Transport And Communications 2024, 12(1):27-35 | DOI: 10.26552/tac.C.2024.1.4
An Assessment of the Roadway segment Environmental Influence on Traffic Crash
- 1 Olabisi Onabanjo University, Ago-Iwoye, Nigeria
Traffic crashes remain a critical global issue. Governments have introduced measures such as legislation, driver education, and improved road design to address this challenge. This study examines highway traffic collision data from Federal Road Safety Corps (FRSC) records (2020–2022), integrating georeferenced maps, road networks, satellite imagery (Landsat, Google Maps), Shuttle Radar Topography Mission (SRTM) data, and field survey data from blackspot road segments. Analysis, which was done in ARCgis 10.8 environment, identifies 66 blackspots across 18 federal routes, with 56 curved sections linked to these routes. The findings reveal a strong correlation between environmental terrain and crash occurrences. The study advocates for the strategic placement of road signs and symbols and highlights the necessity of leveraging Global Positioning System (GPS) and Geographic Information System (GIS) technologies for accurate traffic data collection and analysis, particularly in complex terrains. These recommendations aim to enhance roadway safety and inform evidence-based policymaking.
Keywords: Traffic Crashes, Blackspot, Geo-reference, Roadway environment
JEL classification: L91, O18, R42
Received: April 20, 2024; Revised: April 20, 2024; Accepted: April 27, 2024; Prepublished online: April 27, 2024; Published: April 28, 2024 Show citation
References
- Ali, H., Moradkhani, N., Abulibdeh, A., & Younes, A. (2018). Application of geographically weighted regres-sion technique in spatial analysis of fatal and injury crashes. International Journal of Transportation, 2(4), 28-40.
- Andrá¹ik, R., & Bíl, M. (2020). Efficient road geometry identification from digital vector data. Journal of Geo-graphical Systems, 18(3), 249-260. https://doi.org/10.1007/s10109-016-0230-1
Go to original source...
- Ivan, K., Haidu, I., Benedek, J., & Ciobanu, S. M. (2018). Identification of traffic accident risk-prone areas under low lighting conditions. Natural Hazards and Earth Sys-tem Sciences Discussions, 3, 1453-1471. https://doi.org/10.5194/nhessd-3-1453-2023
Go to original source...
- Lamidi, A. J., Jegede, P. A., & Odeyemi, C. A. (2022). Road network mapping and analysis of Ado-Ekiti town-ship roads using remote sensing and GIS techniques. Quest Journals: Journal of Research in Environmental and Earth Sciences, 8(9), 59-64. ISSN (Online): 2348-2532. Retrieved from https://www.questjournals.org
- World Health Organization (WHO). (2020). Global sta-tus report on road safety 2020. Retrieved from https://www.who.int/publications/i/item/global-status-report-on-road-safety-2020
- Songchitruksa, P., & Zeng, X. (2018). Getis-Ord spatial statistics to identify hot spots by using incident man-agement data. Transportation Research Record, 2165(1), 42-51. https://doi.org/10.3141/2165-05
Go to original source...
- World Health Organization (WHO). (2023). Global sta-tus report on road safety. Geneva: WHO. Retrieved from https://www.who.int/violence_injury_prevention/road_safety_status/2023/en/
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