attributes and CR of children in school commuting. The findings reveal that the crash scale can better represent crash severity of school commuting than a single indicator. Policy interventions should be targeted at specific spatial scales, school types, and time windows to effectively improve travel safety. However, there are some common findings. It is recommended to use a scale of 200 m to explain the relationship between the variables. The MGWR model outperforms the other two models. To reduce CR, it is important to consider lower road network density, a reasonable layout of educational facilities, fewer bus routes, and more on-street parking spaces. Our findings can help to enrich the understanding of associations between land use and CR of children, as well as offer local planning and operating guidance for creating child-friendly environment.

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).

The effect of PM2.5 exposure on the mortality of patients with hepatocellular carcinoma in Tianjin, China

Several studies have shown the effects of PM_2.5 exposure on respiratory and cardiovascular systems. However, there is no cohort study evidence of adverse effects of PM_2.5 exposure on survival in patients with hepatocellular carcinoma (HCC) in China. This study is aimed at evaluating this association. This cohort study included 1440 HCC patients treated at the Third Central Clinical College of Tianjin Medical University from September 2013 to December 2018. We collected patient information, including demographic data, medical history, lifestyle characteristics, and disease characteristics. Based on PM_2.5 concentrations measured at monitoring stations, the inverse distance weighted (IDW) method was used to assess the individuals' exposure during their survival period. Survival status was analysed by the Kaplan-Meier method. Restricted cubic splines and Cox proportional hazards models were used to estimate the relationship between PM_2.5 and mortality, and potential confounders were adjusted for. The mortality rate of HCC patients exposed to PM_2.5 ≥ 58.56 μg/m³ was significantly higher than that of HCC patients living in environments with PM_2.5 < 58.56 μg/m³ (79.0% vs 50.7%, P < 0.001). The restricted cubic spline model showed a linear relationship between the PM_2.5 concentration and mortality risk (P overall-association < 0.0001 and P nonlinear-association = 0.3568). Cox regression analysis showed that after adjusting for confounding factors, for every 10-μg/m³ increase in atmospheric PM_2.5, the risk of death for HCC patients increased by 44% [hazard ratio (HR) = 1.44, 95% confidence interval (CI) 1.34, 1.56; P < 0.001]. Compared with patients exposed to PM_2.5 <58.56 μg/m³, those exposed to PM_2.5 ≥ 58.56 μg/m³ had a 1.55-fold increased risk of death. Stratified analysis results showed that the effects of PM_2.5 on HCC mortality were more significant in patients aged ≥60 years or patients living in central urban areas. We found that exposure to elevated PM_2.5 after HCC diagnosis may affect survival, with a higher concentration corresponding to a greater effect.

Do footbridge and underpass improve pedestrian safety? A Hong Kong case study using three-dimensional digital map of pedestrian network

Hong Kong is a compact city with high activity and travel intensity. In the past decades, many footbridges and underpasses were installed to reduce the pedestrian-vehicle conflicts on urban roads. However, it is rare that the effects of configuration of pedestrian network on pedestrian crashes are investigated. In Hong Kong, many footbridges and underpasses are connected to major transport hubs and commercial building development and become parts of giant elevated and underground walkway systems. It is challenging to characterize such a complicated pedestrian network. In this study, a three-dimensional digital map is applied to estimate the connectivity and accessibility of pedestrian network, and measure the relationship between pedestrian network characteristics and pedestrian safety at the macroscopic level. Hence, the effects of footbridge and underpass on pedestrian safety are examined. For example, comprehensive built environment, pedestrian network, traffic, and crash data are aggregated to 379 grids (0.5 km × 0.5 km). Then, multivariate Poisson lognormal regression approach is applied to model fatal and severe injury (FSI) and slight injury pedestrian crashes, with which the effects of unobserved heterogeneity, spatial correlation, and correlation between crash counts are accounted. Results indicate that population density, traffic volume, walking trip, footpath density, node density, number of vertices per footpath segment, bus stop, metro exit, residential area, commercial area, and government and utility area are positively associated with pedestrian crashes. In contrast, average gradient, accessibility of footbridge, accessibility of underpass, and number of crossings per road segment are negatively associated with pedestrian crashes. In other word, pedestrian safety would be improved when footbridge and underpass are more accessible. Findings have implications for the design and planning of pedestrian network to promote walkability and improve pedestrian safety.

Road crashes in Adelaide metropolitan region, the consequences of COVID-19

Background

Many countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries.

Methods

At the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied.

Results

This research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer.

