Jul 2026· Journal of Transportation Science and Technology· Vol 15, pp. 43-53· 0 citations
Abstract
With limited public investment, phased investment strategies (2-lane or limited 4-lane configurations) are widely adopted in Vietnam. However, this model poses safety risks due to the lack of median barriers and continuous emergency lanes. This study analyzes and quantifies factors influencing traffic accident severity using 170 accident records from 2018-2025. Integrating descriptive statistics and hierarchical logistic regression, the research identifies heavy trucks as the primary vehicles in over 71% of collisions. The model reveals that accidents on weekdays pose a severity risk 2.445 times higher than on weekends. Notably, "vehicle overturn or run-off-road" accidents - a direct result of inadequate emergency lanes - increase the probability of severe outcomes by 4.178 times. These findings provide a scientific basis for prioritizing infrastructure upgrades and optimizing traffic management on phased expressways.
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
This study analyzes road traffic accident risk in Tanzania and introduces a composite Regional Road Accident Risk Index (RARI), defined as accidents per 100 km of road, to compare regional risk and assess progress toward SDG 3.6. A multi-method approach was used: national traffic fatality trends from 2000–2022 were modeled using ARIMA forecasting to 2030; a five-year regional panel of 30 regions from 2018–2022 was examined using fixed-effects Poisson regression; and spatial clustering of RARI values was assessed using GIS and Global Moran’s I. Results show that official fatalities declined markedly from the mid-2010s to 2020 but rose again in 2022–2023, suggesting that Tanzania is unlikely to sustain progress toward the SDG target without renewed interventions. Regression findings indicate that driver-related factors, especially speeding, reckless driving, and negligence, are the strongest predictors of accident counts, while vehicle defects and alcohol-related factors also increase risk. RARI reveals substantial regional disparities, with the national average close to one accident per 100 km annually, the highest-risk urban region reaching about 2.5 accidents per 100 km, and the lowest-risk regions around 0.3 accidents per 100 km. Spatial analysis confirms significant clustering, with high-risk areas concentrated around major urban centers and trunk highways. The study is limited by likely under-reporting in police data, lack of vehicle-kilometres-travelled data, and the short regional panel. Nevertheless, the combined forecasting, regression, and spatial approach provides actionable evidence for prioritizing enforcement, infrastructure improvements, vehicle safety checks, and protection of vulnerable road users in high-risk regions.
S. Hamisi· Journal of Environment, Clim...· 0 citations
Using annual national-level data for Poland (2010–2024), this study applies Hansen’s threshold regression framework to identify the point at which the historical positive relationship between motorisation and road traffic accidents reversed sign. A statistically significant and robust structural break is detected in 2019–2020 when the density of motorways and expressways exceeded approximately 1.36 km per 100 km² of territory (95% CI: 1.31–1.46). In the pre-threshold regime (2010–2019), a 10% increase in passenger cars per 1000 inhabitants was associated with an 11.2% rise in accidents; after crossing the threshold (2020–2024), the elasticity became – 0.94. The elasticity of high-speed road infrastructure switched from statistically insignificant to significantly negative (−0.41). Robustness checks using fatalities, injuries, alternative threshold variables, and a two-threshold specification confirm the results. The findings provide the first formal evidence of a system-level safety threshold in Central Europe and demonstrate that sufficiently dense grade-separated infrastructure can trigger a genuine “safety-in-numbers” effect at the national level. Policy implications are drawn for Romania, Bulgaria, and Western Balkan countries still below the identified threshold.
Piotr Gorzelańczyk· Acta Technica Jaurinensis· 0 citations
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
Abstract Iran experiences notably high rates of road crashes, particularly on suburban roads. This study analysed 27,729 crash records from the Isfahan Province Traffic Police, collected between 2018 and 2020, to identify factors influencing crash severity and distinguish between fatal and non-fatal incidents. Using Chi-square tests and logistic regression in SPSS 27, the findings indicate that 6.1% of the analysed crashes (1,692 incidents) were fatal. Driver inattention was the predominant cause of these crashes, accounting for 54.2% of cases. Factors that exacerbated crash severity included night-time conditions (OR = 1.22), passenger vehicles (OR = 1.34), motorcycles (OR = 1.68), rainy weather (OR = 1.75), and incidents occurring on Wednesdays (OR = 1.22). Conversely, elements that were found to mitigate severity included older drivers (OR = 0.99), heavy vehicles (OR = 0.78), buses (OR = 0.35), cloudy weather (OR = 0.66), and occurrences during spring and autumn (OR = 0.84 and 0.80, respectively). These findings suggest that implementing enhanced driver training programs and weather-specific safety measures could significantly reduce the severity of road crashes in Iran.
Mahboubeh Hojati, Neda Yaghobieh, Maziyar Mollaei Pardeh et al.· International Journal of Cra...· 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
