Jul 2026· ISPRS International Journal of Geo-Information· Vol 15, pp. 325· 0 citations· 37 references
Abstract
This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the study utilizes radar graphs, Kernel Density Estimation (KDE), and DBSCAN cluster analysis to delineate high-risk zones and temporal peaks. Temporal findings indicate that Thursdays recorded the highest accident frequency (2382 cases), with peak occurrences between 17:00 and 23:00. Spatial clustering identified five significant high-risk zones, with Central Amman emerging as the primary critical area. The study’s novelty lies in being the first in the Jordanian context to bridge accident frequency with severity mechanisms by integrating advanced spatial clustering and KDE with a robust Ordered Logit Model. Severity analysis reveals that while 59.34% of incidents resulted in minimal injuries, fatalities accounted for 5.02%. The model demonstrates that injury outcomes are systematically associated with traffic dynamics and behavior rather than environmental factors. Speed-related driver error was identified as the strongest predictor of severe outcomes (OR = 81.3). Significant dependencies were confirmed between vehicle category and road type (χ2 = 2182.20, p < 0.001), lighting and road surface (χ2 = 76.21, p < 0.001), and vehicle type and lighting (χ2 = 148.52, p < 0.001). The study proposes a multi-layered framework combining site-specific nodal improvements with corridor-level strategies to enhance urban safety in Amman City.
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
Road traffic accidents are a leading cause of death and disability worldwide, and their burden falls disproportionately on low- and middle-income countries, yet data-driven analyses remain scarce for Sudan. This study presents a descriptive and unsupervised-learning analysis of 2,218 road traffic accidents recorded across the localities of Khartoum State. After cleaning, translating, and feature-engineering the records, an exploratory analysis is conducted through a series of descriptive visualizations spanning the spatial, temporal, vehicular, demographic, and severity dimensions of the data. K-means clustering is then applied within a principal-component space and validated using the elbow method, silhouette analysis, the Calinski–Harabasz and Davies–Bouldin indices, and a hierarchical-clustering cross-check, to derive latent accident typologies. A five-cluster solution separates a dominant high-volume low-severity pedestrian and motorcycle stratum from a smaller but critical high-fatality cluster, a bus-and-collision cluster, and an outlier group, with fatality rates ranging from 1.0% to 99.0%. The analysis reveals that the pedestrian and the afternoon/early evening hours account for almost half of all accidents, and that an interpretable risk segmentation can be derived, with direct consequences for the road-safety policy in Khartoum.
Sallam O. f. Khairy, M. Fadlalla, Taha Alfadul et al.· Journal of Intelligent Decis...· 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
Pedestrian safety remains a major concern in rapidly urbanizing environments, where traffic volumes steadily increase and interactions with motorized vehicles are more frequent. However, few studies have examined how these evolving conditions influence pedestrian crash patterns within the unique urban and mobility context of countries like Qatar. This study examined the spatiotemporal distribution, demographic associations, and contributing factors of pedestrian casualties using crash data from 2017 to 2022. A Kernel density estimation (KDE) was used to locate where casualties are spatially concentrated. Descriptive and ordinal logistic regression analyses were also applied to examine casualty patterns and determine the factors associated with severity levels. The spatial analysis revealed pedestrian casualties of all severity levels were predominantly concentrated in the central and southwestern zones of Doha, particularly in areas characterized by high population density, low-income residents, and industrial activities. While severe and minor injuries formed more spatially continuous clusters within densely populated and economically active zones, fatal casualties were more scattered and extended toward peripheral zones. Male, and non-Arab pedestrians were highly affected, reflecting exposure and physical vulnerability. Casualties were most frequent during evening hours and relatively colder months, when pedestrian exposure increases. Young, and Arab drivers were disproportionately involved in pedestrian casualties, while reckless driving was identified as a key contributing factor. Higher-class roadways (expressways and arterials) and increasing posted speed limits also significantly elevated the odds of severe outcomes. The findings underscore the need for targeted interventions focusing on speed management, improved pedestrian infrastructure, and behavioral interventions. Policy recommendations include installing speed cameras and constructing grade-separated crossings in industrial and commercial corridors, and enhancing sidewalks and marked crosswalks in labor-dense neighborhoods. Additionally, awareness campaigns should focus on young drivers and vulnerable pedestrian groups to promote safer road user behavior. These evidence-based insights provide a foundation for planners and policymakers to improve pedestrian safety by pinpointing high-risk areas.
