Macroscopic spatial analysis of pedestrian and bicycle crashes

Evaluating the safety impact of mid-block pedestrian signals (MPS)

The Florida Department of Transportation (FDOT) has recently started implementing a new signal system at mid-blocks called Mid-block Pedestrian Signals (MPS). This study aims to evaluate the effectiveness of these newly implemented MPSs. A total of 260 h of video data were collected from five locations across Florida, with 130 h recorded before MPS installation and 130 h after installation, including both weekdays and weekends. State-of-the-art computer vision technology was employed to detect and track various road users. A random parameters multinomial logit model with heterogeneity in the means was implemented to assess safety of vehicle-pedestrian interaction by three conflict categories: No Conflict, Moderate Conflict, and Serious Conflict. Relative-Time-to-Collision (RTTC) values were utilized to classify these level of conflicts. The analysis demonstrates that the presence of MPS significantly enhances safety outcomes by increasing the likelihood of avoiding conflicts and reducing the probabilities of both moderate and serious conflicts. Key factors influencing conflict probabilities were identified, including pedestrian and vehicle counts, average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix, all of which increase the probability of serious conflicts. Interestingly, the analysis identified three significant interaction variables with MPS: average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix. While these factors individually had a higher probability of leading to serious conflicts, the presence of MPS effectively mitigates these risks by moderating their adverse effects, increasing the likelihood of no conflicts. These results underscore the importance of MPS as an effective measure to improve safety at mid-block crossings.

Examining the nonlinear effects of traffic and built environment factors on the traffic safety of cyclist from different age groups

In the Netherlands and all over the world, traffic safety problem has been growing particularly for cyclists over the last decades with more people shifting to cycling as a healthy and sustainable mode of transport. Literature shows that age is an important factor in crash involvement and consequences; however, few studies identify the risk factors for cyclists from across different age groups. Therefore, this study aims to identify and understand the effects of traffic, infrastructure, and land use factors on vehicle-to-bike injury and fatal crashes involving cyclists from different age groups. For this purpose, we adopted an approach consisting of resampling and machine learning (XGBoost-Tweedie) techniques to analyse police-reported crashes between the years 2015 and 2019 in the Netherlands. The analysis shows that effects of external variables on crashes widely vary among different age groups and the analysis of total crash rates may not disclose the nature of crashes of cyclist from different age groups. The analysis also shed light on the nonlinear effects of traffic and built environment factors on cyclist crashes, which are usually disregarded in the traffic safety literature. The proposed approach and findings provide a profound understanding of the nature of cyclist crashes and the complex relationships between factors, which can contribute to developing effective crash prevention strategies tailored to different age groups.

A hybrid approach for modeling bicycle crash frequencies: Integrating random forest based SHAP model with random parameter negative binomial regression model

To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley Additive exPlanations (RF-SHAP) method with a random parameter negative binomial regression model (RPNB). First, four machine learning algorithms, including random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and Extreme Gradient Boosting (XGBoost), were compared for variable importance calculation. The RF algorithm, demonstrating the best performance, was selected and integrated into an interpretable machine learning-based method (i.e., RF-SHAP) to provide an interpretable measure of each variable's impact, which is critical for understanding the model's predictions results. Finally, the RF-SHAP method was combined with the RPNB model to explore individual-specific variations that influence crash frequency predictions. Using 288 traffic analysis zones (TAZs) in Greater London and various regional risk factors for bicycle crash frequency, the proposed framework was validated. The results indicate that the proposed framework demonstrates improved prediction accuracy and better factor interpretation in analyzing bicycle crash frequency. The model exhibits consistent Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, indicating its reliable explanatory power. Furthermore, there is a significant improvement in the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This suggests that the proposed model effectively combines the explanatory power of statistical models with the forecasting powers of data-driven models. The interpretability of SHAP values, coupled with the causal insights from RPNB, provides policymakers with actionable information to develop targeted interventions.

Road urban planning sustainability based on remote sensing and satellite dataset: A review

Infrastructural development and urbanization effects have been investigated over the past decades with novel approaches and adaptation strategies. Road network expansions are more useful for the socio-economic development from urban to rural areas where 75 % of the passenger, and goods transportation sectors are influenced by the road. Road infrastructure and urbanization are perpendicular to each other, and this research investigation indicates that the novel approaches and adaptation strategies for road infrastructure and urbanization effects. This study evaluated the trend in the road network and urbanization-related literature from 2010 to 2022 with some measurable keywords. Around 370 pieces of research literature are analysis and around 85 research evaluations for the road network and urbanization-related Land use and land cover (LULC) studies while numerous road network analysis approaches and LULC-related investigations are evaluated in this research. Three major parts road network analysis-related approaches, LULC, and urbanization-related approaches related to road network expansion and urbanization, were investigated. In this work, many research publications' approaches to LULC simulation, kernel density, shortage distance, and picture classification are discussed and assessed. The survey is more valuable for urban planners, future disaster management teams, and administrators to implement the shortage distance analysis, reduction of road accidents, and urbanization effects on the environment.

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.

Cycling safely: Examining the factors associated with bicycle accidents in Seoul, South Korea

This study investigates the factors contributing to bicycle accidents, focusing on four types of bicycle lanes and other exposure and built environment characteristics of census blocks. Using Seoul as a case study, three years of bicycle accident spot data from 2018 to 2020 was collected, resulting in 1,330 bicycle accident spots and a total of 2,072 accidents. The geographically weighted Poisson regression (GWPR) model was used as a methodological approach to investigate the spatially varying relationships between the accident frequency and explanatory variables across the space, as opposed to the Poisson regression model. The results indicated that the GWPR model outperforms the global Poisson regression model in capturing unobserved spatial heterogeneity. For example, the value of deviance that determines the goodness of fit for a model was 0.244 for the Poisson regression model and 0.500 for the far better-fitting GWPR model. Further findings revealed that the factors affecting bicycle accidents have varying impacts depending on the location and distribution of accidents. For example, despite the presence of bicycle lanes, some census blocks, particularly in the northeast part of the city, still pose a risk for bicycle accidents. These findings can provide valuable insights for urban planners and policymakers in developing bicycle safety measures and regulations.

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.

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

INTRODUCTION

Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved.

METHOD

In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country.

RESULTS

The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.

