Problem, research strategy, and findingsPedestrian and bicyclist deaths now account for nearly one in five traffic fatalities in the United States. They continue to rise even as peer nations have reduced deaths dramatically. We examined 222 miles of arterial highways in Florida to understand the nature of the unique risk confronted by vulnerable road users in the United States. We found that land use decisions-particularly the siting of groceries, pharmacies, gas stations, and fast-food outlets-are strongly associated with the death and injury of vulnerable road users. These household-sustaining uses generate exposure in locations fundamentally incompatible with vulnerable road user safety, activating latent hazards embedded into infrastructure design. We discuss in this article how land use practice differs from that of Europe, and how these differences explain the differences in safety outcomes. We conclude by developing tools for estimating the magnitude of this risk during the project development process and by outlining strategies for enhancing land use and transportation practices to better account for the land use-road safety connection.Takeaway for practiceMeaningful progress toward Vision Zero in the United States requires acknowledging that land use decisions-not just street design-are a primary driver of the elevated risk faced by vulnerable road users. Development codes that encourage household-sustaining uses to locate along arterial corridors create predictable and preventable exposure in environments where safety is structurally unattainable without simultaneous changes to the street's design and function. Addressing this risk demands a shift in planning practice: revising zoning and site-planning standards to explicitly account for safety, redirecting high-risk uses away from arterial corridors, and applying analytic tools that identify and mitigate latent land use hazards during the planning and project development process.

Conventional traffic impact analysis (TIA) neglects traffic safety in development review procedures, often serving as an afterthought because the traffic generated is easier to measure than proactively addressing and predicting safety issues. To explore this TIA neglect issue further, interviews conducted with TIA practitioners and developers in five US Mid-Atlantic and Southeast states explore whether and how these professionals incorporated road user safety considerations into their TIA practices. While the means and ends of TIA are quantifiable, understanding the possible role that safety plays in the TIA process requires qualitative analysis. Drawing upon coded interview findings, literature on relationships among traffic congestion, safety, and induced travel demand, qualitative analysis using causal loop diagramming highlights the complex yet fluid interplay between traffic safety and conventional TIA practices through the lens of system archetypes. These practices adhered to two interrelated systems archetypes—generic representations of problem-causing structures that produce established patterns of behavior in a system— discerning potential points to integrate road user safety into TIA practice first as opposed to last in the TIA process. The results enhance TIA practice by explicitly incorporating safety, yet the overall approach is limited in its reliance on the qualitative articulations of TIA practitioners and developers about TIA procedures rather than quantitative analysis of TIA outputs and outcomes.

Lower-income and minority populations in the United States are at disproportionate risk of being injured or killed while walking. This review synthesizes the literature to understand the magnitude of this risk, as well as the underlying factors that may best explain it. On average, lower-income areas experience 3 times the number of per capita pedestrian fatalities as affluent areas. With respect to race, Hispanic people are 1.6 times as likely to be killed as are White non-Hispanic people, while Black people are 1.7 times more likely to be killed, and Indigenous persons are fully 4 times as likely. Despite the consistency of these findings, none of the prevailing explanations, such as increased exposure or increased likelihood of walking under the influence, are supported by the literature. Instead, the primary difference pertains to trip purposes. Affluent households walk primarily for leisure and recreation. If an environment is perceived as being unpleasant or unsafe, they can shift the trip to another location or forego the trip entirely. Lower-income households, by contrast, walk principally for utilitarian reasons, making them less able to avoid unsafe environments. This paper concludes by discussing the need to better account for social vulnerability in planning and project development processes.

Conventional transportation practice attributes traffic crashes to human error, leading to the prevailing assumption that crash prevention is principally an outcome of driver education and law enforcement programs. But what if planning and urban design decisions induce human errors? In this study, we examine the literature in organizational systems safety, cognitive psychology, and behavioral economics to detail how cognitive interpretations of the built environment may produce the errors that result in traffic crashes. We proceed to examine crash incidence in Charlotte-Mecklenberg County in light of this cognitive framework and discuss its implications for research and practice.

