Jul 2026· International Journal of Research Publication and Reviews· 0 citations
Abstract
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.
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
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
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
OBJECTIVES
Identifying the hotspots is crucial to prevent the hazardous effects of Road Traffic Accidents (RTAs). This research based on the significant current and future hotspots and temporal patterns of the RTAs of two years (2022-2023) by integrating the GIS tools with statistical analysis, investigates and reveals the future hotspots and road intersections and segments having high density of RTAs in Faisalabad city, Pakistan. The aim of this research is to identify the spatial hotspots of RTAs by analyzing the RTAs density along road network intersections, and to predict the future hotspots of the RTAs. It provides the insights for a thorough understanding of the road network's aspects leading RTAs.
METHODS
To examine this, we have acquired the datasets of total 38,865 RTAs incidents, city area administrative boundaries, 4,458 km road network having 64,557 segments. Three advanced spatio-temporal analysis tools are employed. Network Kernel Density Estimation (NetKDE) to identify the dense accident segments, Repeat and Near Repeat analysis tool integrating with Predictive Zones analysis tool for predicting future hotspots are used. These tools allow to identify the highly effected road intersections and future hotspots locations and temporally variations of RTAs.
RESULTS
This research seeks attention to the density of accidents at specific intersections and road sections, examining the part that city's road infrastructure and rush-hour traffic play in the frequencies of accidents. Important City's intersections and arterial network, such as West Canal Road, Chenab Chowk, and Clock Tower, were the primary locations of accidents. The most accidents are concentrated near commercial areas or roads that are commonly used for commuting. The X-intersection accounts for 27.4% and Crossroad intersection accounts for 24.1% of total accidents. The accuracy index of predictive model reached at the point of 1.78, that is indicating the 78% better efficiency of this model rather than randomly allocating the future hotspots. The results reveal a continuous pattern of accidents at these segments throughout both years, it also provides the precise locations, peak time and risk zones of accidents. That requires the traffic control during peak time and improvements in road infrastructure the installation of traffic signals and availability of proper walkable spaces and maintenance of footpaths.
CONCLUSION
This research provides a thorough examination of the spatio-temporal aspects of RTAs with futuristic approach, providing reliable and practical findings for enhancing the public safety, improving traffic management and infrastructure, for the well-being of the urban environment and living standard.
Abdullah Munif, Shoaib Khalid, Fariha Zameer et al.· Traffic Injury Prevention· 0 citations
This article presents a geospatial model for identifying and assessing the risk of hazardous locations in the road network, developed to predict traffic safety hazards in areas with complex infrastructure where traditional methods, such as the Highway Safety Manual, are insufficient. The objective of the study was to develop a model that classifies road segments into five risk categories based on environmental and infrastructural characteristics, without using accident or traffic volume data. The model accounts for speed limits, road geometry, and the proximity of facilities that generate pedestrian traffic (schools, preschools, stores) and infrastructure elements (crosswalks, intersections). A hybrid approach was used, combining proprietary methods for determining distances from objects: vector-based (geodetic distance), route-based (road graph), and geometric (classification of a road segment’s shape), using QGIS, OpenStreetMap, and custom Python scripts. The results enabled assigning a risk category to each road segment, and validation was performed by comparing them with the locations of actual accidents resulting in serious injuries or fatalities. The developed model for identifying hazardous locations is a scalable tool that supports sensor-network-based area-based speed control systems, infrastructure planning, and safety management in regions with diverse road networks.
Mariusz Rychlicki, Z. Kasprzyk· Applied Sciences· 0 citations
Road traffic accidents pose a significant threat to life and property. However, the multitude of contributing factors makes it challenging to pinpoint the most critical ones, thereby hindering effective prevention strategies. Therefore, this study conducts relevant work using the UK Department for Transport’s 2019 road traffic accident datasets. To investigate the distribution characteristics of accidents across various dimensions (person, vehicle, road, environment, and accident configuration), we first preprocessed the data. Missing values were imputed using a chained random forest-based multiple imputation method. To identify key contributing factors, we employed an integrated approach combining Bayesian-optimized random forest, Cramér’s V correlation test, K-modes clustering, and frequency statistics. This framework enabled the exploratory identification of potential high-risk scenarios for both non-operating and passenger vehicles. Subsequently, we applied a constraint-based Apriori algorithm to analyze correlations across these dimensions and temporal factors, revealing significant associations between accident severity and the examined attributes. Finally, a Bayesian-optimized LightGBM model was built to predict accident risk levels. External validation using the 2022 UK dataset, combined with interpretive analysis, confirmed the model’s strong generalization ability.
Ziyan Zhang, Zhenfei Zhan, Rongjie Mao et al.· Vehicles· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