Implications

The outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

Pedestrian crash frequency: Unpacking the effects of contributing factors and racial disparities

In this paper, we unpack the magnitude effects of the determinants of pedestrian crashes using a multivariate analysis approach. We consider four sets of exogenous factors that characterize residential neighborhoods as well as potentially affect pedestrian crashes and the racial composition of such crashes: (1) crash risk exposure (CE) attributes, (2) cultural variables, (3) built environment (BE) features, and (4) sociodemographic (SD) factors. Our investigation uses pedestrian crash and related data from the City of Houston, Texas, which we analyze at the spatial Census Block Group (CBG) level. Our results indicate that social resistance considerations (that is, minorities resisting norms as they are perceived as being set by the majority group), density of transit stops, and road design considerations (in particular in and around areas with high land-use diversity) are the three strongest determinants of pedestrian crashes, particularly in CBGs with a majority of the resident population being Black. The findings of this study can enable policymakers and planners to develop more effective countermeasures and interventions to contain the growing number of pedestrian crashes in recent years, as well as racial disparities in pedestrian crashes. Importantly, transportation safety engineers need to work with social scientists and engage with community leaders to build trust before leaping into implementing planning countermeasures and interventions. Issues of social resistance, in particular, need to be kept in mind.

The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach

INTRODUCTION

Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies.

METHOD

This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance.

RESULTS

This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area.

CONCLUSION

These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces.

PRACTICAL APPLICATIONS

The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.

Effect of urban street trees on pedestrian safety: A micro-level pedestrian casualty model using multivariate Bayesian spatial approach

In the past decades, trees were considered roadside hazard. Street trees were removed to provide clear zone and improve roadside safety. Nowadays, street trees are considered to play an important role in urban design. Also, street tree is considered a traffic calming measure. Studies have examined the effects of urban street trees on driver perception, driving behaviour, and general road safety. However, it is rare that the relationship between urban street trees and pedestrian safety is investigated. In this study, a micro-level frequency model is established to evaluate the effects of tree density and tree canopy cover on pedestrian injuries, accounting for pedestrian crash exposure based on comprehensive pedestrian count data from a state in Australia, Melbourne. In addition, effects of road geometry, traffic characteristics, and temporal distribution are also considered. Furthermore, effects of spatial dependency and correlation between pedestrian casualty counts of different injury severity levels are accounted using a multivariate Bayesian spatial approach. Results indicate that road width, bus stop, tram station, on-street parking, and 85th percentile speed are positively associated with pedestrian casualty. In contrast, pedestrian casualty decreases when there is a pedestrian crosswalk and increases in tree density and canopy. Also, time variation in pedestrian injury risk is significant. To sum up, urban street trees should have favorable effect on pedestrian safety. Findings are indicative to optimal policy strategies that can enhance the walking environment and overall pedestrian safety. Therefore, sustainable urban and transport development can be promoted.

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

Macro-level literature analysis on pedestrian safety: Bibliometric overview, conceptual frames, and trends

Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: "Pedestrian crash frequency models", "Pedestrian injury severity crash models", "Traffic engineering measures in pedestrians' safety", "Global reports around pedestrian accident epidemiology", "Effect of age and gender on pedestrians' behavior", "Distraction of pedestrians", and "Pedestrian crowd dynamics and evacuation". Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely "Collision avoidance and intelligent transportation systems (ITS)", "Epidemiological studies of pedestrian injury and prevention", "Pedestrian road crossing and behavioral factors", "Pedestrian flow simulation", and "Walkable environment and pedestrian safety". Finally, "autonomous vehicle", "pedestrian detection", and "collision avoidance" themes were identified as having the greatest centrality and development degrees in recent years.

Exploring the impacts of street layout on the frequency of pedestrian crashes: A micro-level study

INTRODUCTION

Pedestrian safety has become a critical issue since walking is increasingly promoted as a sustainable transport mode. However, pedestrians are vulnerable to severe injury and mortality in road crashes. Therefore, it is important to understand the factors that affect the safety of pedestrians. This paper investigates the impacts of street layout on the frequency of pedestrian crashes by examining the interactive pattern of built environment, crossing facilities, and road characteristics.

METHOD

A surrogate exposure variable of pedestrian crashes at the road-segment level is proposed by considering the locations of crossing facilities, distribution of points of interest (POIs), road characteristics, and pedestrian activities. A network-based kernel density technique is used to identify the pedestrian crash risk at the road segment level. Bayesian spatial models based on different exposure variables are employed and compared.

RESULTS

The results suggest that models using the surrogate exposure of pedestrian crashes provide better model fit than the ones simply using the density of pedestrians. It is also found that the presence of POIs is related to a higher risk of pedestrian-vehicle crash. In addition, a significantly higher number of pedestrian crashes are found to occur on segments with more bus stops and metro stations. Results also show that the longer the distance between the crossing facilities and road segments, the more pedestrian crashes are observed.