This study examined the incidence of injurious and fatal pedestrian crashes for lower-income and affluent communities in Broward and Palm Beach counties, FL, finding notable differences in the environmental risk factors for these populations. In lower-income areas, pedestrian deaths and injuries increased with traffic volumes, multilane streets, and restaurants and shopping centers. They decreased with the presence of raised medians, which can serve as a refuge island for crossing pedestrians. These variables all suggest that, at least in lower-income areas, pedestrian death and injury was associated with difficulties safely accessing household-supporting destinations. For affluent areas, the factors associated with increased pedestrian death and injury were those relating to recreation and nightlife-specifically, bars and clubs, hotels, and restaurants. Neither traffic volumes nor multilane roads proved to be meaningfully related to increased pedestrian death or injury in affluent areas. Perhaps most notably, higher concentrations of Black populations were strongly related to increased pedestrian death and injury, even after accounting for differences in income. Considered as a whole, these results suggest that pedestrian crash risk, like much else in U.S. society, is strongly intertwined with broader issues of racial and income inequality. Attempts to address the safety of the transportation system's most vulnerable users need to move beyond asserting that any pedestrian project constitutes a safety enhancement, and to begin to more meaningfully account for social vulnerabilities associated with race and income.

Motorcycle crashes account for a significant proportion of traffic-related fatalities on U.S. roadways. Compared with motor vehicles, motorcycles traveling straight ahead are more susceptible to collisions with left-turning vehicles at intersections (note - in a system where traffic travels on the right-hand side of the road). The limited knowledge of the causes and influences of this specific type of crash deters efforts to improve motorcycle safety and is partly influenced by two issues. First, significant variables are unknown; second, motorcycles comprise a small proportion of vehicles in the traffic stream. This study sought to understand the factors that may contribute to the disproportionate crash risk left-turning vehicles pose for motorcyclists while accounting for the imbalance of vehicle proportions. Data containing motorcycle-motor vehicle and motor vehicle-motor vehicle crashes involving left-turning motor vehicles at intersections in South Florida were collected from 2015 to 2017. The study applied logistic regression on a balanced dataset generated using the random oversampling technique. The proposed model improved the predictive accuracy and enabled the identification of factors contributing to motorcycle crashes with left-turning vehicles. A Bayesian network analysis was also applied to the balanced data to analyze the interrelationship of factors associated with motorcycle crashes with left-turning vehicles. Results indicated that the type of intersection and traffic control, time of day, age of drivers, sex of the motorcyclist, roadway type, and weather were significantly associated with motorcyclists' susceptibility to collisions with left-turning vehicles. Recognizing these attributes could help devise engineering measures and policies for promoting motorcycle safety.

This paper investigates the safety in numbers effect in cycling by comparing monthly bicycle crash trends before and after public bikeshare's launch in Philadelphia and Pittsburgh, PA, from 2010 to 2019, and in Portland, OR, from 2014 to 2019. To estimate the bicycle crash trend in each city, the authors conduct time series analysis with a regression discontinuity design using generalized linear quasi-Poisson models. The entries of dock-based public bikeshare programs signal an increase in biking activity on the road and serve as the point of discontinuity in the analysis. For each city, the analysis models pre- and post-bikeshare bicycle crash trends inside and outside bikeshare program's service area separately to compare crash trends at different biking activity levels within the same local context. Results show that before bikeshare, crash trends inside and outside service areas had similar patterns in Philadelphia and Portland while moving in opposite directions in Pittsburgh. The post-bikeshare crash trends differed across cities for both inside and outside of the service areas. The finding that bicycle crash trends changed in different patterns across the three cities indicates that there may be no single safety in numbers effect on bicycle crashes. The inconsistent patterns of bicycle crash trends within and across cities suggest confounding factors underlying the change in bicycle crashes.

Lower-income areas experience an increased incidence of traffic crashes, injuries, and deaths than more affluent ones. Researchers tend to blame these outcomes on differences in car ownership, with lower-income households expected to use vulnerable active modes at higher rates, as well as being more likely to drive older vehicles with fewer safety features. While these explanations seem plausible enough, they likely fail to provide a complete picture of the problem. This study examines the role of the built environment in road safety outcomes for lower- and higher-income block groups in Orange County, Florida. It finds that the hazard posed by urban arterials is three times greater for lower-income environments than for more affluent communities. Sidewalk buffers, ordinarily regarded as a pedestrian amenity, were associated with crash increases in affluent areas but not lower-income ones. Areas with concentrations of black residents were found to be increased risk, even after accounting for differences in income. Considered as a whole, the risk factors for lower-income and high-income populations are not the same. This article examines the underlying reasons that lead to these outcomes and discusses the need to better account for the manner in which design may uniquely affect the safety of different demographic cohorts.

