Aug 2026· Frontiers in Sustainable Cities· Vol 8· 0 citations· 17 references
Abstract
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.
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
Road crashes undermine the sustainability of transportation systems by generating substantial human, social, and economic costs. Identifying the factors associated with severe crash outcomes is therefore essential for supporting safer and more sustainable mobility, in accordance with Vision Zero and Sustainable Development Goals 3.6 and 11.2. This study investigates the influence of behavioral, environmental, and infrastructural factors on road crash severity through a comparative evaluation of three modeling approaches: Ordinal Logistic Regression (OLR), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The analysis was based on 5194 police-reported crash records from Cyprus covering the period 2015–2024. The dataset included crash severity outcomes together with traffic violations and roadway and environmental conditions. The results identified speeding, substance use, and improper turning maneuvers as influential predictors of crash severity. Among the evaluated models, CatBoost achieved the strongest overall predictive performance, obtaining the highest accuracy (0.52), weighted area under the curve (AUC) (0.72), weighted precision (0.50), and weighted recall (0.52), while CatBoost and XGBoost tied for the highest weighted F1-score (0.48). CatBoost’s overall performance reflects its ability to efficiently process categorical variables and capture nonlinear relationships without extensive preprocessing. The findings highlight the dominant role of driver behavior in determining crash severity and demonstrate how the comparative modeling framework can serve as an analytical decision-support tool for identifying and monitoring crash-severity risk factors, prioritizing targeted interventions, and supporting progress towards safer and more sustainable transportation systems.
Road traffic accidents continue to be a leading public safety concern in urban areas, and particularly for vulnerable road users including pedestrians and motorcyclists. While the increasing fatality and casualty counts urge a comprehensive approach to understand the factors contributing to crash risks, only a few studies in Mumbai focus on the dynamic interplay between the predictor variables of traffic volume, road condition, and types of road users in the prediction of crash risk. This study uses traffic data from Mumbai's road safety reports from 2019 to 2024 to develop a Negative Binomial Regression Model in order to predict crash risk. Safety Performance Functions are applied to estimate the influence of road features, traffic volume, location type, and weather conditions. The model is tested on real-world data, analyzing factors such as speed limits, road geometry, and user-specific risk factors. Some of the most significant factors that were identified in the model include the volume of traffic, geometric and location factors. The volume of traffic is an important factor, as the additional increase of 1,000 vehicles per day raises the risk of the accident by 0.23%. It has been predicted that 726 injuries have been caused by high-speed high-traffic peak-hour intersections, while 595 injuries have been caused by high-speed mid-block. The highest pedestrian crash risk was found to be in high-traffic intersections with 45.3%, compared to lower traffic intersections with only 15.2% in rural conditions. In terms of practical application, this model offers a means of identifying hazardous locations at a particular time that allows making effective measures to prevent accidents.
Trupti Narkhede, L. Gupta, Prasun Chakrabarti et al.· International Journal of Civ...· 0 citations
Road traffic accidents on national highways pose a significant public health and economic challenge in Bangladesh, necessitating systematic safety assessment. This study analyzes accident trends, contributing factors, and spatial patterns by identifying accident-prone locations (blackspots) along the Kushtia–Jhenaidah National Highway (N704). Accident data for the period 2017–2021 were obtained from nearby police stations. In addition, a cluster random sampling approach was used to conduct a questionnaire survey involving 100 participants, including drivers and general road users, to capture behavioural insights related to accident occurrence. The study integrates descriptive statistical methods, such as trend analysis and frequency distribution, with spatial techniques including severity index evaluation, Kernel Density Estimation (KDE), and hotspot analysis.The findings indicate a decline in overall accident frequency from 2018 to 2021, while fatality rates increased in 2021. Heavy vehicles, particularly trucks, were identified as major contributors to accidents, and head-on collisions emerged as the most common crash type. Key risk factors include driver inexperience, mobile phone usage while driving, overspeeding, inadequate training, and nighttime driving conditions. The analysis further reveals that individuals aged 20–40 are the most affected group, with higher fatality rates among males and higher injury rates among females.A total of 35 accident-prone locations were identified, with several segments classified as blackspots based on accident frequency, injury severity, and fatality occurrence. The study recommends targeted interventions such as driver training, infrastructure improvement, enhanced enforcement, and coordinated policy actions to improve highway safety and reduce accident risks.
