Evaluating pedestrian crashes in areas with high low-income or minority populations

Patterns and sources of spatial inequity in freight crashes: An application of decomposition analysis

Despite considerable increases in road freight traffic and associated crashes over the past decade, our understanding of their spatial distribution remains limited. This is concerning because freight vehicle crashes often lead to fatal and severe injuries. This study focuses on Seoul, South Korea and contributes to the literature by investigating the patterns and sources of spatial inequity in freight crashes. Specifically, it examines whether socioeconomically disadvantaged neighborhoods experience a higher concentration of freight crashes. Using the Gelbach's decomposition technique, this study also aims to identify the factors contributing to differences in freight crashes between disadvantaged and less-disadvantaged neighborhoods and quantify their relative contributions. The regression results show that severe freight crashes are more prevalent in disadvantaged neighborhoods before adjusting for other factors-a pattern not observed in non-severe crashes. The decomposition analysis reveals that the observed disparities in severe freight crashes between disadvantaged and less-disadvantaged neighborhoods are fully explained by differences in several neighborhood characteristics, including local road density, truck traffic volume density, proximity to logistics terminals, and off-road bicycle lane density, between neighborhood types. Interestingly, differences in built environment characteristics between neighborhood types not only fail to explain but instead counteract the disparities in severe freight crashes. The findings of this study suggest detailed policy implications for mitigating freight crash occurrences and addressing related spatial inequities.

Major Traffic Safety Reform and Road Traffic Injuries Among Low-Income New York Residents, 2009-2021

Objectives. To evaluate the effects of a comprehensive traffic safety policy-New York City's (NYC's) 2014 Vision Zero-on the health of Medicaid enrollees. Methods. We conducted difference-in-differences analyses using individual-level New York Medicaid data to measure traffic injuries and expenditures from 2009 to 2021, comparing NYC to surrounding counties without traffic reforms (n = 65 585 568 person-years). Results. After Vision Zero, injury rates among NYC Medicaid enrollees diverged from those of surrounding counties, with a net impact of 77.5 fewer injuries per 100 000 person-years annually (95% confidence interval = -97.4, -57.6). We observed marked reductions in severe injuries (brain injury, hospitalizations) and savings of $90.8 million in Medicaid expenditures over the first 5 years. Effects were largest among Black residents. Impacts were reversed during the COVID-19 period. Conclusions. Vision Zero resulted in substantial protection for socioeconomically disadvantaged populations known to face heightened risk of injury, but the policy's effectiveness decreased during the pandemic period. Public Health Implications. Many cities have recently launched Vision Zero policies and others plan to do so. This research adds to the evidence on how and in what circumstances comprehensive traffic policies protect public health. (Am J Public Health. 2024;114(6):633-641. https://doi.org/10.2105/AJPH.2024.307617).

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.

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.

Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas

Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC). We performed spatial autocorrelation analyses to identify the clusters of high-risk and high-vulnerable census tracts in Texas. Meanwhile, we innovatively applied the Multiscale Geographically Weighted Regression model (MGWR) to assess the impacts of CDC SVI on crash risks spatially and statistically. The results demonstrate that the crash rate and the social vulnerability are significantly correlated in the highly urbanized regions as well as the southern border along the Rio Grande in Texas. The MGWR results indicate the minority status of census tracts is strongly correlated with overall crashes in north-central and northeastern Texas, and the socioeconomic status is tightly correlated with fatal crashes across Texas. The outcomes from this study have significant implications for transportation planning and policymaking.

Association of human mobility with road crashes for pandemic-ready safer mobility: A New York City case study

BACKGROUND

The COVID-19 pandemic has reshaped our cities in many ways. The number of motor vehicles on the road has plummeted during lockdowns, and an increasing number of people are turning to walking and biking. From a road safety perspective, the overall question is what effects the human behavior shift brings on the crash occurrence and, more importantly, how to support decision-makers on safer mobility policies?

METHOD

Based on anonymous mobile phone location and crash report data in New York City, this study attempts to provide some new insights by using survival analysis (the hazard function approach) to explore the effects of human mobility changes due to the pandemic on crashes that involve injuries and fatalities (of pedestrian, cyclist or motorist).

RESULTS

(1) the increased percentage of people staying at home improves pedestrian and cyclist safety, which adds evidence for making walking and cycling more appealing; (2) the increased percentage of people staying at home raises the likelihood of injuries for motor vehicle drivers, suggesting that it will be critical to monitor the driving behavior and establish new speed limits during the future pandemic waves and in the post-pandemic era as well; (3) non-work trips (e.g., shopping, recreation, personal business, etc.) are positively associated with crash injuries for motor vehicle drivers as well as pedestrian and cyclist; (4) human mobility factors were found not related to crash fatalities; (5) control NPIs implemented increased the motor vehicle drivers' crash risk.

Malignant mixes: The overlap of motor vehicle crashes and crime in Stockholm, Sweden

Places that concentrate both motor vehicle crashes and crime in Stockholm, Sweden were examined for common socio-economic, land use, and neighborhood characteristics. Using vehicle crash (N = 3,700) and non-traffic crime (N = 605,052) data from 2016 to 2018, hot spots of these two sets of events and their overlap were identified. Crash hot spots captured 14% of the crashes in only 0.5% of Stockholm's area while crime hot spots captured 27% of the recorded offences in less than 1% of the area. There was overlap in these hot spots for 7% of the crashes and 10% of the crimes. To model predictors, the events were allocated to roadway segments (N = 5511) and tested using a Poisson-Gamma-CAR spatial regression model. Both crashes and crimes exhibit a clear center-periphery pattern that varies over time and by type of crashes and crimes. Crashes tended to occur on roadways with higher average daily traffic (ADT) while crimes tend to occur on roadways with lower ADT with around half occurring on residential streets. Both types of incidents tended to be higher in lower income neighborhoods. Land uses common to both types of harm were the location of underground stations, ATM machines, and alcohol-serving businesses. These are places where people and cars converge at particular times. The effect of these events on police, emergency, and medical services is discussed.

