Examining the influence of early rainfall on road traffic accidents: A spatial approach
DOI:
https://doi.org/10.61511/srsd.v3i1.2026.3181Keywords:
geographic information system, road traffic accidents, spatial analysis, rain seasonAbstract
Background: Road Traffic Accidents (RTAs) pose a significant hindrance to efficient road transportation and a substantial risk to public safety. This research provides a stepwise analysis of RTA frequency and spatial distribution in the Special Region of Yogyakarta Province, using self-reported accident data from a prominent local social media platform. While RTA research is extensive globally, a paucity of localized studies specifically focusing on Yogyakarta using advanced spatial techniques necessitated this investigation to address the existing literature gap. The study’s objective was to determine the frequency of RTA incidents across temporal and incident categories, identify spatial clustering patterns, and precisely locate high-frequency red zones (hotspots). Methods: The methodology employed a multi-method approach, integrating IBM SPSS Statistics for descriptive analysis and ArcGIS for spatial analysis, explicitly using the Nearest Neighbour Analysis (NNA) and Kernel Density Estimation (KDE). The study focused on RTA data from the early onset of the rainy season (October 1st to late October 2025), using this temporal constraint as a control for seasonal risk. The ultimate goal is to generate actionable insights and provide practical solutions to reduce accident occurrences. Insights were derived through statistical analysis, spatial mapping of accident-prone areas, and detailed categorization of incidents. Findings: The analysis revealed that traffic accidents were most frequent during weekdays preceding the weekend. Two-wheel vehicle collisions accounted for the majority of incidents, particularly at night and in the early morning (18:30–06:30). Spatially, accidents were not randomly distributed but clustered along major arterial roads within Yogyakarta City’s urban core. These hotspots align strongly with areas of high traffic density, emphasizing the vulnerability of two-wheeler users during the early rainy season. This align with previous studies suggesting that reduced visibility, driver fatigue, and slippery road conditions during early rainfall events amplify accident risks especially among vulnerable two-wheeler users. Conclusion: The study highlights that the early rainy season significantly intensifies accident risks in high-traffic urban corridors. Strengthening targeted traffic management in high-risk areas and during these times is essential to mitigating future accidents. Novelty/Originality of this article: This study isolates the early rainy season as a temporal window for assessing accident vulnerability, offering new insights into the transitional weather phase. It also introduces the use of social media–based incident data integrated with spatial–temporal analysis (NNA and KDE) as a quick, low-cost approach to mapping road safety risks.
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