Aug 2026· Journal of urban health· 0 citations· 14 references
Medicine
Abstract
Active transportation increases physical activity and improves health, yet rising pedestrian and bicyclist deaths are a growing public health concern. In North America, regional agencies allocate transportation investments to support safe walking and bicycling. While comparing crash statistic across regions is standard, accurate safety assessment requires exposure data, or the volume of active transportation users, to determine crash rates. Calculating spatial variation in crash rates is difficult given the lack of spatially detailed, regionally extensive exposure data. Our goal is to develop a method to compare bicycling and pedestrian safety between cities, to inform decisions on where investments are most needed to reduce injury and death. We measured exposure as miles walked or biked in California's 15 largest cities, using a validated model and data from the US Census, National Household Travel Survey, OpenStreetMap, and a statewide crash database. We performed linear regression with exposure and crashes, ranking cities by safety based on residuals. Higher positive residuals indicated less safe cities for active transportation. Including exposure shifted which cities had the highest safety risks for bicyclists and pedestrians. Safety differences were greatest in cities with high percentages of Hispanic residents, where crash-to-volume ratios showed unsafe conditions. Our method enables robust regional assessment of bike and pedestrian safety, allowing health departments and planners to identify where infrastructure investments can effectively prevent injuries and deaths, reduce health disparities, and create environments supporting active living. Our approach is scalable to major population centers nationwide, supporting evidence-based decisions that protect public health.
As U.S. cities expand multimodal transportation networks and pursue Vision Zero goals, crash severity among pedestrians and bicyclists remains a critical public safety challenge. Charlotte, NC, offers a timely case study given its rapid urban growth, rising active transportation demand, and commitment to Vision Zero. This study investigates the human, behavioral, and environmental determinants of pedestrian and bicyclist crash severity in Charlotte, North Carolina, using a five-year crash dataset (2019–2023). Binary logistic regression was applied after evaluating and rejecting ordinal and multinomial approaches due to proportional odds violations and class imbalance. Two models were developed: a primary cause (behavioral) model and an environmental and primary cause model. Crash records were drawn from the Highway Safety Information System (HSIS), supplemented with historical weather data and site inspection to address missing values. The behavioral model found that crashes with no identified contributing circumstance had 2.32 times higher odds of severe injury compared to human-attributed crashes. This association is best interpreted as a crash-documentation artifact, as it attenuates to non-significance once road design and road-user type are controlled. The extended environmental model achieved an AUC of 0.78 (95% CI 0.74–0.82). Daylight reduced severe crash odds by 66%. Divided roads with positive median barriers increased severity risk more than twofold (OR = 2.52). Each additional travel lane raised severity odds by 18%. Urban locations and intersections were also statistically significant predictors, and bicyclists faced substantially lower severity odds than pedestrians (OR = 0.24). Infrastructure design (particularly road division, lane count, and lighting), is a stronger predictor of severe crash outcomes than behavioral fault assignment. Findings support targeted infrastructure interventions in Charlotte’s Vision Zero and active transportation planning efforts.
Fatemeh Abdous, R. Javid, Celeste Chavis et al.· Frontiers in Sustainable Cit...· 0 citations
Past urban transportation safety efforts have largely focused on controlling road user behavior through tactics such as speed management and separating users in space and time. This research moves beyond roadway behavior to examine associations between kinetic energy exposure and safety via travel behavior factors such as vehicle miles traveled (VMT) and mode share.
After analyzing 227 USA urban areas and controlling for state-level heterogeneity via linear mixed-effects models, we found that kinetic energy exposure is more strongly associated with transportation safety outcomes than variables focused on roadway behavior such as vehicle speeds, education requirements, enforcement efforts, alcohol involvement or seat belt use.
Urban areas with lower commute mode share by car and, to a lesser extent, lower VMT are most strongly associated with lower per capita transportation fatality rates.
These results add new knowledge by suggesting that, in addition to focusing on trying to get people to behave better on the roads – as many current safety frameworks do – reducing driving exposure by facilitating shorter driving distances and providing viable multimodal options is also strongly associated with improved safety outcomes. The findings suggest that prevailing transportation safety paradigms, such as the Safe System Approach, may benefit from greater consideration of driving exposure.
Nicholas N. Ferenchak, Wesley E. Marshall· Journal of Transport and Sus...· 0 citations
Highway roads intersect with a railroad track, forming a multimodal transportation system. The intersection is called a highway railroad grade crossing (HRGC). Because of the multiple transportation operations, induced conflict at the grade crossing, crashes may happen between road users (e.g., automobiles, pedestrians, bicyclists, etc.) and trains in operations at highway railroad grade crossings. This is a global challenge for many countries around the world. According to the 2022–2026 Indiana Strategic Highway Safety Plan and 2022 Indiana Highway-Rail Grade Crossing Safety Action Plan, one of the biggest challenges facing Indiana is the higher numbers of pedestrians in and around crossing locations and the possible eligibility of Federal funds to address nonmotorized safety concerns. This study systematically examined the grade crossing safety impacts among human factors, environmental factors, warning device conditions, and other factors through extensive site visits and data analysis. Key findings indicate that grade crossing safety is a function of multiple parameters. Different factors have various levels of contribution to the potential crashes. Crash report analysis and site visit outcomes consistently highlighted some factors’ impacts on the grade crossing safety for nonmotorized users in Indiana. Implementing the insights from this study is expected to improve grade crossing safety, mitigate potential grade crossing crashes, and optimize the budget for grade crossing upgrades and improvements.
