Analisa Keselamatan Jalan Pada Masa Konstruksi Dengan Metode Kombinasi Work Zone Dan Hotspot Mapping
DOI:
https://doi.org/10.31851/deformasi.v10i2.20405Keywords:
Keselamatan Lalu Lintas, Jalan Tol; Work Zone, Frekuensi Kecelakaan, Tingkat KeparahanAbstract
Keselamatan lalu lintas pada tahap konstruksi jalan tol merupakan isu penting karena adanya peningkatan risiko kecelakaan akibat perubahan geometri jalan, mobilisasi kendaraan proyek, serta pengaturan lalu lintas sementara yang mengganggu arus normal. Penelitian ini dilakukan pada proyek jalan tol Bayung Lencir–Tempino (Seksi 1 dan 2), dengan fokus pada titik pertemuan antara akses proyek dan jalan eksisting, khususnya di tikungan Harmoko. Metode penelitian menggabungkan analisis frekuensi kecelakaan, tingkat keparahan (severity), pemetaan spasial hotspot, serta pendekatan work zone untuk mengidentifikasi daerah rawan kecelakaan. Data teknis rencana jalan (RTA), karakteristik lalu lintas, dan variabel akses proyek dianalisis untuk memprediksi risiko. Model regresi Poisson dan Negative Binomial digunakan untuk memodelkan data kecelakaan berbentuk count data, dengan NB lebih unggul dalam menangani overdispersion. Hasil penelitian menunjukkan bahwa risiko kecelakaan meningkat pada zona dengan perubahan akses tidak terkontrol, mobilisasi alat berat, serta transisi geometri jalan. Mitigasi yang diusulkan meliputi penempatan rambu lalu lintas sesuai kondisi, penggunaan water barrier dan flag man, serta pengaturan kecepatan pada zona kritis. Evaluasi selama enam bulan menunjukkan hasil positif dengan tercapainya zero accident tanpa korban jiwa maupun luka. Penelitian ini menegaskan efektivitas metode work zone dalam mengidentifikasi risiko dan merancang mitigasi berbasis zona, sehingga dapat menjadi acuan dalam perencanaan keselamatan proyek jalan tol di Indonesia
References
Akin, D. (2014). Analysis of Highway Crash Data by Negative Binomial and Poisson Regression Models. Lecture Notes in Computer Science, 123–134. https://www.researchgate.net/publication/268807409_Analysis_of_highway_ crash_data_by_Negative_Binomial_and_Poisson_regression_models
Alam, M. S., & Tabassum, N. J. (2023). Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio. Heliyon, 9(5), e16303. https://doi.org/10.1016/j.heliyon.2023.e16303
Bhuiyan, H., Ara, J., Hasib, K. M., Sourav, M. I. H., Karim, F. B., Sik-Lanyi, C., Governatori, G., Rakotonirainy, A., & Yasmin, S. (2022). Crash severity analysis and risk factors identification based on an alternate data source: a case study of developing country. Scientific Reports, 12(1), 21243. https://doi.org/10.1038/s41598-022-25361-5
Butt, M. S., & Shafique, M. A. (2025). A literature review: AI models for road safety for prediction of crash frequency and severity. Discover Civil Engineering, 2(1), 99. https://doi.org/10.1007/s44290-025-00255-3
Erkan, B., Ertan, G., Yeo, J., & Comfort, L. K. (2016). Risk, profit, or safety: Sociotechnical systems under stress. Safety Science, 88, 199–210. https://doi.org/10.1016/j.ssci.2016.02.002
Federal Highway Administration. (2025). Road Safety Audit guidelines. https://highways.dot.gov/safety/data-analysis-tools/rsa/fhwa-road-safety-audit-guidelines
Hauer, E., & Bamfo, J. (2020). Estimation of Safety Performance Functions for Urban Intersections Using Various Functional Forms of the Negative Binomial Regression Model. Accident Analysis & Prevention, 144, 105664. https://documentserver.uhasselt.be/bitstream/1942/33076/3/AAAP-D-20-00273_R1_peer-reviewed.pdf
Mokhtarimousavi, S., Sharma, A., & Porter, R. J. (2021). Work zone crash severity analysis using mixed statistical and machine learning methods. Transportation Research Record, 2675(12), 1–12. https://pdxscholar.library.pdx.edu/cengin_fac/615/
National Highway Traffic Safety Administration. (2023). Work zone fatal crashes and fatalities. https://workzonesafety.org/work-zone-data/work-zone-fatal-crashes-and-fatalities/
Palese, S., Pazzini, M., Chiola, D., Lantieri, C., Simone, A., & Vignali, V. (2025). Crash Risk Analysis in Highway Work Zones: A Predictive Model Based on Technical, Infrastructural, and Environmental Factors. Sustainability, 17(13), 6112. https://doi.org/10.3390/su17136112
Soltani, A., & Qadikolaei, M. R. (2024). Space-time analysis of accident frequency and the role of built environment in mitigation. Transport Policy, 150, 189–205. https://news.flinders.edu.au/blog/2024/04/24/crash-data-shows-road-safety-must-improve/
Vock, D. (2022). Work zones cause more crashes—only sometimes. https://www.route-fifty.com/infrastructure/2022/06/work-zones-cause-more-crashes-only-sometimes/367950/
Zhang, P., Wang, C., Chen, F., Cui, S., Cheng, J., & Bo, W. (2022). A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime. Sustainability, 14(15), 9061. https://doi.org/10.3390/su14159061
Zhang, Z., Akinci, B., & Qian, S. (2022). Inferring heterogeneous treatment effects of work zones on crashes. Accident Analysis & Prevention, 177, 106811. https://doi.org/10.1016/j.aap.2022.106811
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Jurnal Deformasi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
.png)
.png)
