Jul 2026· Journal of Environment, Climate, and Ecology· 0 citations· 38 references
Abstract
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.
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
Traffic accidents are one of the major problems in the transportation sector, posing significant risks to road users’ safety. Jember City, as one of the activity centers in East Java, has a high level of mobility that potentially contributes to an increased number of traffic accidents. This study aims to identify and map accident-prone areas (black spots) in Jember City using the Z-Score method and the Equivalent Accident Number (EAN). The data used include the number of accidents, fatalities, serious injuries, and minor injuries obtained from relevant agencies. The analysis was carried out by calculating the EAN to determine accident severity levels and applying the Z-Score method to classify locations based on their accident-prone levels. The results indicate that several locations fall into high, medium, and low-risk categories, mostly concentrated along Jember’s main road corridors. This mapping is expected to serve as a basis for local governments and stakeholders in formulating road safety policies, traffic engineering planning, and accident prevention programs in the future.
T. Y. Murti, E. A. Nurdin, S. Astutik et al.· IOP Conference Series: Earth...· 0 citations
Road traffic crashes remain a critical public health concern in Sri Lanka, with Jaffna District facing elevated risk driven by rapid post-conflict motorization and an ageing road network lacking adequate traffic control infrastructure. This study analyses 5,642 police-recorded crashes at uncontrolled locations in Jaffna District over a ten-year period (2014–2024), structured across three phases: Pre-COVID-19 (2014–2019), During COVID-19 (2020–2021), and Post-COVID-19 (2022–2024). A mixed methodology combining Equivalent Property Damage Only (EPDO) crash severity analysis, QGIS-based kernel density blackspot mapping, and systematic field intersection inventories were employed. A total of 3,398 crashes at uncontrolled locations were analyzed, and 22 blackspot junctions were confirmed across four priority road corridors: A009, AB016, AB018, and AB020. The pre-COVID-19 baseline annual EPDO averaged 2,223, declining to 1,725 during COVID-19, and partially recovering to 1,651 post-pandemic. The study found that 68.2% of crashes occurred at uncontrolled T-junctions and four-leg cross junctions, with angles, head-on, rear-end, sideswipe, and single-vehicle collisions accounting for over 85% of events. Field inventories revealed consistent deficiencies including absent road markings, insufficient sight distances, and no advance warning signs. Eight evidence-based engineering countermeasures are proposed — including stop sign packages for T-junction blackspots and mini roundabouts for four-leg junctions. International evidence indicates these measures could achieve a 40–75% reduction in fatal and serious injury crashes, supporting Sri Lanka's National Road Master Plan (2021–2030) and UN SDG 3.6.
J. Sujivan, I. Dias· Proceedings of the 19th Tran...· 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
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
OBJECTIVE
We analyzed monthly administrative data on road crashes and casualties in Greece for the period April 2010-June 2024 (N = 171), with the aim of supporting operational road safety planning.
METHODS
We applied interrupted time series analysis (ITSA) to separate stable seasonal patterns from the impact of the Corona virus disease 2019 (COVID-19) shock. The main analysis is based on segmented regression (ITSA) with monthly dummies for seasonality, estimated using ordinary least squares (OLS) with Newey-West heteroskedasticity and autocorrelation consistent (HAC) errors, with quasi-Poisson models used as count-data robustness checks. Outcomes include monthly counts of crashes, fatalities, and serious and slight injuries.
RESULTS
Road safety indicators peak in June-August and reach their lowest levels in January-March. In March 2020 there was an abrupt level drop in all outcomes (crashes, fatalities, serious injuries, slight injuries) (followed by only partial recovery). The rebound after March 2020 is more visible for total crashes and slight injuries. For fatalities and serious injuries, the count-data checks do not show the same clear upward pattern. This indicates stabilization at a lower post-COVID-19 baseline for the most severe outcomes. Pre/post comparisons around March 2020 indicate large observed differences relative to pre-pandemic average monthly levels, corresponding descriptively to approximately 7,200 fewer crashes and about 1,070 fewer deaths over March 2020-June 2024. We refer to absolute monthly numbers, which are useful for planning system load. They should not be interpreted as exposure-adjusted individual crash risk, because monthly exposure measures such as vehicle-kilometres traveled (VKT) were not available for the full study period.
CONCLUSIONS
The study is quasi-experimental and describes associations. It does not establish full causal attribution. All data and code are provided as supplementary files.
I. Sitzimis· Traffic Injury Prevention· 0 citations