From sky to road: Incorporating the satellite imagery into analysis of freight truck-related crash factors

Freight truck-related crashes in urban contexts have caused significant economic losses and casualties, making it increasingly essential to understand the spatial patterns of such crashes. Limitations regarding data availability have greatly undermined the generalizability and applicability of certain prior research findings. This study explores the potential of emerging geospatial data to delve deeply into the determinants of these incidents with a more generalizable research design. By synergizing high-resolution satellite imagery with refined GIS map data and geospatial tabular data, a rich tapestry of the road environment and freight truck operations emerges. To navigate the challenges of zero-inflated issues of the crash datasets, the Tweedie Gradient Boosting model is adopted. Results reveal a pronounced spatial heterogeneity between highway and urban non-highway road networks in crash determinants. Factors such as freight truck activity, intricate road network patterns, and vehicular densities rise to prominence, albeit with varying degrees of influence across highways and urban non-highway terrains. Results emphasize the need for context-specific interventions for policymakers, encompassing optimized urban planning, infrastructural overhauls, and refined traffic management protocols. This endeavor may not only elevate the academic discourse around freight truck-related crashes but also champion a data-driven approach towards safer road ecosystems for all.

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 the spatial relationship of e-bike and motorcycle crashes: Implications for risk reduction

INTRODUCTION

Electric bicycles, or e-bikes, have become very popular over the past decade. In order to reduce the risk of crashes, it is necessary to understand the contributing factors. While several researchers have examined these elements, few have considered the spatial heterogeneity between crashes and environmental variables, such as Points of Interest (POI). In addition, there is a scarcity of studies comparing the crash-related factors of e-bikes and motorcycles. Despite their differing speed and range capabilities, different POIs also tend to impact area/bandwidths differently because e-bikes cannot cover the same range that motorcycles can.

METHOD

In this study, we compared e-bike and motorcycle crashes at 11 different types of POIs in Taipei from 2016 to 2020. Since crashes are sparse events and easily affected by the Modifiable Areal Unit Problem (MAUP), Kernel Density Estimation (KDE) was employed to transform crash points (count data) to crash risk surfaces (continuous data). Additionally, an advanced variant of Geographical Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR) utilized to predict crash risk because each predictor is allowed to have a different bandwidth.

RESULTS

The results showed: (a) For e-bike crashes, the MGWR model outperformed the GWR and OLS models in terms of AIC values, while the MGWR and GWR performed similarly with regard to motorcycle crashes; (b) The analysis revealed e-bike and motorcycle crash risk to be associated with various types of POIs. E-bike crashes tended to occur more frequently in areas with more schools, supermarkets, intersections, and elderly people. Meanwhile, motorcycle crashes were more likely to occur in areas with a high number of restaurants and intersections. The search bandwidths of e-bikes are inconsistent and narrower than those of motorcycles.

Factors associated with different types of freight crashes: A macro-level analysis

INTRODUCTION

Despite evidence showing higher fatality rates in freight-related crashes, there has been limited exploration of their spatial distribution and factors associated with such distribution. This gap in the literature primarily stems from the focus of existing studies on micro-level factors predicting the frequency or severity of injuries in freight crashes. The present study delves into the factors contributing to freight crashes at the neighborhood level, particularly focusing on different types of freight crashes: collisions involving a freight vehicle and a passenger vehicle, crashes between freight vehicles, and freight vehicle-non-motorized crashes.

METHOD

This study analyzes traffic crash data from the urbanized region of Seoul, collected between 2016 and 2019. To effectively deal with spatial autocorrelation and model different types of crashes in a unified framework, a Bayesian multivariate conditional autoregressive model was employed.

RESULTS

Findings show substantial differences in the factors associated with various types of freight crashes. The predictors for crashes between freight vehicles diverge significantly from those for freight vehicle-non-motorized crashes. Crashes between freight vehicles are relatively more influenced by road network structure, while freight crashes involving non-motorized users are relatively more affected by the built environment and freight facilities than the other crash types examined. Freight vehicle-passenger vehicle crashes fall into an intermediate category, sharing most predictors with either of the other two types of freight crashes.

CONCLUSIONS AND PRACTICAL APPLICATIONS

The findings of this study offer valuable lessons for transportation practitioners and policymakers. They can guide the formulation of effective land use policies and infrastructure planning, specifically designed to address the unique characteristics of different types of freight crashes.

Geographically weighted random forests for macro-level crash frequency prediction

Machine learning models such as random forests (RF) have been widely applied in the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a single decision tree such as overfitting and instability. However, the traditional RF is a global concept, and thus may fail to capture spatial variability. In macro-level analysis of road safety, the relationship between crash frequency and risk factors can vary spatially. To address this issue, we employ a modified RF algorithm, named geographically weighted random forest (GWRF). Based on the data from London at the level of Middle-super-output-area (MSOA), the predictive performances of RF and GWRF are compared using mean absolute error (MAE) and root mean square error (RMSE). Moreover, considering MSOAs are geographically connected with each other, several factors related to the discrepancies between adjacent zones are also included in the models. Our results indicate that GWRF outperforms the traditional RF and GWR when an appropriate bandwidth is selected. We further explore the effects of multicollinearity on model performance. The results show that prediction accuracy of GWRF models are not susceptible to the multicollinearity. However, the importance values of those variables with multicollinearity may reduce. Finally, and of equal importance, it is found that the importance of each explanatory variable varies across zones. The density of minor road makes the highest contribution to crash frequency in downtown area, while the crash frequency in peripheral area is more sensitive to the discrepancy of road environment between MSOAs. With such information, road safety interventions can be designed and implemented according to the locally important factors, avoiding thus general guidelines addressed for the entire city.

Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis

Understanding the relationship between social disparities and traffic crash frequency is essential for long-term transportation planning and policymaking. Few studies have systemically examined the influence of socioeconomic and infrastructure-related disparities in macro-level traffic crash frequency. This study provides a framework to spatially examine the relationships between crash rates and demographic and socioeconomic characteristics, as well as roadway infrastructure and traffic characteristics at the Census Block Groups (CBGs) level. Spatial autocorrelation analysis was first performed on the residual of the Ordinary Least Squares (OLS) model to identify whether non-stationarity exists. Then, the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model were applied to assess the impacts of these factors on crash rates spatially and statistically. Our findings indicate that MGWR outperforms both OLS and GWR in uncovering the spatial relationships between contributing factors and both fatal and injury (FI) crashes as well as property damage only (PDO) crashes. A thorough examination of local coefficient maps highlighted six pivotal variables that significantly influenced a majority of CBGs. Improving infrastructure, including pedestrian pathways and public transit facilities, in low-income areas can offer significant benefits. These findings and recommendations can inform the development of effective strategies for reducing crashes and guide the appropriate selection of modeling techniques for macro-level crash analysis.

