Spatial Analysis of Accident Rates to Determine Accident-Prone Areas on the Waena–Sentani Road, Jayapura Regency
DOI:
https://doi.org/10.58812/wsis.v2i06.2228Keywords:
Accident Characteristics, Black Site, Black Spot, Upper Control Limit, Jayapura RegencyAbstract
The number of traffic accidents in Indonesia is still relatively high, based on data from the Jayapura Regency Traffic Unit, the number of traffic accidents in Jayapura Regency from 2017 to 2019 was 360 incidents. Jayapura Regency which is one of the regencies in Papua Province that is directly related to Jayapura City which continues to experience economic growth and population growth of 2.50% per year (BPS. Jayapura Regency in figures, 2020), thus causing quite high population activity and mobility. The method used in determining blacksite and blackspot areas is the Equivalent Accident Number method, Upper Control Limit and Upper Control Limit to determine the location of the blacksite and the Cumulative Summery (Cusum) method to determine the location of blackspots on Jalan Raya Waena Sentani. Accident-prone areas on the Waena-Sentani Road are located in areas one and area four. Region one with an Accident Number Equivalent value of 155, an Upper Control Limit value of 143.5, and an Upper Control Limit value of 144. Region four with an Accident Number Equivalent value of 155, an Upper Control Limit value of 144.2, and an Upper Control Limit value of 153. with the highest cusum value at location one of 2.56 and location two of 3.34.
References
[1] T. Sayed and W. Abdelwahab, “Using accident correctability to identify accident-prone locations,” J. Transp. Eng., vol. 123, no. 2, pp. 107–113, 1997.
[2] Y. Khosravi, F. Hosseinali, and M. Adresi, “Identifying accident prone areas and factors influencing the severity of crashes using machine learning and spatial analyses,” Sci. Rep., vol. 14, no. 1, p. 29836, 2024.
[3] A. Ifthikar and S. Hettiarachchi, “Analysis of historical accident data to determine accident prone locations and cause of accidents,” in 2018 8th International conference on intelligent systems, modelling and simulation (ISMS), IEEE, 2018, pp. 11–15.
[4] F. A. Gharaybeh, “Identification of accident-prone locations in greater Amman,” Transp. Res. Rec., vol. 1318, pp. 70–74, 1991.
[5] N. Zagorodnikh, A. Novikov, and A. Yastrebkov, “Algorithm and software for identifying accident-prone road sections,” Transp. Res. procedia, vol. 36, pp. 817–825, 2018.
[6] S. Paul, A. M. Alvi, M. A. Nirjhor, S. Rahman, A. K. Orcho, and R. M. Rahman, “Analyzing accident prone regions by clustering,” in Asian Conference on Intelligent Information and Database Systems, Springer, 2017, pp. 3–13.
[7] Y. Hu, Y. Yang, J. Liu, and M. Bai, “Estimating accident-prone freeway sections: simulation and accident prediction model,” in Proceedings of the Institution of Civil Engineers-Transport, Emerald Publishing Limited, 2024, pp. 479–493.
[8] G. Kaur and H. Kaur, “Prediction of the cause of accident and accident prone location on roads using data mining techniques,” in 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2017, pp. 1–7.
[9] S. Mishra, P. K. Rajendran, L. F. Vecchietti, and D. Har, “Sensing accident-prone features in urban scenes for proactive driving and accident prevention,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 9, pp. 9401–9414, 2023.
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