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.

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.

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.

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.

Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea

Pedestrian-vehicle crashes can result in serious injury to pedestrians, who are exposed to danger when in close proximity to moving vehicles. Furthermore, these injuries can be considerably serious and even lead to death in a manner that varies depending on the pedestrian's age. This is because the pedestrian's physical characteristics and behaviors, particularly in relation to roads with moving vehicles, differ depending on the pedestrian's age. This study examines the determinants of pedestrian injury severity by pedestrian age using binary logistic regression. Factors in the built environment, such as road characteristics and land use of the places where pedestrian crashes occurred, were considered, as were the accident characteristics of the pedestrians and drivers. The analysis determined that the accident characteristics of drivers and pedestrians are more influential in pedestrian-vehicle crashes than the factors of the built environmental characteristics. However, there are substantial differences in injury severity relative to the pedestrian's age. Young pedestrians (aged under 20 years old) are more likely to suffer serious injury in school zones; however, no association between silver zones and injury severity is found for elderly pedestrians. For people in the age range of 20-39 years old, the severity of pedestrian injuries is lower in areas with more crosswalks and speed cameras. People in the age range of 40-64 years old are more likely to be injured in areas with more neighborhood streets and industrial land use. Elderly pedestrians are likely to suffer fatal injuries in areas with more traffic signals. This study finds that there are differences in the factors of pedestrian injury severity according to the age of pedestrians. Therefore, it is suggested that concrete and efficient policies related to pedestrian age are required to improve pedestrian safety and reduce pedestrian-vehicle crashes.

The effects of Chile's 2005 traffic law reform and in-country socioeconomic differences on road traffic deaths among children aged 0-14 years: A 12-year interrupted time series analysis

OBJECTIVES

This study assessed the effect of Chile's 2005 traffic law reform (TLR) on the rates of road traffic deaths (RTD) in children aged 0-14 years, adjusting for socioeconomic differences among the regions of the country.

METHODS

Free-access sources of official and national information provided the data for every year of the study period (2002-2013) and for each of the country's 13 upper administrative divisions with respect to RTD in child pedestrians and RTD in child passengers (dependent variables), and the following control variables: the number of road traffic tickets processed, investment in road infrastructure, poverty, income inequality, insufficient education, unemployment, population aged 0-14 years, and prevalence of alcohol consumption in the general population. Interrupted time series analyses (level and slope change impact model), using generalized estimating equation methods, were conducted to assess the impact of the TLR (independent variable) on the dependents variables.

RESULTS

There was a significant interaction between time and Chile's 2005 TLR for a reduction in child pedestrians (incidence rate ratio [IRR] 0.87, 95% confidence interval [CI] 0.79-0.96) and passengers RTD (IRR for interaction 0.80, 95% CI 0.67-0.96) trends. In addition, in child pedestrians, RTD rates were affected by poverty (IRR 1.04, 95% CI 1.02-1.05), income inequality (IRR 1.02, 95% CI 1.00-1.04), and unemployment (IRR 0.94, 95% CI 0.90-0.98), whereas in the case of child passengers, poverty (IRR 1.05, 95% CI 1.01-1.08) and income inequality (IRR 0.93, 95% CI 0.91-0.95) were significant.

CONCLUSIONS

Large-scale legislative actions can be effective road safety measures if they are aimed at promoting behavioral change in developing countries, improving the safety of children on the road. Additionally, regional socioeconomic differences are associated with higher RTD rates in this population, making this an argument in favor of road safety policies that consider these inequalities. The number of road traffic tickets processed and the investment in road infrastructure were not significant.

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

A review of spatial approaches in road safety

Spatial analyses of crashes have been adopted in road safety for decades in order to determine how crashes are affected by neighboring locations, how the influence of parameters varies spatially and which locations warrant interventions more urgently. The aim of the present research is to critically review the existing literature on different spatial approaches through which researchers handle the dimension of space in its various aspects in their studies and analyses. Specifically, the use of different areal unit levels in spatial road safety studies is investigated, different modelling approaches are discussed, and the corresponding study design characteristics are summarized in respective tables including traffic, road environment and area parameters and spatial aggregation approaches. Developments in famous issues in spatial analysis such as the boundary problem, the modifiable areal unit problem and spatial proximity structures are also discussed. Studies focusing on spatially analyzing vulnerable road users are reviewed as well. Regarding spatial models, the application, advantages and disadvantages of various functional/econometric approaches, Bayesian models and machine learning methods are discussed. Based on the reviewed studies, present challenges and future research directions are determined.