This paper presents a study that evaluates the nature of the associations (i.e., linear or non-linear) between built environment variables and pedestrian crash frequency at the census block group level. A machine learning approach, called the componentwise model-based gradient boosting algorithm, was implemented to estimate the nature and effects of sociodemographic, land use, road network, and traffic attributes on pedestrian crashes from Broward and Miami-Dade Counties in Florida. The algorithm provides the flexibility to use different types of base-learners, including but not limited to decision tree (DT), generalized additive model (GAM), and Markov Random Field (MRF). While gradient boosting with DT base-learner has widely been used in safety studies, other base-learners and their performances in crash frequency predictions are yet to be explored. This study compared the performance of DT and GAM base-learners, with an MRF base-learner to account for spatial correlation among analysis units. Models fitted with GAM base-learner were found to perform better than the models fitted with DT base-learner, with several variables showing non-linear and several showing linear or approximately linear correlations with pedestrian crash frequency. The study provides useful insights on how the results can help urban planners and policy makers to optimize pedestrian safety measures.

AbstractRoad safety has come a long way in our lifetimes, and there are steps in this progress that mark their place in history. Many of these were technical innovations, such as seat belts, electronic stability control, and geofencing for vehicle speed control. Also important, though perhaps fewer in number, were innovations in strategies to achieve change. These include the public health model of Dr. William Haddon, the introduction of Vision Zero, the World Report on Road Traffic Injury Prevention from WHO and the World Bank, and more recently, the Decade of Action 2011–2020. I am sure that the work and recommendations presented in this report will deserve their place in a “Hall of Fame” for strategic innovation in saving lives across the globe.

Driving errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence. In this study, we harness highly detailed pre-crash Naturalistic Driving Study (NDS) data 1) to understand errors and violations in crash, near-crash, and baseline (no event) driving situations, and 2) to explore pathways that lead to crashes in diverse built environments by applying rigorous modeling techniques. The “locality” factor in the NDS data provides information on various types of roadway and environmental surroundings that could influence traffic flow when a precipitating event is observed. Coded by the data reductionists, this variable is used to quantify the associations of diverse environments with crash outcomes both directly and indirectly through mediating driving errors and violations. While the most prevalent errors in crashes were recognition errors such as failing to recognize a situation (39 %) and decision errors such as not braking to avoid a hazard (34 %), performance errors such as poor lateral or longitudinal control or weak judgement (8 %) were most strongly correlated with crash occurrence. Path analysis uncovered direct and indirect relationships between key built-environment factors, errors and violations, and crash propensity. Possibly due to their complexity for drivers, urban environments are associated with higher chances of crashes (by 6.44 %). They can also induce more recognition errors which correlate with an even higher chances of crashes (by 2.16 % with the “total effect” amounting to 8.60 %). Similar statistically significant mediating contributions of recognition errors and decision errors near school zones, business or industrial areas, and moderate residential areas were also observed. From practical applications standpoint, multiple vehicle technologies (e.g., collision warning systems, cruise control, and lane tracking system) and built-environment (roadway) changes have the potential to reduce driving errors and violations which are discussed in the paper.

Studies of rail transit safety typically focus on collisions involving rail vehicles. Nonetheless, the introduction of new rail transit service on existing urban freight corridors may result in changes to the design and use of streets and intersections that influence the safety of other road users as well. This study examines the Orlando SunRail and Charlotte Lynx systems to understand how the introduction of transit service may influences the incidence of total and KAB crashes near transit stations and along at-grade rail intersections. Vehicle-pedestrian and rear-end collisions were found to significantly increase near new transit stations, and vehicle-pedestrian, vehicle-bicyclist, rear-end, and angle collisions significantly increased along at-grade intersections. An examination of satellite imagery for high-crash intersections show that these problems are particularly pronounced when located near multi-lane arterial thoroughfares. Signal placement, preemption, and clearance intervals appear to be major contributing factors, as does restricted sight distance on the approach to at-grade intersections.

Driving errors and violations are identified as contributing factors in most crash events. To examine the role of human factors and improve crash investigations, a systematic taxonomy of driver errors and violations (TDEV) is developed. The TDEV classifies driver errors and violations based on their occurrence during the theoretically based perception-reaction process and analyzes their contributions in safety critical events. To empirically explore errors and violations, made by drivers of instrumented vehicles, in diverse built environments, this study harnesses unique and highly detailed pre-crash sensor data collected in the Naturalistic Driving Study (NDS), containing 673 crashes, 1,331 near-crashes and 7,589 baselines (no-event). Human factors are categorized into recognition errors, decision errors, performance errors, and errors due to the drivers' physical condition or their lack of contextual experience/familiarity, and intentional violations. In the NDS data, built environments (measured by roadway localities) are classified based on roadway functional classification and land uses, e.g., residential areas, school zones, and church zones. Based on the crash percentage to baseline percentage in a specific locality, interstates and open country/open residential (rural and semi-rural settings) may pose lower risks, while urban, business/industrial, and school zone locations showed higher crash risk. Human errors and violations by instrumented vehicle drivers contributed to 93% of the observed crashes, while roadway factors contributed to 17%, vehicle factors contributed in 1%, and 4% of crashes contained unknown factors. The most common human errors were recognition and decision errors, which occurred in 39% and 34% of crashes, respectively. These two error types occurred more frequently (each contributing to nearly 39% of crashes) in business or industrial land use environments (but not in dense urban localities). The findings of this study reveal continued prevalence of human factors in crashes. The distribution of driving errors and violations across different roadway environments can aid in the implementation of driver assistance systems and place-based interventions that can potentially reduce these driving errors and violations.