Risk exposure factors influencing the frequency of road crashes during the COVID-19 pandemic in Ciudad Juarez, Mexico. A negative binomial spatial regression model

The article aims to investigate the influence of risk exposure factors on the frequency of road crashes from January to August 2020 in Ciudad Juarez, Mexico. It is a longitudinal study with four data sets: road crashes, population and housing census, location of economic activities, and road network information. Specifically, this study investigates the relationship between exposure factors - demographics, main roads and land use - and road crashes. A mixed method analysis was employed, (1) spatial analysis using GIS techniques; and (2) a negative binomial spatial regression model. The results showed a strong spatial dependence (0.274; p-value 0.00) of road crashes in the census tracts, and this effect was statistically significant (0.007) in the spatial regression model. In the model, a high probability (<0.05) of road crashes in the census tracts was found with the population aged 15 to 65 years, the length of main roads and the level of road coverage (Engel index), land uses with economic activities of an industrial and commercial character. The findings of this study successfully capture the social, economic, and urban conditions during the January-August 2020 period in the context of the COVID-19 pandemic. This new knowledge could help create preventive plans and policies to address the frequency of road crashes.

Systematic literature review of 10 years of cyclist safety research

Cyclist safety is a research field that is gaining increasing interest and attention, but still offers questions and challenges open to the scientific community. The aim of this study was to provide an exhaustive review of scientific publications in the cyclist safety field. For this purpose, Bibliometrix-R tool was used to analyse 1066 documents retrieved from Web of Science (WoS) between 2012 and 2021. The study examined published sources and productive scholars by exposing their most influential contributions, presented institutions and countries most contributing to cyclist safety and explored countries open towards international collaborations. A keywords analysis provided the most frequent author keywords in cyclist safety shown in a word cloud with E-bike, behaviour, and crash severity representing the primary keywords. Furthermore, a thematic map of cyclist safety field drafted from the author's keywords was identified. The strategic diagram is divided in four quadrants and, according to both density and centrality, the themes can be classified as follows: 1) motor themes, characterized by high value of both centrality and density; 2) niche themes, defined by high density and low centrality; 3) emerging or declining themes, featured by low value of both centrality and density; and 4) basic themes, distinguished by high centrality and low density. The motor themes (i.e., the main topics in cyclist safety field) crash severity and bike network were further explored. The research findings will be useful to develop strategies for making bike a safer and more confident form of transport as well as to guide researchers towards the future scientific knowledge.

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.

Assessing the effectiveness of built environment-based safety measures in urban and rural areas for reducing the non-motorist crashes

Introduction

Built environment (BE) has a well-documented impact on non-motorist crashes. Interestingly, the urban-rural distinction of the impacts received scant attention in the literature. Moreover, the combined effect of these elements are less studied than their standalone effects.

Objective

This study explores the combined effectiveness of built environment-based safety measures in urban and rural settings.

Data and method

The study uses nine years (2011-2019) of non-motorist (pedestrian and bicyclist) crash data in Florida. It classifies urban and rural areas with the multivariate clustering method and models the crash count with Log-transformed Spatial Error Models.

Results

Findings suggest that urban areas, tracts with low median income, a lower percentage of senior citizens, and a higher percentage of black, white, and Hispanic people are significantly associated with a high number of nonmotorist crashes. The percentage of pedestrian and bicyclist commuters is positively associated with pedestrian and bicycle crash count, respectively. Among BE variables, more crashes are observed in tracts with more commercial land use (LU), less recreational LU, higher LU mix, more traffic, signalized intersection, transit stops, and sidewalks. Having more traffic and fewer transit stops pose lesser risk in urban areas than rural areas. The combined effects suggest that increasing commercial LU where LU entropy is high (or vice-versa) will help to reduce nonmotorist crashes. Also, in high entropy areas, increasing rural traffic is riskier whereas increasing urban traffic is safer.

Conclusion

This paper documents the need of considering urban-rural differences and interaction effects among BE elements for nonmotorist safety.

Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm

Pedestrians and bicyclists from marginalized and underserved populations experienced disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity among road users of different races and the increasing trend of traffic risk for underserved racial groups called for an urgent agenda for transportation policy making and research to ensure equity in roadway safety. Pedestrian and bicyclist crashes involved drivers and pedestrians/bicyclists; the latter were usually victims. Traditional safety studies did not account for the interaction between the two parties and assumed that they were independent from each other. In this study we paired the driver and pedestrian/bicyclist involved in the same crash to understand the socioeconomic and demographic make-up of the two parties involved in crashes and assessed the geographic distribution of these crashes and crash-contributing factors. For this purpose, we applied thelatent class clustering analysis (LCA) to classify different crash types and analyze the patterns of the crashes based on the income and ethnicity of both drivers and victims involved in pedestrian and bicyclist crashes. We then used random forest algorithms and partial dependence plots (PDPs) to model and interpreted the contributing factors of the clusters in both pedestrian and bicyclist models. The clustering results showed a pattern of social segregation in pedestrian and bicyclist crashes that drivers and victims with similar socioeconomic characteristics tend to be involved in one crash. Pedestrian/bicyclist exposure, driver's age, victim's age, year of the car in use, annual average daily traffic (AADT), speed limit, roadbed width, and lane width were the most influential factors contributing to this pattern. Crashes that involved drivers and victims with lower income and non-white ethnicity tended to happen in the location with higher pedestrian/bicyclist exposure, higher speed limit, and wider road. The findings of this research can help to inform the decision-making process for improving safety to ensure equitable and sustainable safety for all road users and communities.