Safe mobility, socioeconomic inequalities, and aging: A 12-year multilevel interrupted time-series analysis of road traffic death rates in a Latin American country

As the resources for road safety in developing countries are scarce and unevenly distributed, vulnerable road users -such as the elderly- may be particularly at risk of road traffic deaths. To date, the impact of road safety measures over the rate of road traffic deaths in older adults (60 years or older), considering the within-country socioeconomic inequalities, has not been explored in developing nations. This study takes the Chilean case as an example -with its 2005 traffic law reform as one of the road safety measures investigated-, in which open data available from official national sources for all its 13 regions over the 2002-2013 period were used for a multilevel interrupted time-series analysis. A statistically significant secular reduction of the rates of road traffic deaths in the elderly population was found (incidence rate ratio [IRR] 0.95, 95% confidence interval [CI] 0.91 to 0.99), but no evidence for a significant intercept or slope change after the traffic law reform was observed. Regions with the highest number of traffic offenses prosecuted in local police courts had lower rates of road traffic deaths in older adults (IRR 0.95, 95% CI 0.90 to 1.00), and those regions in the third (IRR 1.61, 95% CI 1.16 to 2.25) and the fifth (IRR 1.66, 95% CI 1.08 to 2.54) quintiles of socioeconomic deprivation had higher rates of road traffic deaths in the elderly. Such findings strongly support the conceptualization of the road safety of seniors in developing countries as a social equity issue, with implications for the design of traffic regulations and road environments.

Using crowd sourcing to locate and characterize conflicts for vulnerable modes

Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-motor vehicle and other similar conflicts types may define a better performance measure for safety assessment. In the field of transportation safety, an absolute conflict occurs when two parties' paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver is required may also impact public perceptions of safety especially for vulnerable modes. Most of the existing literature focuses on vehicle conflicts. While in the past several years, more research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. A comprehensive analysis of conflicts appears critical. The major objective of this study is two fold: 1) Development of an innovative and cost effective conflict data collection technique to better understand the conflicts (and their severity) involving vulnerable road users (e.g. bicycle/pedestrian, bicycle/motor vehicle, and pedestrian/motor vehicle) and their severity. 2) Test the effectiveness and practicality of the approach taken and its associated crowd sourced data collection. In an endeavor to undertake these objectives, the researchers developed an android-based crowd-sourced data collection app. The crowd-source data collected using the app is compared with traditional fatality data for hot spot analysis. At the end, the app users provide feedback about the overall competency of the app interface and the performance of its features to the app developers. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, encouragement, enforcement, programs, policies, and infrastructure design and planning.

Exposure to pedestrian crash based on household survey data: Effect of trip purpose

Pedestrian are vulnerable to severe injury and mortality in the road crashes. Understanding the essence of the pedestrian crash is important to the development of effective safety countermeasures and improvement of social well-being. It is necessary to measure the exposure for the quantification of pedestrian crash risk. The primary goals of this study are to explore the efficient exposure measure for pedestrian crash, and identify the possible factors contributing to the incidence of pedestrian crash. In this study, amount of travel was estimated based on the Travel Characteristic Survey (TCS) data in 2011, and the crash data were obtained from the Transport Information System (TIS) of the Hong Kong Transport Department during the period from 2011 to 2015. Total population, walking frequency and walking time were adopted to represent the pedestrian exposure to road crash. The effect of trip purpose on pedestrian crash was evaluated by disaggregating the pedestrian exposure proxies by purpose. Three random-parameter negative binomial regression models were developed to compare the performances of the three pedestrian exposure proxies. It was found that the model in which walking frequency was used as the exposure proxy provided the best goodness-of-fit. Frequency of walking back home, among other trip purposes, was the most sensitive to the increase in pedestrian crash risk. Additionally, increase in the frequency of pedestrian crash was correlated to the increases in the proportions of children and elderly people. Furthermore, household size, median household income, road density, number of non-signalized intersection as well as number of zebra crossings also significantly affected the pedestrian crash frequency. Findings of this study should be indicative to the development and implementation of effective traffic control and management measures that can improve the pedestrian safety in the long run.

Spatial analysis of driving accidents leading to deaths related to motorcyclists in Tehran

PURPOSE

Traffic accidents are one of the main causes of death and disability, causing annual deaths of 1.23 million and tens of millions injured people worldwide. Meanwhile, a significant proportion of the deaths and injuries caused by traffic accidents occur among motorcyclists. According to the world health organization's 2015 report, about 25% of deaths from traffic accidents occur in motorists. In Iran, a significant proportion of deaths and injuries result from traffic accidents among motorcyclists, especially in passages within the cities. According to traffic police, about 25% of deaths and 50% of injuries in traffic accidents of Tehran are reported among motorcyclists. Therefore, due to the importance of this issue, the spatial factors influencing the incidence of motorcycle-related accidents in Tehran were investigated using the geographic information system.

METHODS

The present work was a cross-sectional and descriptive analysis study. The data necessary for the study were extracted from Tehran traffic police as well as municipality databases. Zoning maps were used to display the distribution of events. In the analytical investigation, Moran index was used to determine the distribution pattern of the events, while Getis-Ord G * statistics were applied to analyze hot spots. To investigate the role of regional and environmental factors in the frequency of traffic accidents related to motorcyclists in geographic units (Tehran 22 districts), Poisson regression and negative binomial models were used. The geographically weighted regression (GWR) model was used to analyze the relationship between environmental factors and the location of these events. Statistical analyses were performed using SPSS, STATA, ARC-GIS and GWR software.

RESULTS

The southern and eastern margins of Tehran are the most vulnerable areas in terms of deaths related to traffic accidents of motorcyclists. Highways are considered the location of most traffic accidents which lead to death of motorcyclists. Getis-Ord General G * (p < 0.04) indicates that the distribution of high-risk points is statistically significant. The final model showed that in Tehran, the association of different variables including demographic characteristics, pathways network and type of land use with the number of accidents in geographic units was statistically significant. The spatial distribution of traffic accidents leading to deaths of motorcyclists in the center of Tehran varies considerably with changes in population density, length of highways, volume of traffic, and land use in different parts.