N. O, Bhagyalakshmi, Surendrababu M S et al.· International Journal of Res...· 0 citations
This study analyses 397,850 police-reported crashes in Sri Lanka from 2010 to 2020 using a multinomial logit model, validated through Random Forest and XGBoost, to identify key factors influencing crash severity across four outcome levels. Results reveal four findings not apparent from conventional crash statistics. First, motorcycle–vulnerable road user crashes (OR = 202, 10.24% of crashes) produced higher fatal odds than heavy vehicle–motorcycle interactions (OR = 143, 6.86%), despite the latter receiving greater policy attention. This suggests that motorcycle-related risk to vulnerable road users is considerably underappreciated in current safety programs. Second, single-vehicle motorcycle crashes (OR = 46.2, 9.05%) were three times more fatal per incident than motorcycle–passenger vehicle collisions (OR = 14.6), implicating loss of control and emergency response delays as under-recognized contributors. Third, passengers falling from buses yielded a fatal odds ratio of 2,337, the highest in the model, despite representing only 1.50% of crashes, indicating a concentrated fatality risk understated in national reporting. Fourth, dusk and dawn conditions (OR = 2.24, 17.27% of crashes) were found to be equally as dangerous as completely unlit nighttime roads (OR = 2.26), while fog and mist produced the highest lighting-related fatal odds ratio of 4.71. The findings indicate that targeted interventions addressing motorcycle–vulnerable road user interactions, bus passenger safety, and transitional lighting conditions offer the greatest potential for reducing fatal crash outcomes in Sri Lanka.
D. Bhagya, N. Jayantha, H. Pasindu· Proceedings of the 19th Tran...· 0 citations
Road traffic crashes represent a critical public health and safety concern in rapidly urbanizing regions across Ethiopia, mainly affecting vulnerable road users. This study assesses the safety impacts of proposed multimodal and geometric corridor development interventions along the high traffic 18 Mazoria-Ashewa Meda Corridor in Addis Ababa. Using a mixed-methods, historical crash data across 12 equal segments (500m length), field observations, regression modeling and stakeholder perception surveys were analyzed. The assessments revealed severe infrastructure deficiency: 83.3% of segments lacked continuous walkways and 100% lacked dedicated bike lanes and bus bays. Major junctions were strongly correlated with crash severity (r=0.688, p=0.013). multiple regression modeling confirmed major junctions as the dominant predictor of the Weighted Severity Index (B=40.214, p=0.02). Segment 11 (Kidanemihret roundabout) emerged as the primary blackspot (Crash Frequency (CF)=36, representing 31.3% of total crashes) due to steep gradient (~7%) and substandard geometry. Given the absence of post-intervention crash data and data limitation, Crash Modification Factors (CMFS) from the FHWA Clearinghouse and the Highway Safety Manual (HSM) were applied to project safety benefits. The projected interventions demonstrate expected reductions of 32% for dedicated bike lanes (CMF=0.68), 65% for continuous walkways (CMF=0.35), 44% for right-turn geometry modification (CMF=0.558), and 68% for left-turn/ U-turn geometry prohibitions (CMF=0.32). The hazardous Kidanemihret roundabout redesigned into a priority junction to eliminate heavy vehicle rollover risks on steep gradients. Statical evidence, CMF projections and stakeholder perceptions collectively indicate that the interventions will improve road safety.
Rehima Bedewi, Tefera Bahiru· American Journal of Traffic...· 0 citations
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