Spatial and Temporal Analysis of Road Traffic Accidents Using GIS - Bhele, Raju; Dhungana, Sameer; Chimoriya, Dikshit; Sapkota, Aabhushan; Ghorasaini, Sujan (International Journal on Engineering Technology, 2024)
Road traffic accidents are becoming more common, presenting serious public safety and urban development problems. This study employs Geographic Information Systems to conduct a comprehensive spatial and temporal analysis of road traffic accidents, aiming to identify accident hotspots and underlying patterns. By integrating and analyzing accident data from police reports, the distribution and frequency of accidents across different locations and periods were studied. The accident rate has increased by 1.27% in the fiscal year 2020/2021 than 2019/2020, 2.54% in 2021/2022 than 2020/2021, and by 6.03% in 2022/2023 than 2021/2022. About 12.7% of total accidents recorded were minor, 72.4% of total accidents were major and 14.9% of total accidents recorded were observed to be fatal. The findings reveal a high concentration of accidents at the central part along the Araniko highway. Banepa municipality has a high accident rate compared to Dhulikhel and Panauti due to overspeeding, overloading, overtaking, careless driving, mechanical failures, and loss of vehicle control. Minor accidents were frequent at high-traffic intersections, whereas the major and fatal were predominantly concentrated along the Araniko highway. 41% of medium size, 38% of heavy vehicles, and 39% of light vehicles encountered accidents. The study revealed that head-on collisions on the narrow section of Araniko Highway occur often. Intersection management and signalization in Banepa need to be addressed since rear-end collisions increased during periods of high traffic congestion. Two-wheel riders and pedestrians are the more vulnerable. The study utilizes spatial statistical tools such as Kernel Density Estimation (KDE) to identify accident-prone locations. Temporal analysis further explains peak times and seasons for accidents. The findings reveal distinct spatial clusters of accidents and temporal patterns during peak and non-peak hours. This spatial and temporal analysis of accidents showed the need for specific road safety interventions. Through the identification of accident causes and the implementation of recommended measures, these municipalities can significantly lower accident rates and improve overall road safety in their locality.