CONCLUSION

Most of the traffic accidents leading to deaths of motorcyclists occur in highways. Various environmental variables play a role in determining the distribution pattern of these types of events. Through proper traffic management, controlling environmental risk factors and training people the safety of motorcyclists in Tehran can be improved.

A multivariate spatial approach to model crash counts by injury severity

Conventional safety models rely on the assumption of independence of crash data, which is frequently violated. This study develops a novel multivariate conditional autoregressive (MVCAR) model to account for the spatial autocorrelation of neighboring sites and the inherent correlation across different crash types. Manhattan, which is the most densely populated urban area of New York City, is used as the study area. Census tracts are used as the basic geographic units to capture crash, transportation, land use, and demo-economic data. The specification of the proposed multivariate model allows for jointly modeling counts of various crash types that are classified according to injury severity. Results of Moran's I tests show the ability of the MVCAR model to capture the multivariate spatial autocorrelation among different crash types. The MVCAR model is found to outperform the others by presenting the lowest deviance information criterion (DIC) value. It is also found that the unobserved heterogeneity was mostly attributed to spatial factors instead of non-spatial ones and there is a strong shared geographical pattern of risk among different crash types.

The effects of roadway and built environment characteristics on pedestrian fatality risk: A national assessment at the neighborhood scale

Characteristics of the transportation system and built environment contribute to pedestrian fatality risks, including vehicular traffic and land-use characteristics associated with higher pedestrian activity. We combined data from FHWA, NHTSA, EPA, and the Census Bureau and performed regression modeling to explore associations between transportation system and built environment characteristics and pedestrian fatalities between 2012 and 2016 at the Census tract scale across the United States. In urban tracts, we found especially strong associations between traffic on non-access-controlled principal arterial and minor arterial roadways and pedestrian fatalities (0.91 and 0.68 additional annual pedestrian fatalities per 100,000 persons per 10,000 VMT/mi² increase in traffic density, respectively). In both urban and rural tracts, we also found strong associations between employment density in the retail sector and pedestrian fatalities. Finally, we compared our model to the High Injury Network in Los Angeles, CA. Nearly half (43%) of observed fatalities were identified by both methods, while some fatalities were identified by only one (19% by our model and 23% by the High Injury Network). This work shows that traffic on certain roadway facility types and employment in certain sectors have especially strong associations with pedestrian fatality risk. More broadly, we illustrate how leveraging cross-disciplinary data in novel ways can support prospective, risk-based assessments of pedestrian fatality risks and support integrated and systemic approaches to transportation safety.

Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure

This study intended to identify the potential factors contributing to the occurrence of pedestrian crashes at signalized intersections in a densely populated city, based on a comprehensive dataset of 898 pedestrian crashes at 262 signalized intersections during 2010-2012 in Hong Kong. The detailed geometric design, traffic characteristics, signal control, built environment, along with the vehicle and pedestrian volumes were elaborately collected. A Bayesian measurement errors model was introduced as an alternative method to explicitly account for the uncertainties in volume data. To highlight the role played by exposure, models with and without pedestrian volume were estimated and compared. The results indicated that the omission of pedestrian volume in pedestrian crash frequency models would lead to reduced goodness-of-fit, biased parameter estimates, and incorrect inferences. Our empirical analysis demonstrated the existence of moderate uncertainties in pedestrian and vehicle volumes. Six variables were found to have a significant association with the number of pedestrian crashes at signalized intersections. The number of crossing pedestrians, the number of passing vehicles, the presence of curb parking, and the presence of ground-floor shops were positively related with pedestrian crash frequency, whereas the presence of playgrounds near intersections had a negative effect on pedestrian crash occurrences. Specifically, the presence of exclusive pedestrian signals for all crosswalks was found to significantly reduce the risk of pedestrian crashes by 43%. The present study is expected to shed more light on a deeper understanding of the environmental determinants of pedestrian crashes.

Local vs. national: Epidemiology of pedestrian injury in a mid-Atlantic city

OBJECTIVE

Understanding pedestrian injury trends at the local level is essential for program planning and allocation of funds for urban planning and improvement. Because we hypothesize that local injury trends differ from national trends in significant and meaningful ways, we investigated citywide pedestrian injury trends to assess injury risk among nationally identified risk groups, as well as identify risk groups and locations specific to Baltimore City.

METHODS

Pedestrian injury data, obtained from the Baltimore City Fire Department, were gathered through emergency medical services (EMS) records collected from January 1 to December 31, 2014. Locations of pedestrian injuries were geocoded and mapped. Pearson's chi-square test of independence was used to investigate differences in injury severity level across risk groups. Pedestrian injury rates by age group, gender, and race were compared to national rates.

RESULTS

A total of 699 pedestrians were involved in motor vehicle crashes in 2014-an average of 2 EMS transports each day. The distribution of injuries throughout the city did not coincide with population or income distributions, indicating that there was not a consistent correlation between areas of concentrated population or concentrated poverty and areas of concentrated pedestrian injury. Twenty percent (n = 138) of all injuries occurred among children age ≤14, and 22% (n = 73) of severe injuries occurred among young children. The rate of injury in this age group was 5 times the national rate (Incident Rate Ratio [IRR] = 4.81, 95% confidence interval [CI], [4.05, 5.71]). Injury rates for adults ≥65 were less than the national average.

CONCLUSIONS

As the urban landscape and associated pedestrian behavior transform, continued investigation of local pedestrian injury trends and evolving public health prevention strategies is necessary to ensure pedestrian safety.