Shanyue Guan, Siqi Chen, Shufan Liu et al.· 0 citations
OBJECTIVE
This study established high-resolution, localized fatal crash rate benchmarks across the 50 most populous U.S. urban areas to facilitate future safety performance assessment of automated driving systems (ADS). This study asks the research question: what localized fatal crash trends emerge when analyzing typology, road classification, and temporal variables that can inform more precise, unbiased ADS safety performance assessments?
METHODS
Benchmarks were created using several established fatal crash rate metrics to examine trends in these top 50 urban areas. Fatal crash involvement rates (FCIR), which quantified passenger vehicle fleet involvements in fatal crashes per vehicle miles traveled (VMT), served as the primary analytical metric. This measure accounts for the fatal injury status of all participants, encompassing both other vehicle occupants and vulnerable road users (VRUs). The analysis integrated 2023 Fatality Analysis Reporting System (FARS) crash data with multi-source exposure data, including Federal Highway Administration (FHWA) Highway Statistics and INRIX GPS probe data. Fatal crash rates were stratified by road type (freeways vs. surface streets), temporal groupings (day vs. night; weekday vs. weekend), and a crash typology.
RESULTS
The median fatal crash rates across the top 50 urban areas were generally lower than the national average, indicating that lower population and rural areas tend to have a higher fatal crash rate. The analysis of trends revealed an 7.5-times difference in FCIR between the highest-risk (Memphis, TN) and lowest-risk (Boston, MA) urban areas. On average, surface streets exhibited FCIR 2.3 times higher than freeways. Temporal effects were pronounced, with weekend nighttime driving presenting a risk level 6.2 times greater than weekday daytime driving. Pedestrian, V2V intersection, and single vehicle collisions were the most prominent fatal crash types on surface streets, while single-vehicle, V2V front-to-rear, and secondary crashes dominated freeway involvements.
CONCLUSIONS
The substantial geographic, road type, and temporal variability in fatal crash rates observed across the 50 studied urban areas underscores the necessity of localized, stratified benchmarks to avoid analytical bias in ADS safety performance assessments. By proactively defining these high-resolution benchmarks, this study provides a transparent roadmap for the incremental evaluation of ADS as statistical power accumulates, ensuring conformance with safety assessment best practices such as the RAVE Checklist.
John M. Scanlon, Kristofer D. Kusano, Timothy L. McMurry et al.· Traffic Injury Prevention· 0 citations
The population increase in urban areas leads to heightened vehicle usage, resulting in more interactions among vehicles, pedestrians, and bicyclists, thereby raising substantial road safety issues. Infrastructure must be appropriately constructed for both motorized and non-motorized vehicles to mitigate safety problems for all road users. This study identifies high-crash-prone road segments and critical risk factors for vulnerable road user (VRU) crashes, encompassing pedestrians, bicyclists, and motorcyclists. This is accomplished by creating heatmaps from black-spot research through Geographic Information Systems (GIS) and by determining risk factors with a binary logistic regression (BLR) model. Crash data from Visakhapatnam, India, for the years 2014-2016 and 2019-2021 reveals that more than 50% of fatal incidents involved VRUs, and 75.6% of identified blackspot road segments are situated along a 62-km stretch of National Highway traversing the city. Roadways and land-use factors were collected during road-safety audits along the designated segment. The BLR Model identifies risk factors such as segment length, crash time, season, land use, driver sight distance, and vehicle type. The severity of crashes increases by 17.6% per unit increase in segment length, whereas insufficient visibility increases it by 43.1%. This integrated approach directs targeted interventions to improve the safety of VRUs.
S. Gandupalli, Purnanandam Kokkeragadda, M. Dangeti et al.· EPJ Web of Conferences· 0 citations
Horizontal curves are a significant contributor to crash risk on rural road networks. Historically, high-risk curves are identified reactively, based on examination of crash history. However, to reduce the influence of random variation in observed crashes, proactive approaches, which estimate underlying crash risk, are increasingly preferred. To efficiently target road safety investment, a scalable, proactive approach to estimating rural crash risk is required. This paper presents a proactive corridor prioritisation framework for rural roads in the United States of America (US), based on horizontal curve risk. Road centreline geometry and speed limit data were used to estimate typical operating speeds and “curve context” – the degree to which the estimated operating speed of a curve differs from its safe traversal speed. A Safety Performance Function was used to estimate underlying risk, based on curve context and other geometric and traffic variables. Estimation and validation of the model using Arkansas and California datasets indicated that poor curve context is a significant predictor of crash risk. A case study of the Arkansas network is presented. The model allows estimation of crash risk across the US without reliance on crash data. Corridors are prioritised according to predicted out-of-context curve crash risk per unit length. This approach highlights corridors where treatments to address curve context are expected to deliver the greatest crash reductions relative to treatment costs. A map-based interactive tool is used to display corridor priorities. Targeting safety improvements on prioritised corridors is expected to produce significant reductions in rural road crashes.
Joesph Corbett-Davies· Journal of Road Safety· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