CONCLUSIONS

The proposed aggregated indicator can provide more efficient exposure and higher prediction accuracy than the density of pedestrians. Besides, the POIs, crossing facilities, and road types were all significantly related to pedestrian crashes.

PRACTICAL APPLICATIONS

Our results suggest that the locations of POIs and transport facilities should be planned in a way that can decrease the number of road crossed or guide pedestrians to take safe crossing path.

Predicting effects of built environment on fatal pedestrian accidents at location-specific level: Application of XGBoost and SHAP

Understanding locally heterogeneous physical contexts in built environment is of great importance in developing preemptive countermeasures to mitigate pedestrian fatality risks. In this study, we aim to investigate the non-linear relationship between physical factors and pedestrian fatality at a location-specific level using a machine learning approach. The state-of-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), is employed for a binary classification problem, in which nationwide locations where fatal pedestrian accidents occurred for the years from 2012 to 2019 in Korea serve as positive samples (n_p = 13,366). For negative samples, locations with no pedestrian accidents are selected randomly to the size that is 10 times larger (n_n = 133,660) than positive samples. Fifteen features under the categories of road conditions, road facilities, road networks, and land uses are assigned to both the positive and negative sample locations using Geographic Information System (GIS). A method is proposed to avoid the class imbalance problem, and a final unbiased model is utilized to predict fatal pedestrian risks at the negative sample locations. In addition, Shapley Additive Explanations (SHAP) is introduced to provide a robust interpretation of the XGBoos prediction results. It is shown that 21.6% of the negative sample locations have a probability of fatal pedestrian accidents greater than 0.5 (or 78.4% accuracy). Generally, a road segment that lies in many of the shortest routes in a dense residential area with many lively activities from aligned buildings is a potential spot for fatal pedestrian accidents. However, based on the SHAP interpretation, the relationships between the features and pedestrian fatality are found nonlinear and locally heterogeneous. We discuss the implications of this result has for drafting policy recommendations to reduce pedestrian fatalities.

Uncertainty matters: Bayesian modeling of bicycle crashes with incomplete exposure data

BACKGROUND

One major challenge faced by neighborhood-level bicycle safety analysis is the lack of complete and reliable exposure data for the entire area under investigation. Although the conventional travel-diary surveys, together with the emerging smartphone fitness applications and bike-sharing systems, provide straightforward and valuable opportunities to estimate territory-wide bicycle activities, the obtained ridership suffers inherently from underreporting.

METHODS

We introduced the Bayesian simultaneous-equation model as a sound methodological alternative here to address the uncertainty arising from incomplete exposure data when modeling bicycle crashes. The proposed method was successfully fitted to a crowdsourced dataset of 792 bicycle-motor vehicle (BMV) crashes aggregated from 209 neighborhoods over a 3-year period in Hong Kong.

RESULTS

Our analysis empirically demonstrated the bias due to omission of activity-based exposure measures or to the direct use of cycling distance extracted from the travel-diary survey without correcting for incompleteness. By modeling bicycle activities and the frequency of BMV crashes simultaneously, we also provided new evidence that an expansion of bicycle infrastructure was likely associated with a significant increase in cycling levels and a substantial reduction in the risk of BMV crashes, despite a slight increase in the absolute number of BMV crashes.

CONCLUSIONS

Our approach is promising in adjusting for the uncertainty in raw exposure data, extrapolating the missing exposure values, and untangling the linkage among built environment, bicycle activities, and the frequency of BMV crashes within a unified framework. To promote safer cycling, designated facilities should be provided to consecutively separate cyclists from motor vehicles.

Proactive pedestrian safety evaluation at urban road network level, an experience in Kolkata City, India

In developing nations, road traffic crashes involving pedestrians have become a foremost worry. Presently, most of the road safety assessment projects and selection of interventions are still restricted to traditional methods that depend on historical crash data. However, in low and middle-income countries such as India, the availability, reliability, and accuracy of crash data are uncertain. Alternatively, Post Encroachment Time (PET) has added attention as a proximal indicator to examine pedestrian-vehicular potential crashes and address pedestrian risk under mixed traffic conditions. Hence, it will be meaningful to examine if the PET is a good substitute for pedestrian-vehicular crashes and if so, what built environment and pedestrian-level factors influence PET. In this background, the present study establishes a mathematical association between the average PET value of the urban road network level and actual crashes. Afterward, multiple linear regression models are developed to study the impact of the built environment, traffic parameters, and pedestrian-level attributes on PET. The outcomes indicate that vehicle speed, lack of enforcement, absence of traffic signal (for traffic as well as pedestrians), land use type, slum population, inadequate sight distance, pedestrian's state of crossing, and pedestrian's risky crossing behaviour substantially affect the average PET at road network-level.