RETRACTED ARTICLE: Effective environmental factors on geographical distribution of traffic accidents on pedestrians, downtown of Tehran City

The following article has been retracted from publication in the Taylor & Francis journalInternational Journal of Injury Control and Safety Promotion:Ali Moradi, 'Effective environmental factors on geographical distribution of traffic accidents on pedestrians, downtown of Tehran City', International Journal of Injury Control and Safety Promotion, https://doi.org/10.1080/17457300.2018.1431933. Version of Record published online 20 February 2018.This article has already been published in the International Journal of Critical Illness and Injury Science.

Neighborhood Influences on Vehicle-Pedestrian Crash Severity

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

Evaluating the impact of connectivity, continuity, and topography of sidewalk network on pedestrian safety

With the increasing demand for sustainability, walking is being encouraged as one of the main active modes of transportation. However, pedestrians are vulnerable to severe injuries when involved in crashes which can discourage road users from walking. Therefore, studying factors that affect the safety of pedestrians is important. This paper investigates the relationship between pedestrian-motorist crashes and various sidewalk network indicators in the city of Vancouver. The goal is to assess the impact of network connectivity, directness, and topography on pedestrian safety using macro-level collision prediction models. The models were developed using generalized linear regression and full Bayesian techniques. Both walking trips and vehicle kilometers travelled were used as the main traffic exposure variables in the models. The safety models supported the safety in numbers hypothesis showing a non-linear positive association between pedestrian-motorist crashes and the increase in walking trips and vehicle traffic. The model results also suggested that higher continuity, linearity, coverage, and slope of sidewalk networks were associated with lower crash occurrence. However, network connectivity was associated with higher crash occurrence. The spatial effects were accounted for in the full Bayes models and were found significant. The models provide insights about the factors that influence pedestrian safety and the spatial variability of pedestrian crashes within a city, which can be useful for the planning of pedestrian networks.

Comparison of US metropolitan region pedestrian and bicyclist fatality rates

Annual US pedestrian and bicyclist fatalities involving motor vehicles have each increased by 30% in just six years, reaching their highest levels in two decades. To provide information to reverse this trend, we quantified pedestrian and bicyclist fatality rates in 46 of the largest US metropolitan statistical areas (MSAs) during two five-year time periods: 1999-2003 and 2007-2011. We divided the annual average number of pedestrian and bicyclist fatalities during 1999-2003 from the Fatality Analysis Reporting System by the annual estimates of pedestrian and bicycle trips, kilometers traveled, and minutes traveled from the 2001 National Household Travel Survey (NHTS) and the annual average number of fatalities from 2007 to 2011 by similar estimates from the 2009 NHTS. The five most dangerous regions for walking during 2007-2011 averaged 262 pedestrian fatalities per billion trips while the five safest averaged 49 pedestrian fatalities per billion trips. The five most dangerous regions for bicycling averaged 458 bicyclist fatalities per billion trips while the five safest averaged 75 bicyclist fatalities per billion trips. Random-effects meta-analysis identified eight metropolitan regions as outliers with low pedestrian fatality rates, six with high pedestrian fatality rates, one with a low bicyclist fatality rate, and five with high bicyclist fatality rates. MSAs with low pedestrian and bicycle fatality rates tended to have central cities recognized as Walk Friendly Communities and Bicycle Friendly Communities for investing in pedestrian and bicycle projects and programs. Random-effects meta-regression showed that certain socioeconomic characteristics and high pedestrian and bicyclist mode shares were associated with lower MSA fatality rates. Results suggest that pedestrian and bicycle infrastructure and safety programs should be complemented with strategies to increase walking and bicycling. In particular, safety initiatives should be honed to reduce pedestrian and bicyclist fatality risk in immigrant communities and to make pedestrian travel safer for the growing senior-age population.

Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots

This study aims to explore the potential of using big data in advancing the pedestrian risk analysis including the investigation of contributing factors and the hotspot identification. Massive amounts of data of Manhattan from a variety of sources were collected, integrated, and processed, including taxi trips, subway turnstile counts, traffic volumes, road network, land use, sociodemographic, and social media data. The whole study area was uniformly split into grid cells as the basic geographical units of analysis. The cell-structured framework makes it easy to incorporate rich and diversified data into risk analysis. The cost of each crash, weighted by injury severity, was assigned to the cells based on the relative distance to the crash site using a kernel density function. A tobit model was developed to relate grid-cell-specific contributing factors to crash costs that are left-censored at zero. The potential for safety improvement (PSI) that could be obtained by using the actual crash cost minus the cost of "similar" sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots. The proposed hotspot identification method takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. Big data, on the one hand, enable more precise estimation of the effects of risk factors by providing richer data for modeling, and on the other hand, enable large-scale hotspot identification with higher resolution than conventional methods based on census tracts or traffic analysis zones.

Effective environmental factors on geographical distribution of traffic accidents on pedestrians, downtown Tehran city

Introduction:

In most countries, occurrence of traffic causalities is high in pedestrians. The aim of this study is to geographically analyze the traffic casualties in pedestrians in downtown Tehran city.

Methods:

The study population consisted of traffic injury accidents in pedestrians occurred during 2015 in Tehran city. Data were extracted from offices of traffic police and municipality. For analysis of environmental factors and site of accidents, ordinary least square regression models and geographically weighted regression were used. Fitness and performance of models were checked using the Akaike information criteria, Bayesian information criteria, deviance, and adjusted R².

Results:

Totally, 514 accidents were included in this study. Of them, site of accidents was arterial streets in 370 (71.9%) cases, collector streets in 133 cases (25.2%), and highways in 11 cases (2.1%). Geographical units of traffic accidents in pedestrians had statistically significant relationship with a number of bus stations, number of crossroads, and recreational areas.