Macroscopic Spatial Analysis of the Impact of Socioeconomic, Land Use and Mobility Factors on the Frequency of Traffic Accidents in Bogotá

The urban structure of a city, defined by its inhabitants, daily movements, and land use, has become an environmental factor of interest that is related to traffic accidents. Traditionally, macro modeling is usually implemented using spatial econometric methods; however, techniques such as support vector regression have proven to be efficient in identifying the relationships between factors at the zonal level and the frequency associated with these events when considering the autocorrelation between spatial units. As a result of this, the main objective of this study was to evaluate the impact of socioeconomical, land use, and mobility variables on the frequency of traffic accidents through the analysis of area data using spatial and vector support regression models. The spatial weighting matrix term was incorporated into the support vector regression models to compare the results against those that ignore it. The urban land of Bogotá, disaggregated into the territorial units of mobility analysis, was delimited as a study area. Two response variables were used: the traffic accidents index on the road perimeter and the traffic accidents index with deaths on the road perimeter, to analyze the total number of traffic accidents and the deaths caused by them. The results indicated that the rate of trips per person by taxi and motorcycle had the greatest impact on the increase in total accidents and deaths caused by them. Support vector regression models that incorporate the spatial structure allowed the modeling of the spatial dependency between spatial units with a better fit than the spatial regression models.

Factors impacting bike crash severity in urban areas

INTRODUCTION

Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks.

METHOD

The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee's two largest urban areas, Nashville and Memphis.

RESULTS

The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified.

CONCLUSION

Based on these results, a series of specific, suggested policy changes are presented for implementation consideration.

PRACTICAL APPLICATIONS

There is existing guidance in FHWA Lighting Handbook and TDOT's Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested.

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.

Mapping pedestrian safety studies between 2010 and 2021: A scientometric analysis

Pedestrian deaths constitute 23% of road traffic deaths globally. Although several research papers have contributed to pedestrian safety analysis, they did not provide a comprehensive overview of the progress in the research domain and publication trends. This makes it difficult to identify trends and insights into the pedestrian research domain in light of the voluminous number of papers. This study fills this gap with a scientometric analysis of research on pedestrian safety analysis indexed in the Web of Science. The scope covers 2594 papers published between 2010 and 2021 in English. This study analyzed the annual publications and citation trends, top ten most cited papers, influential papers in their first three years after publication, contributing authors, funding agencies, and contributing journals. The regional gaps between the proportion of pedestrian deaths and research were also analyzed. The results showed low research productivity from low and middle-income countries although they have a high incidence of pedestrian deaths. Subsequently, the main keyword clusters or frontier topics were identified and topic analysis was employed to identify the evolution of studies. Four keyword clusters were identified, i.e., "vehicle-to-pedestrian crash and injury severity analysis", "pedestrian movement and decision simulation experiments", "improving the vehicle system towards reducing body region impact injuries", "pedestrian behavior in crosswalks and signalized intersections". This study contributes an integrated knowledge map of pedestrian safety analysis, publication trends, the evolution of studies, and under-researched topics to guide future research work in pedestrian safety analysis.

The Use of Macro-Level Safety Performance Functions for Province-Wide Road Safety Management

Safety Performance Functions (SPFs) play a key role in identifying hotspots. Most SPFs were built at the micro-level, such as for road intersections or segments. On the other hand, in case of regional transportation planning, it may be useful to estimate SPFs at the macro-level (e.g., counties, cities, or towns) to determine ad hoc intervention prioritizations. Hence, the final aim of this study is to develop a predictive framework, supported by macro-level SPFs, to estimate crash frequencies, and consequently possible priority areas for interventions. At a province-wide level. The applicability of macro-level SPFs is investigated and tested thanks to the database retrieved in the context of a province-wide Sustainable Urban Mobility Plan (Bari, Italy). Starting from this database, the macro-areas of analysis were carved out by clustering cities and towns into census macro-zones, highlighting the potential need for safety interventions, according to different safety performance indicators (fatal + injury, fatal, pedestrian and bicycle crashes) and using basic predictors divided into geographic variables and road network-related factors. Safety performance indicators were differentiated into rural and urban, thus obtaining a set of 4 × 2 dependent variables. Then they were linked to the dependent variables by means of Negative Binomial (NB) count data models. The results show different trends for the urban and rural contexts. In the urban environment, where crashes are more frequent but less severe according to the available dataset, the increase in both population and area width leads to increasing crashes, while the increase in both road length and mean elevation are generally related to a decrease in crash occurrence. In the rural environment, the increase in population density, which was not considered in the urban context, strongly influences crash occurrence, especially leading to an increase in pedestrian and bicyclist fatal + injury crashes. The increase in the rural network length (excluding freeways) is generally related to a greater number of crashes as well. The application of this framework aims to reveal useful implications for planners and administrators who must select areas of intervention for safety purposes. Two examples of practical applications of this framework, related to safety-based infrastructural planning, are provided in this study.

Evaluation of Exclusive Pedestrian Phase Safety Performance at One-Level Signalized Intersections in Vilnius

This work aims to identify the effectiveness of one-level signalized intersections with exclusive pedestrian phases in terms of vehicle-pedestrian crashes resulting in pedestrian injury or fatality. The work analyzes and evaluates specific exclusive pedestrian phases without diagonal crossing possibility at one-level signalized intersections in the city of Vilnius. Anonymized data on traffic accidents from the Lithuanian Police Department Accident Register were used for safety analysis purposes. The traffic accident data cover all traffic accidents with dead or injured persons. The traffic accident data was analyzed with the help of QGIS for selected time intervals (before and after analysis). The density of traffic accidents was calculated with the help of the comparative analysis method at 11 signalized intersections in Vilnius City, where an exclusive pedestrian phase without diagonal crossing was implemented. An exclusive pedestrian phase with diagonal crossing is usually implemented to increase pedestrian safety at a signalized intersection with a high pedestrian intensity. The analysis carried out indicates that the specific exclusive pedestrian phase without diagonal crossings in Vilnius reduced pedestrian traffic accidents by up to 100%. No traffic accidents occurred after the installation of the exclusive pedestrian phase at intersections where there were no pedestrian accidents prior to the installation.

