Modelling Road Traffic Crashes in the Sunyani Municipal using Gis
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Date
2025-02
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UENR
Abstract
Road traffic accidents continue to be a major global public health and safety concern, requiring creative mitigation strategies. Using Sunyani Municipal as a case study, this study investigates how effective Geographic Information Systems (GIS) can be applied to model road traffic crashes to influence decision-making and reduce crash occurrence. This study identifies highrisk areas, assesses contributing factors, and suggests data-driven remedies by combining geographic analysis with collision data, road infrastructure characteristics, and traffic flow patterns. Road network layouts, traffic volume figures, and police crash reports from 2018 to 2022 were among the secondary data that were georeferenced and examined using GIS methods such as spatial autocorrelation, hotspot analysis, and Kriging.
The findings showed clear spatial-temporal clusters of crashes, with a significant Moran's I index of 0.080 (z-score = 2.49, p-value = 0.006), confirming non-random clustering. Hotspot analysis (Getis-Ord Gi*) identified the Sunyani Technical
University to Sunyani Senior High School stretch as the most critical hotspot with a
99% confidence level, alongside several other corridors (e.g., Estate Junction to Post Office) at a 95% confidence level. Key contributing factors with high respondent agreement included over-speeding and careless driving (18.67% each) and wrongful U-turns (14.67%). Socioeconomic elements that increased the crash risk included proximity to schools and markets. It is important to note that these findings are subject to uncertainties, primarily due to limitations in data completeness and potential geolocation inaccuracies inherent in the police-reported crash data.
The study illustrates how GIS may be used to visualize risk patterns, which aids policymakers in prioritizing infrastructure improvements, implementing focused traffic laws, and streamlining emergency response routes. Installing traffic-calming measures in designated hotspots, improving street lighting, and incorporating realtime GIS monitoring systems are among the recommendations. This study emphasizes the importance of GIS as a tool for data-driven road safety management, providing urban governments facing comparable difficulties with scalable insights. The findings support the implementation of GIS-driven policies in Sunyani Municipal and similar places to promote safer road ecosystems and lower crash related injuries and deaths to materialize the country's UN goal by 2030.
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Keywords
Road Safety, Traffic, Accidents, Roads