Conclusion:

Distribution of injury traffic accidents in pedestrians is different in downtown Tehran city. Neighborhoods close to markets are considered as most dangerous neighborhoods for injury traffic accidents. Different environmental factors are involved in determining the distribution of these accidents. The health of pedestrians in Tehran city can be improved by proper traffic management, control of environmental factors, and educational programs.

Vehicle-Pedestrian crashes at Intersections in Dhaka city

Background:

Pedestrians are some of the most vulnerable road users, especially in large congested cities in developing countries. In order to develop appropriate countermeasures to improve safety, research has to be conducted to understand the factors contributing to vehicle-pedestrian collisions.

Objective:

This study aims to identify the factors contributing to intersection crashes in a developing country context.

Method:

A Poisson regression model was applied to police reported crash data from the capital of Bangladesh, Dhaka.

Results:

This study finds that an increase in vehicle traffic and the presence of police officer, footbridge, bus stop, solar panel and waste deposit facility were associated with an increase in the number of vehicle-pedestrian crashes, whereas an increase in pedestrian volume, roads with the same number of inbound and outbound lanes, roads with greater number of lanes, and the presence of traffic signal, commercial area or offices, speed breaker and rail crossing were associated with a reduction in the number of vehicle-pedestrian crashes.

Conclusion:

While the results of most traffic and engineering factors are consistent with those obtained in previous studies in developed countries, some of the results on human related factors and unusual road furniture are atypical and require more locally targeted countermeasures.

Spatial Factors Affecting the Frequency of Pedestrian Traffic Crashes: A Systematic Review

Context

Considering the importance of pedestrian traffic crashes and the role of environmental factors in the frequency of crashes, this paper aimed to review the published evidence and synthesize the results of related studies for the associations between environmental factors and distribution of pedestrian-vehicular traffic crashes.

Evidence Acquisition

We searched all epidemiological studies from 1966 to 2015 in electronic databases. We found 2,828 studies. Only 15 observational studies out of these studies met the inclusion criteria of the study. The quality of the included studies was assessed using the strengthening the reporting of observational studies in epidemiology (STROBE) checklist.

Results

A review of the studies showed significant correlations between a large number of spatial variables including student population and the number of schools, population density, traffic volume, roadway density, socio-economic status, number of intersections, and the pedestrian volume and the dependent variable of the frequency of pedestrian traffic crashes. In the studies, some spatial factors that play an important role in determining the frequency of pedestrian traffic crashes, such as facilities for increasing the pedestrians’ safety were ignored.

Conclusions

It is proposed that the needed research be conducted at national and regional levels in coordination and cooperation with international organizations active in the field of traffic crashes in various parts of the world, especially in Asian, African and Latin American developing countries, where a greater proportion of pedestrian traffic crashes occur.

Examining racial bias as a potential factor in pedestrian crashes

INTRODUCTION

In the US people of color are disproportionately affected by pedestrian crashes. The purpose of this study was to examine the potential for racial bias in driver yielding behaviors at midblock crosswalks in low and high income neighborhoods located in the sprawling metropolitan area of Las Vegas, NV.

METHODS

Participants (1 white, 1 black female) crossed at a midblock crosswalk on a multilane road in a low income and a high income neighborhood. Trained observers recorded (1) number of cars that passed in the nearest lane before yielding while the pedestrian waited near the crosswalk at the curb (2) number of cars that passed through the crosswalk with the pedestrian in the same half of the roadway.

RESULTS

The first car in the nearest lane yielded to the pedestrian while they waited at the curb 51.5% of the time at the high income and 70.7% of the time at the low income crosswalk. Two way ANOVAs found an interaction effect between income and race on yielding behaviors. Simple effects for income revealed that at the high income crosswalk, drivers were less likely to yield to the white pedestrian while she waited at the curb (F(1,122)=11.18;p=0.001), and were less likely to yield to the black pedestrian while she was in the same half of the roadway at the high income crosswalk (F(1,124)=4.40;p=0.04). Simple effects for race showed significantly more cars passed through the crosswalk while the black pedestrian was in the roadway compared to the white pedestrian at the high income crosswalk (F(1,124)=6.62;p=0.01).

CONCLUSIONS

Bias in driver yielding behavior may be one influencing factor in higher rates of pedestrian crashes for people of color.

Macro-level safety analysis of pedestrian crashes in Shanghai, China

Pedestrian safety has become one of the most important issues in the field of traffic safety. This study aims at investigating the association between pedestrian crash frequency and various predictor variables including roadway, socio-economic, and land-use features. The relationships were modeled using the data from 263 Traffic Analysis Zones (TAZs) within the urban area of Shanghai - the largest city in China. Since spatial correlation exists among the zonal-level data, Bayesian Conditional Autoregressive (CAR) models with seven different spatial weight features (i.e. (a) 0-1 first order, adjacency-based, (b) common boundary-length-based, (c) geometric centroid-distance-based, (d) crash-weighted centroid-distance-based, (e) land use type, adjacency-based, (f) land use intensity, adjacency-based, and (g) geometric centroid-distance-order) were developed to characterize the spatial correlations among TAZs. Model results indicated that the geometric centroid-distance-order spatial weight feature, which was introduced in macro-level safety analysis for the first time, outperformed all the other spatial weight features. Population was used as the surrogate for pedestrian exposure, and had a positive effect on pedestrian crashes. Other significant factors included length of major arterials, length of minor arterials, road density, average intersection spacing, percentage of 3-legged intersections, and area of TAZ. Pedestrian crashes were higher in TAZs with medium land use intensity than in TAZs with low and high land use intensity. Thus, higher priority should be given to TAZs with medium land use intensity to improve pedestrian safety. Overall, these findings can help transportation planners and managers understand the characteristics of pedestrian crashes and improve pedestrian safety.