Predicting Pedestrian Crashes in Texas’ Intersections and Midblock Segments

This study analyzes pedestrian crash counts at more than one million intersections and midblock segments using Texas police reports over ten years. Developing large-scale micro-level analyses is challenging due to the lack of geographic information and characterization at a statewide scale. Therefore, key contributions include methods for obtaining many points and related variables across a vast network while controlling for traffic control variables (signalized intersections), highway design details, traffic attributes, and land use information across multiple sources. The analytical framework includes a method to estimate the intersection and midblock segments’ geometry and characteristics, data processing of historical pedestrian crashes and mapping to the estimated geometry, and the development of predictive models. A negative binomial model for crash counts across the state of Texas and within the city of Austin suggests that signalized intersections, arterial roads, more lanes, narrower or non-existent medians, and wider lanes coincide with higher crash rates per vehicle-mile traveled (VMT) and per walk-mile traveled. The analysis suggests that daily VMT increases the likelihood of pedestrian crashes, and midblock segments are more vulnerable than intersections, where one standard deviation increase in VMT caused an increase in crashes at intersections and midblock sections of 52% and 187%, respectively. Furthermore, the number of intersection crashes in Austin is higher than in the rest of Texas, but the number of midblock crashes is lower. Analysis of the Austin area suggests that the central business district location is critical, with midblock crashes being more sensitive (240%) in this area than intersection (78%) crashes. Moreover, a significant inequity was found in the area: an increase of USD 41,000 in average household income leads to a reduction of 32% (intersections) and 39% (midblock) in pedestrian crash rates.

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.

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.

A context-aware driver model for determining recommended speed in blind intersection situations

The near-miss events involving vulnerable road users can lead to serious accidents. Safe and careful expert drivers perform a hazard-anticipatory driving and they will naturally seek to reduce the uncertainty by attempting to fit their current driving context into a pre-existing category they have already developed, that is, predicting what can happen. In this study, our target situation consists of a cyclist attempting a road crossing at a blind spot. This study aims at developing a context-aware driver model for determining the recommended driving speed at blind intersections based on the analysis of near-miss-incidence database, which includes the data on driver behavior and road environmental factors just before the near-miss. First, we extracted the drive-recorder data using the management tool provided in the database. Second, risk, which is defined as the time margin for drivers to perform evasive actions to avoid a crash, was quantified for the extracted data using the safety-cushion time. The safety-cushion time can be observed as a result of the driver's adjustment to the vehicle velocity depending on the given road environment. One of the key aspects in developing the context-aware driver model is to categorize the extracted near-miss data into two levels based on the risk quantifications: low- and high-risk events. The low- and high-risk events were regarded as a result of the driver's appropriate adjustment of, and inability or failure to adjust the vehicle velocity depending on the given road environment, respectively. Third, based on a multiple linear regression analysis with low-risk event dataset, we constructed a context-aware driver model to produce the recommended vehicle speed depending on the given road environment. The road environment variables, determined by stepwise regression, were identified as factors that reduced or increased the vehicle velocity at blind intersections, and were incorporated into the model as predictors. Furthermore, we quantitatively visualized drivers setting the baseline for speed adjustment and increasing or decreasing the speed according to the given road environment context. Fourth, the model validation demonstrated a coefficient of determination (R²) of 0.20, and a mean absolute error (MAE) of 6.54 km/h on average in the 5-fold cross-validation. Finally, to investigate the effectiveness of the constructed driver model on safety performance, we used the dataset of high-risk events as test data. Theoretically, the constructed driver model guided the drivers to drive the vehicle at the recommended speed, and thus convert more than half of the high-risk events into low-risk events. These results indicate that the context-aware driver model is feasible to be used to adjust the approaching speed at blind intersections in accordance with the road environment factors.

Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field

INTRODUCTION

Scholarly research on road accidents over the past 50 years has generated substantial literature. We propose a robust search strategy to retrieve and analyze this literature.

METHOD

Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles.

RESULTS

The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity " and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature.

CONCLUSIONS

By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps. Practical Applications: The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.

Estimating the effect of vehicle speeds on bicycle and pedestrian safety on the Georgia arterial roadway network

Cyclists and pedestrians account for a disproportionate amount of the world's 1.3 million road deaths every year. This is a growing problem in the United Sates where bicyclist and pedestrian fatalities have increased steadily since 2009. A large body of research suggests vehicle speeds are a key contributing factor for crashes. However, few studies of bicycle or pedestrian crash probability incorporate detailed vehicle speed data. This study uses probe vehicle speed data to examine the impact of vehicle speeds on bicycle and pedestrian crashes on the state of Georgia's network of major arterial roadways. The analysis examines 7000 road segments throughout the state in 2017. A Negative Binomial model relates annual crash and speed data on each segment. Models using speed percentiles (85th, 50th and 15th) are contrasted with models using speed differences (85th-50th and 50th-15th percentile). A small set of covariates are included: segment length, number of lanes, Average Annual Daily Traffic, and urbanicity. Results indicate that larger differences in high-end speed percentiles are positively associated with bicycle and pedestrian crash frequency on Georgia arterials. Furthermore, the coefficients on the high end of the speed distribution, measured by the difference in 85th and 50th percentile speeds, have greater magnitude and statistical significance than the low end of the distribution. This research shows a negative relationship between speed and crashes may be flawed, as it does not account for the distributions of speed. The findings in this study suggest that planners and engineers should identify areas with large speed distributions, especially at the high vehicle speeds, and work to reduce the fastest speeds on these roadways. To do so, differences in speed percentiles measured using probe vehicle speeds can be used to determine where high risk areas are located.

Accounting for seasonal effects on cyclist-vehicle crashes

Crash data is usually aggregated over time where temporal correlation contributes to the unobserved heterogeneity. Since crashes that occur in temporal proximity share some unobserved characteristics, ignoring these temporal correlations in safety modeling may lead to biased estimates and a loss of model power. Seasonality has several effects on cyclists' travel behavior (e.g., the distribution of holidays, school schedules, weather variations) and consequently cyclist-vehicle crash risk. This study aims to account for the effect of seasonality on cyclist-vehicle crashes by employing two groups of models. The first group, seasonal cyclist-vehicle crash frequency, employs four vectors of the dependent variables for each season. The second group, rainfall involved cyclist-vehicle crash frequency, employs two vectors of the dependent variables for crashes that occurred on rainy days and non-rainy days. The two model groups were investigated using three modeling techniques: Full Bayes crash prediction model with spatial effects (base model), varying intercept and slope model, and First-Order Random Walk model with a spatial-temporal interaction term. Crash and volume data for 134 traffic analysis zones (TAZ's) in the City of Vancouver were used. The results showed that the First-Order Random Walk model with spatial-temporal interaction outperformed the other developed models. Some covariates have different associations with crashes depending on the season and rainfall conditions. For example, the seasonal estimates for the bus stop density are significantly higher for the summer and spring seasons than for the winter and autumn seasons. Also, the intersection density estimate for a rainy day is significantly higher than a non-rainy day. This indicates that on a rainy day each intersection to the network adds more risk to cyclists compared to a non-rainy day.