This paper describes a study that applies the Poisson-Tweedie distribution in developing crash frequency models. The Poisson-Tweedie distribution offers a unified framework to model overdispersed, underdispersed, zero-inflated, spatial, and longitudinal count data, as well as multiple response variables of similar or mixed types. The form of its variance function is simple, and can be specified as the mean added to the product of dispersion and mean raised to the power P. The flexibility of the Poisson-Tweedie distribution lies in the domain of P, which includes positive real number values. Special cases of the Poisson-Tweedie distribution models include the linear form of the negative binomial (NB1) model with P equal to 1.0, the geometric Poisson (GeoP) model with P equal to 1.5, the quadratic form of the negative binomial (NB2) model with P equal to 2.0, and the Poisson Inverse Gaussian (PIG) model with P equal to 3.0. A series of models were developed in this study using the Poisson-Tweedie distribution without any restrictions on the value of the power parameter as well as with specific values of the power parameter representing NB1, GeoP, NB2, and PIG models. The effects of fixed and varying dispersion parameters (i.e., dispersion as a function of covariates) on the variance and expected crash frequency estimates were also examined. Three years (2012-2014) of crash data from urban three-leg stop-controlled intersections and urban four-leg signalized intersections in the state of Florida were used to develop the models. The Poisson-Tweedie models or the GeoP models were found to perform better when the dispersion parameter was constant or fixed. With the varying dispersion parameter, the NB2 and PIG models were found to perform better, with both performing equally well. Also, the fixed dispersion parameter values were found to be smaller in the models with a higher value of the power parameter. The variation across the models in their estimates of weight factor, expected crash frequency, and potential for safety improvement of hazardous sites based on the empirical Bayes method was also discussed.

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. (C) 2020 National Safety Council and Elsevier Ltd. All rights reserved.

This paper examines the relationship between residential accessibility, i.e., accessibility from a person’s home address, and their likelihood of being in a crash over a three-year period. We explore two potential relationships with accessibility. The first is that persons who live in areas with high destination accessibility may drive less and therefore are less likely to be in vehicular crashes. The second is that persons who live in high vehicle miles traveled (VMT) accessibility areas may be exposed to higher levels of traffic in their regular activity space and therefore may be more likely to be in crashes of all modal types. Examining traffic analysis zones in Knoxville, Tennessee, this research finds some evidence for each of these hypothesized effects. These oppositely directed effects have dominant influence within different travel-time thresholds. The first relationship between destination accessibility and fewer crashes is found to be strongest for 10-minute auto accessibility, whereas the second relationship between VMT accessibility and more crashes is found to occur at 10-minute, 20-minute, and 30-minute thresholds.

This paper reviews the literature on the relationship between the built environment and roadway safety, with a focus on studies that analyse small geographical units, such as census tracts or travel analysis zones. We review different types of built environment measures to analyse if there are consistent relationships between such measures and crash frequency, finding that for many built environment variables there are mixed or contradictory correlations. We turn to the treatment of exposure, because built environment measures are often used, either explicitly or implicitly, as measures of exposure. We find that because exposure is often not adequately controlled for, correlations between built environment features and crash rates could be due to either higher levels of exposure or higher rates of crash risk per unit of exposure. Then, we identify various built environment variables as either more related to exposure, more related to risk, or ambiguous, and recommend further targeted research on those variables whose relationship is currently ambiguous.

The findings reveal that recognition errors can be particularly hazardous, given their prevalence in crashes compared with their share in baseline data, and they were also more frequently associated with severe crashes (51%) compared with other errors and violations. Furthermore, the percentage contribution of performance errors and violations in crashes on principal arterials and interstates are almost 7 and 2.5 times higher respectively compared with baselines. Other land uses where certain types of errors are likely to occur were identified.