Cross-sectional study of road accidents and related law enforcement efficiency for 10 countries: A gap coherence analysis

OBJECTIVE

Road crashes are considered as the eighth leading causes of death. There is a wide disparity in crash severity and law enforcement efficiency among low-, medium-, and high-income countries. It would be helpful to review the crash severity trends in these countries, identify the vulnerable road users, and understand the law enforcement effectiveness in devising efficient road safety improvement strategies.

METHOD

The crash severity, fatality rate among various age groups, and law enforcement strategies of 10 countries representing low-income (i.e., India and Morocco), medium-income (i.e. Argentina, South Korea, and Greece), and high-income (i.e., Australia, Canada, France, the UK, and the United States) are studied and compared for a period of 5 years (i.e., 2008 to 2012). The critical parameters affecting road safety are identified and correlated with education, culture, and basic compliance with traffic safety laws. In the process, possible road safety improvement strategies are identified for low-income countries.

RESULTS

The number of registered vehicles shows an increasing trend for low-income countries as do the crash rate and crash severity. Compliance related to seat belt and helmet laws is high in high-income countries. In addition, recent seat belt- and helmet-related safety programs in middle-income countries helped to curb fatalities. Noncompliance with safety laws in low-income countries is attributed to education, culture, and inefficient law enforcement.

CONCLUSION

Efficient law enforcement and effective safety education taking into account cultural diversity are the key aspects to reduce traffic-related injuries and fatalities in low-income countries like India.

Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto

The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety - a prerequisite to promoting a culture of active transportation.

Land use and traffic collisions: A link-attribute analysis using Empirical Bayes method

Road traffic collisions represent one of the major public health problems among the leading causes of deaths globally. This paper examines several approaches in detecting hazardous road locations, and discusses the spatial distribution of these locations as well as their relationships with different land uses in Hong Kong. Two most commonly used methodologies in detecting hazardous road locations are used: the hot spot and hot zone methodologies. Both methodologies are performed using raw collision count, excess collision count and Empirical Bayes (EB) estimations. The EB estimation uses land use characteristics near the road network in defining the reference groups. Finally all the approaches are compared by a test to assess their stability. The results show that for different hazardous road location detection methodologies, the best fit estimation methods on sites are different. The results confirm some land use impacts in previous studies, and suggest some further patterns on road safety. The findings are useful in understanding the complex interrelationships between land use and road safety, and in facilitating planners and policy makers to build safer cities.

Macroscopic modeling of pedestrian and bicycle crashes: A cross-comparison of estimation methods

The paper presents a cross-comparison of different estimation methods to model pedestrian and bicycle crashes. The study contributes to macro level safety studies by providing further methodological and empirical evidence on the various factors that influence the frequency of pedestrian and bicycle crashes at the planning level. Random parameter negative binomial (RPNB) models are estimated to explore the effects of various planning factors associated with total, serious injury and minor injury crashes while accounting for unobserved heterogeneity. Results of the RPNB models were compared with the results of a non-spatial negative binomial (NB) model and a Poisson-Gamma-CAR model. Key findings are, (1) the RPNB model performed best with the lowest mean absolute deviation, mean squared predicted error and Akaiki information criterion measures and (2) signs of estimated parameters are consistent if these variables are significant in models with the same response variables. We found that vehicle kilometers traveled (VKT), population, percentage of commuters cycling or walking to work, and percentage of households without motor vehicles have a significant and positive correlation with the number of pedestrian and bicycle crashes. Mixed land use is also found to have a positive association with the number of pedestrian and bicycle crashes. Results have planning and policy implications aimed at encouraging the use of sustainable modes of transportation while ensuring the safety of pedestrians and cyclist.

Spatial analysis of the association of alcohol outlets and alcohol-related pedestrian/bicyclist injuries in New York City

BACKGROUND

Pedestrian and bicyclist injury is an important public health issue. The retail environment, particularly the presence of alcohol outlets, may contribute the the risk of pedestrian or bicyclist injury, but this association is poorly understood.

METHODS

This study quantifies the spatial risk of alcohol-related pedestrian injury in New York City at the census tract level over a recent 10-year period using a Bayesian hierarchical spatial regression model with Integrated Nested Laplace approximations. The analysis measures local risk, and estimates the association between the presence of alcohol outlets in a census tract and alcohol-involved pedestrian/bicyclist injury after controlling for social, economic and traffic-related variables.

RESULTS

Holding all other covariates to zero and adjusting for both random and spatial variation, the presence of at least one alcohol outlet in a census tract increased the risk of a pedestrian or bicyclist being struck by a car by 47 % (IDR = 1.47, 95 % Credible Interval (CrI) 1.13, 1.91).

CONCLUSIONS

The presence of one or more alcohol outlets in a census tract in an urban environment increases the risk of bicyclist/pedestrian injury in important and meaningful ways. Identifying areas of increased risk due to alcohol allows the targeting of interventions to prevent and control alcohol-related pedestrian and bicyclist injuries.