Geographically weighted poisson regression under linear model of coregionalization assistance: Application to a bicycle crash study

While cycling benefits individuals and society, cyclists are vulnerable road users, and their safety concerns arouse more macro-level spatial crash studies. Our study intends to investigate the spatial effects of population, land use, and bicycle lane infrastructures on bicycle crashes. This was done by developing a semi-parametric Geographically Weighted Poisson Regression (sGWPR) model which deals with the issue of spatial correlation and spatial non-stationarity simultaneously. It is a model that combines both constant and geographically varying parameters. To determine which parameter is fixed or non-stationary, previous studies suggest monitoring the Akaike Information Criterion (AICc). Yet, relying only on AICc might bury some spatial associations. So, in this study, we propose a Linear Model of Coregionalization (LMC) to assist the decision. Here, we use bicycle crash data across the metropolitan area of Greater Melbourne to establish sGWPR models suggested by AICc and LMC, respectively. Comparing the two sGWPR models, we found the sGWPR model under LMC results performs as well as sGWPR models suggested by AICc from the AICc perspective, and a 22.5% improvement in the mean squared error (MSE). It also uncovers more details about the spatial relationship between bicycle crashes and bicycle lane intersection density (BLID), an effect not suggested under AICc results. The parameters of BLID, a new measurement of bicycle lane facilities proposed by us, vary over space across analysis zones in Greater Melbourne.

Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.

An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark

INTRODUCTION

Cycling is one of the main forms of transportation in Denmark. However, while the number of traffic crash fatalities in the country has decreased over the past decade, the frequency of cyclists killed or seriously injured has increased. The high rate of serious injuries and fatalities associated with cycling emphasizes the increasing need for mitigating the severity of such crashes.

METHOD

This study conducted an in-depth analysis of cyclist injury severity resulting from single and multiparty bicycle-involved crashes. Detailed information was collected using self-reporting data undertaken in Denmark for a 12-month period between 1 November 2012 and 31 October 2013. Separate multilevel logistic (MLL) regression models were applied to estimate cyclist injury severity for single and multiparty crashes. The goodness-of-fit measures favored the MLL models over the standard logistic models, capturing the intercorrelation among bicycle crashes that occurred in the same geographical area.

RESULTS

The results also showed that single bicycle-involved crashes resulted in more serious outcomes when compared to multiparty crashes. For both single and multiparty bicycle crash categories, non-urban areas were associated with more serious injury outcomes. For the single crashes, wet surface condition, autumn and summer seasons, evening and night periods, non-adverse weather conditions, cyclists aged between 45 and 64 years, male sex, riding for the purpose of work or educational activities, and bicycles with light turned-off were associated with severe injuries. For the multiparty crashes, intersections, bicycle paths, non-winter season, not being employed or retired, lower personal car ownership, and race bicycles were directly related to severe injury consequences. Practical Applications: The findings of this study demonstrated that the best way to promote cycling safety is the combination of improving the design and maintenance of cycling facilities, encouraging safe cycling behavior, and intensifying enforcement efforts.

Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

In previous research, the effects of commercial vehicle proportions (CVP) on overall crash propensity have been found to be significant, but the results have been varied in terms of the effect direction. In addition, the mediating or moderating effects of roadway attributes on the CVP-vs-safety relationships, have not been investigated. In addressing this gap in the literature, this study integrates databases on crashes, traffic, and inventory for Hong Kong road segments spanning 2014-2017. The classes of commercial vehicles considered are public buses, taxi, and light-, medium- and heavy-goods vehicles. Random-parameter Tobit models were estimated using the crash rates. The results suggest that the CVP of each class show credible effects on the crash rates, for the various crash severity levels. The results also suggest that the interaction between CVP and roadway attributes is credible enough to mediate the effect of CVP on crash rates, and the magnitude and direction of such mediation varies across the vehicle classes, crash severity levels, and roadway attribute type in four ways. First, the increasing effect of taxi proportion on slight-injury crash rate is magnified at road segments with high intersection density. Second, the increasing effect of light-goods vehicle proportion on slight-injury crash rate is magnified at road segments with on-street parking. Third, the association between the medium- and heavy-goods vehicle proportion and killed/severe injury (KSI) crash rate, is moderated by the roadway width (number of traffic lanes). Finally, a higher proportion of medium- and heavy-goods vehicles generally contributes to increased KSI crash rate at road segments with high intersection density. Overall, the findings of this research are expected not only to help guide commercial vehicle enforcement strategy, licensing policy, and lane control measures, but also to review existing urban roadway designs to enhance safety.

Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fatalities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis' framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotemporal association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2-26, 27-57, 58-105, 106-192, and 193-364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2-26 m) class concentrated the most intense level of spatiotemporal association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions.

Role of exposure in bicycle safety analysis: Effect of cycle path choice

Despite the recognized environmental and health benefits of cycling, bicyclists are vulnerable to severe injuries and mortalities in the road crashes. Therefore, it is of paramount importance to identify the possible factors that may affect the bicycle crash risk. However, reliable estimates of bicycle exposure are often not available for the safety risk evaluation of different entities. The objective of this study is to advance the estimation of exposure in the bicycle safety analysis, using the detailed origin-destination data of each trip of the London public bicycle rental system. Two approaches including shortest path method (SPM) and weighted shortest path method (WSPM) are proposed to model the bicycle path choice and to estimate the bicycle distance traveled (BDT). Then, the bicycle crash frequency models that adopt BDTs as the exposure estimated using SPM and three WSPMs are developed. Three exposure measures including bicycle trips, bicycle time traveled (BTT), and BDT are assessed. Results indicate that the bicycle crash frequency models that incorporate the BDTs using WSPM have superior model fit. Moreover, the bicycle crash frequency model that incorporate the BDTs as the exposure outperforms those that incorporate the bicycle trips and BTT as the exposures. Findings of current study are indicative to the development of bicycle crash frequency model. Moreover, it should enhance the understanding on the roles of environmental, traffic and bicyclist factors in bicycle crash risk, based on appropriate estimates of bicycle exposures. Therefore, it should be useful to the transport planners and engineers for the development of bicycle infrastructures that can improve the overall bicycle safety in the long run.