A. N. Arshi, Wael K. M. Alhajyaseen, Tufail Ahmed et al.· The Arabian journal for scie...· 0 citations
Road traffic accident (RTA) is a global public health concern, particularly in developing regions where infrastructure, enforcement, and driver behavior vary widely. This study investigates the influence of gender, vehicle type, and temporal dynamics on road traffic accidents within Gombe State, Nigeria, in 2025. A quantitative research design and hierarchical Analysis of Variance (ANOVA) was used to examine how these factors interact and contribute to the frequency and severity of accidents. Data were obtained from official records, including police and hospital reports, and analyzed for patterns in accident trends across different months, vehicle types, and gender categories. Findings reveal significant differences in accident rates between males and females, with males recording a higher mean number of accidents (M = 34.03, SD = 25.57) compared to females (M = 10.33, SD = 9.57), with a p-value of 0.000, indicating statistical significance. Vehicle type also emerged as a critical factor, with motorcycle accidents having the highest mean (M = 35.46, SD = 25.97), followed by moving vehicles (M = 29.29, SD = 15.02), and pedestrian-related accidents (M = 1.79, SD = 1.82), all with a p-value of 0.000, showing significant differences between these groups. Temporal dynamics, such as monthly variations, showed no statistically significant effect on accident rates (p = 0.634), despite noticeable fluctuations in mean accident counts across months, with January (M = 25.00, SD = 26.71) and June (M = 28.00, SD = 29.93) having the highest frequencies, and September recording the lowest (M = 13.67, SD = 13.05). The study underscores the need for targeted interventions, such as enhanced motorcycle safety regulations and gender-specific awareness campaigns, to mitigate accident risks.
C. P. Buba, Usman Abdulkadir, Lamidi-Sarumoh Alaba et al.· Dutse Journal of Pure and Ap...· 0 citations
Pedestrian safety represents a critical priority for the development of sustainable urban mobility systems. This study proposes an innovative methodological framework integrating supervised and unsupervised learning techniques with econometric modeling to identify and interpret risk scenarios. Using the Italian national dataset from 2010 to 2023, an XGBoost model has been initially trained and tested. Then, SHapley Additive exPlanations (SHAPs) have been applied to highlight contributing factors. Using the resulting SHAP values, a K-Means clustering algorithm was finally employed to segment crashes into homogeneous clusters. For each cluster, a Generalized Linear Mixed Model incorporating geographic random intercepts and temporal random slopes was calibrated. Through this hybrid-stratified approach, three risk scenarios have been identified, primarily driven by demographic vulnerability. For elderly pedestrians, the involvement of heavy vehicles nearly doubles the odds of a fatal outcome. Crash dynamics varied significantly: heavy vehicles and speeding nearly double the fatality risk for elderly pedestrians; nighttime represents a severe hazard for adults (OR = 3.87) and youths (OR = 7.99), with the latter also highly penalized by unsafe road behaviors (OR = 3.12). From a spatio-temporal perspective, random effects revealed that the Islands (Sicily and Sardinia) are the most critical macro-areas (+55.2% baseline risk for adults) and the North-West the safest. Furthermore, the COVID-19 pandemic mitigated fatal risk for young pedestrians nationwide, had a neutral impact on the elderly, and for adults was protective in Southern regions but corresponded to higher odds of mortality in the North, reflecting altered traffic dynamics.
G. Cappelli, Sofia Nardoianni, M. D’Apuzzo et al.· Sustainability· 0 citations