Multilevel models for evaluating the risk of pedestrian-motor vehicle collisions at intersections and mid-blocks

Walking is a popular form of physical activity associated with clear health benefits. Promoting safe walking for pedestrians requires evaluating the risk of pedestrian-motor vehicle collisions at specific roadway locations in order to identify where road improvements and other interventions may be needed. The objective of this analysis was to estimate the risk of pedestrian collisions at intersections and mid-blocks in Seattle, WA. The study used 2007-2013 pedestrian-motor vehicle collision data from police reports and detailed characteristics of the microenvironment and macroenvironment at intersection and mid-block locations. The primary outcome was the number of pedestrian-motor vehicle collisions over time at each location (incident rate ratio [IRR] and 95% confidence interval [95% CI]). Multilevel mixed effects Poisson models accounted for correlation within and between locations and census blocks over time. Analysis accounted for pedestrian and vehicle activity (e.g., residential density and road classification). In the final multivariable model, intersections with 4 segments or 5 or more segments had higher pedestrian collision rates compared to mid-blocks. Non-residential roads had significantly higher rates than residential roads, with principal arterials having the highest collision rate. The pedestrian collision rate was higher by 9% per 10 feet of street width. Locations with traffic signals had twice the collision rate of locations without a signal and those with marked crosswalks also had a higher rate. Locations with a marked crosswalk also had higher risk of collision. Locations with a one-way road or those with signs encouraging motorists to cede the right-of-way to pedestrians had fewer pedestrian collisions. Collision rates were higher in locations that encourage greater pedestrian activity (more bus use, more fast food restaurants, higher employment, residential, and population densities). Locations with higher intersection density had a lower rate of collisions as did those in areas with higher residential property values. The novel spatiotemporal approach used that integrates road/crossing characteristics with surrounding neighborhood characteristics should help city agencies better identify high-risk locations for further study and analysis. Improving roads and making them safer for pedestrians achieves the public health goals of reducing pedestrian collisions and promoting physical activity.

Small-area spatiotemporal analysis of pedestrian and bicyclist injuries in New York City

BACKGROUND

This study quantifies the spatiotemporal risk of pedestrian and bicyclist injury in New York City at the census tract level over a recent 10-year period, identifies areas of increased risk, and evaluates the role of socioeconomic and traffic-related variables in injury risk.

METHODS

Crash data on 140,835 pedestrian and bicyclist injuries in 1908 census tracts from 2001 to 2010 were obtained from the New York City Department of Transportation. We analyzed injury counts within census tracts with Bayesian hierarchical spatial models using integrated nested Laplace approximations. The model included variables for social fragmentation, median household income, and average vehicle speed and traffic density, as well as a spatially unstructured random effect term, a spatially structured conditional autoregression term, a first-order random walk-correlated time variable, and an interaction term for time and place. Incidence density ratios, credible intervals, and probability exceedances were calculated and mapped.

RESULTS

The yearly rate of crashes involving injuries to "pedestrians" (including bicyclists) decreased 16.2% over the study period, from 23.7 per 10,000 population to 16.2 per 10,000. The temporal term in the spatiotemporal model indicated that much of the decrease over the study period occurred during the first 4 years of the study period. Despite an overall decrease, the model identified census tracts that were at persistently high risk of pedestrian injury throughout the study period, as well as areas that experienced sporadic annual increases in risk. Aggregate social, economic, and traffic-related measures were associated with pedestrian injury risk at the ecologic level. Every 1-unit increase in a standardized social fragmentation index was associated with a 19% increase in pedestrian injury risk (incidence density ratio = 1.19 [95% credible interval = 1.16 - 1.23]), and every 1 standardized unit increase in traffic density was associated with a 20% increase in pedestrian injury risk (1.20 [1.15 - 1.26]). Each 10-mile-per-hour increase in average traffic speed in a census tract was associated with a 24% decrease in pedestrian injury risk (0.76 [0.69 - 0.83]).

CONCLUSIONS

The risk of a pedestrian or bicyclist being struck by a motor vehicle in New York City decreased from 2001 to 2004 and held fairly steady thereafter. Some census tracts in the city did not benefit from overall reductions or experienced sporadic years of increased risk compared with the city as a whole. Injury risk at the census tract level was associated with social, economic, and traffic-related factors.

Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level

Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode.

Multi-level hot zone identification for pedestrian safety

According to the National Highway Traffic Safety Administration (NHTSA), while fatalities from traffic crashes have decreased, the proportion of pedestrian fatalities has steadily increased from 11% to 14% over the past decade. This study aims at identifying two zonal levels factors. The first is to identify hot zones at which pedestrian crashes occurs, while the second are zones where crash-involved pedestrians came from. Bayesian Poisson lognormal simultaneous equation spatial error model (BPLSESEM) was estimated and revealed significant factors for the two target variables. Then, PSIs (potential for safety improvements) were computed using the model. Subsequently, a novel hot zone identification method was suggested to combine both hot zones from where vulnerable pedestrians originated with hot zones where many pedestrian crashes occur. For the former zones, targeted safety education and awareness campaigns can be provided as countermeasures whereas area-wide engineering treatments and enforcement may be effective safety treatments for the latter ones. Thus, it is expected that practitioners are able to suggest appropriate safety treatments for pedestrian crashes using the method and results from this study.

Measures of activity-based pedestrian exposure to the risk of vehicle-pedestrian collisions: space-time path vs. potential path tree methods

Research on the extent to which pedestrians are exposed to road collision risk is important to the improvement of pedestrian safety. As precise geographical information is often difficult and costly to collect, this study proposes a potential path tree method derived from time geography concepts in measuring pedestrian exposure. With negative binomial regression (NBR) and geographically weighted Poisson regression (GWPR) models, the proposed probabilistic two-anchor-point potential path tree (PPT) approach (including the equal and weighted PPT methods) are compared with the deterministic space-time path (STP) method. The results indicate that both STP and PPT methods are useful tools in measuring pedestrian exposure. While the STP method can save much time, the PPT methods outperform the STP method in explaining the underlying vehicle-pedestrian collision pattern. Further research efforts are needed to investigate the influence of walking speed and route choice.