Modeling Road Safety in Car-Dependent Cities: Case of Jeddah City, Saudi Arabia

Investigating the connections between pedestrian crashes and various urban variables is critical to ameliorate the prediction of pedestrian fatalities, formulate advisories for the stakeholders, and provide an evidence base for policy change to mitigate the occurrence and intensity of pedestrian fatalities. In this paper, we aim to explore the geographically varying association between the pedestrian fatalities and other associated factors of an urban environment in Jeddah city, which is a car-dependent city in Saudi Arabia. At first, Global Moran’s I and Local Indicators of Spatial Association (LISA) were applied to visualize the clustering of pedestrian fatalities in the various districts of Jeddah. Subsequently, we developed Poisson regression models based on their geographically weighted indicators. Both the global and geographically weighted regression models attempt to assess the association between the pedestrian fatalities and the geographically relevant land use and transport infrastructure factors. The results indicate that geographically weighted Poisson regression (GWPR) performed better than the global Poisson counterparts. It is also revealed that the existing transportation infrastructure in Jeddah was significantly associated with the higher pedestrian fatalities. The results have shown that the proposed model in this study can inform transport policies in Jeddah in prioritizing more safety measures for the pedestrians, including expanding pedestrians’ infrastructure, and cautious monitoring of pedestrian footpaths. It can facilitate the analysis and improvement of road safety for pedestrians in car-dependent cities.

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

Road safety is a major public health issue, with road crashes accounting for one-fourth of all documented injuries. In these crashes, pedestrians are more vulnerable to fatal and/or severe injuries than car occupants. Therefore, it is necessary to have a better understanding of the relationship between pedestrian crashes and possible influencing factors, including road environment, traffic conditions, and population characteristics. In conventional studies, separate prediction models were established for pedestrian crashes and other crash types, which could have ignored possible correlations among the different crash types. Additionally, these influencing factors can contribute to pedestrian crashes in two manners, i.e., contributing to crash occurrence and propensity of pedestrian involvement. Furthermore, extensive pedestrian count data were generally not available, affecting the estimation of pedestrian crash exposure. In this study, a joint probability model is adopted for the simultaneous modeling of crash occurrence and pedestrian involvement in crashes; effects of possible influencing factors, including land use, road networks, traffic flow, population demographics and socioeconomics, public transport facilities, and trip attraction attributes, are considered. Additionally, trip generation and pedestrian activity data, based on a comprehensive household travel survey, are used to determine pedestrian crash exposure. Markov chain Monte Carlo full Bayesian approach is then applied to estimate the parameters. Results indicate that crash occurrence is correlated to traffic flow, number of non-signalized intersections, and points of interest such as restaurants and hotels. By contrast, population age, ethnicity, education, household size, road density, and number of public transit stations could affect the propensity of pedestrian involvement in crashes. These findings indicate that better design and planning of built environments are necessary for safe and efficient access for pedestrians and for the long-term improvement of walkability in a high-density city such as Hong Kong.

Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes

BACKGROUND

Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods.

METHODS

Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared.

RESULTS

Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes.

CONCLUSIONS

The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.

A conceptual framework to understand the role of built environment on traffic safety

INTRODUCTION

Many U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics.

METHODS

Three years (2011-2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data.

RESULTS

Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less.

CONCLUSIONS

This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.

A spatial econometrics perspective on the characteristics of urban traffic accidents: focusing on elderly drivers' accidents in Seoul, South Korea

Due to the rapid ageing of the population, the number of traffic accidents involving elderly drivers has dramatically increased in Northeast Asia countries including South Korea. In order to ensure the mobility of elderly drivers and prevent the risk of accidents, it is necessary to consider various factors, which may affect elderly drivers while driving in urban areas. The primary goal of this study is to examine the characteristics of elderly drivers' traffic accidents in urban areas using spatial econometrics models. The study reveals that the highly populated areas (e.g. commercial areas, employment centres, and subway station catchment areas) have a higher risk of accidents involving elderly drivers. Also, due to an increase in cognitive response time and physical ageing of the elderly, the factors which represent complex driving condition for elderly drivers (e.g. traffic islands, intersections, and school zones) are found to be positively associated with the risk of accidents.

Roles of infrastructure and land use in bicycle crash exposure and frequency: A case study using Greater London bike sharing data

Cycling is increasingly promoted as a sustainable transport mode. However, bicyclists are more vulnerable to fatality and severe injury in road crashes, compared to vehicle occupants. It is necessary to identify the contributory factors to crashes and injuries involving bicyclists. For the prediction of motor vehicle crashes, comprehensive traffic count data, i.e. AADT and vehicle kilometer traveled (VKT), are commonly available to proxy the exposure. However, extensive bicycle count data are usually not available. In this study, revealed bicycle trip data of a public bicycle rental system in the Greater London is used to proxy the bicycle crash exposure. Random parameter negative binomial models are developed to measure the relationship between possible risk factors and bicycle crash frequency at the zonal level, based on the crash data in the Greater London in 2012-2013. Results indicate that model taking the bicycle use time as the exposure measure is superior to the other counterparts with the lowest AIC (Akaike information criterion) and BIC (Bayesian information criterion). Bicycle crash frequency is positively correlated to road density, commercial area, proportion of elderly, male and white race, and median household income. Additionally, separate bicycle crash prediction models are developed for different seasons. Effects of the presence of Cycle Superhighway and proportion of green area on bicycle crash frequency can vary across seasons. Findings of this study are indicative to the development of bicycle infrastructures, traffic management and control, and education and enforcement strategies that can enhance the safety awareness of bicyclists and reduce their crash risk in the long run.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

Cyclist-vehicle crash modeling with measurement error in traffic exposure

Exposure measures are always among the explanatory variables of any crash model. Regardless of the technique used to model crash, the mean crash frequency will increase with an increase in exposure since more crashes are likely to occur at higher exposure. For cyclist-vehicle crash models, bike and vehicle exposure measures are essential for an accurate and reliable estimate of the cyclist crash risk. However, traffic exposure measures are an example of variables that are measured with error. Generally, measurement error in regression estimates has three effects: 1) produce bias in parameter estimation for statistical models, 2) lead to a loss of explanation power, 3) mask important features of the data. This study proposes a full Bayesian Poisson Lognormal crash models that account for measurement error in traffic exposure measures (i.e., Vehicle Kilometers Travelled and Bike Kilometers Travelled). The underlying approach is to adjust the traffic exposure measures for measurement error to improve the accuracy of the crash model and crash model estimates. The full Bayesian models are developed using data for 134 traffic analysis zones (TAZs) in the city of Vancouver, Canada. The results show that Poisson Lognormal models that account for measurement error have a better fit for the modeled cyclist-vehicle crash data compared to traditional Poisson Lognormal models. The estimates of the Poisson Lognormal model that accounts for measurement error are consistent, with traditional Poisson Lognormal models' estimates except for the BKT and VKT estimates. Estimates of the BKT and VKT increased after introducing measurement error, which indicates an underestimation (downward bias) to BKT and VKT estimates in case of overlooking measurement error.

Effects of built environment on bicycle wrong Way riding behavior: A data-driven approach

Bicycle wrong way riding (WWR) is a dangerous and often neglected behavior that engenders threats to traffic safety. Owing to the lack of exposure data, the detection of WWR and its relationship with the built environment (BE) factors remain unclear. Accordingly, this study fills the research gaps by proposing a WWR detection framework based on bike-sharing trajectories collected from Chengdu, China. Moreover, this study adopts Negative Binomial-based Additive Decision Tree to investigate the impacts of built environment on WWR frequencies. Results reveal that (1) WWR distribution is unaffected by different periods in a day; (2) road length is more influential than road level and road direction in WWR occurrence; (3) company, bus stop, subway station, residence, and catering facility are primary contributors affecting WWR behavior during peak hours, whereas education becomes an emerging influential variable during nonpeak hours; and most importantly, (4) these variables clearly present non-linear effects on the WWR frequencies. Therefore, geographically differentiated policies should be adopted for bicycle safety improvement.

In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention

INTRODUCTION

A pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding crash causation patterns is important for clarifying the complicated growth of a pedestrian crash, which ultimately helps recommend corresponding countermeasures. However, previous studies lack an in-depth investigation of pedestrian crash cases, and are insufficient to propose a representative picture of causation patterns.

METHOD

In this study, pedestrian crash causation patterns were discerned by using the Driving Reliability and Error Analysis Method (DREAM). One hundred and forty-two pedestrian crashes were investigated, and five pedestrian pre-crash scenarios were extracted. Then, the crash causation patterns in each pre-crash scenario were analyzed; and finally, six distinct patterns were identified. Accordingly, 17 typical situations corresponding to these causation patterns were specified as well.

RESULTS

Among these patterns, the pattern related to distracted driving and the pattern related to an unexpected change of pedestrian trajectory contributed to a large portion of the total crashes (i.e., 27% and 24%, respectively). Other patterns also played an important role in inducing a pedestrian crash; these patterns include the pattern related to an obstructed line of sight caused by outside objects (9%), the pattern that involves reduced visibility (13%), and the pattern related to an improper estimation of the gap distance between the vehicle and the pedestrian (10%). The results further demonstrated the inter-heterogeneity of a crash causation pattern, as well as the intra-heterogeneity of pattern features between different pedestrian pre-crash scenarios. Conclusions and practical applications: Essentially, a crash causation pattern might involve different contributing factors by nature or dependent on specific scenarios. Finally, this study proposed suggestions for roadway facility design, roadway safety education and pedestrian crash prevention system development.

Not all protected bike lanes are the same: Infrastructure and risk of cyclist collisions and falls leading to emergency department visits in three U.S. cities

OBJECTIVE

Protected bike lanes separated from the roadway by physical barriers are relatively new in the United States. This study examined the risk of collisions or falls leading to emergency department visits associated with bicycle facilities (e.g., protected bike lanes, conventional bike lanes demarcated by painted lines, sharrows) and other roadway characteristics in three U.S. cities.

METHODS

We prospectively recruited 604 patients from emergency departments in Washington, DC; New York City; and Portland, Oregon during 2015-2017 who fell or crashed while cycling. We used a case-crossover design and conditional logistic regression to compare each fall or crash site with a randomly selected control location along the route leading to the incident. We validated the presence of site characteristics described by participants using Google Street View and city GIS inventories of bicycle facilities and other roadway features.

RESULTS

Compared with cycling on lanes of major roads without bicycle facilities, the risk of crashing or falling was lower on conventional bike lanes (adjusted OR = 0.53; 95 % CI = 0.33, 0.86) and local roads with (adjusted OR = 0.31; 95 % CI = 0.13, 0.75) or without bicycle facilities or traffic calming (adjusted OR = 0.39; 95 % CI = 0.23, 0.65). Protected bike lanes with heavy separation (tall, continuous barriers or grade and horizontal separation) were associated with lower risk (adjusted OR = 0.10; 95 % CI = 0.01, 0.95), but those with lighter separation (e.g., parked cars, posts, low curb) had similar risk to major roads when one way (adjusted OR = 1.19; 95 % CI = 0.46, 3.10) and higher risk when they were two way (adjusted OR = 11.38; 95 % CI = 1.40, 92.57); this risk increase was primarily driven by one lane in Washington. Risk increased in the presence of streetcar or train tracks relative to their absence (adjusted OR = 26.65; 95 % CI = 3.23, 220.17), on downhill relative to flat grades (adjusted OR = 1.92; 95 % CI = 1.38, 2.66), and when temporary features like construction or parked cars blocked the cyclist's path relative to when they did not (adjusted OR = 2.23; 95 % CI = 1.46, 3.39).

CONCLUSIONS

Certain bicycle facilities are safer for cyclists than riding on major roads. Protected bike lanes vary in how well they shield riders from crashes and falls. Heavier separation, less frequent intersections with roads and driveways, and less complexity appear to contribute to reduced risk in protected bike lanes. Future research should systematically examine the characteristics that reduce risk in protected lanes to guide design. Planners should minimize conflict points when choosing where to place protected bike lanes and should implement countermeasures to increase visibility at these locations when they are unavoidable.