Predictive Modeling of Flexible Pavement Deterioration Based on Traffic Volume and Service Age
Keywords:
Flexible Pavement, Indonesia Regional Roads, Pavement Deterioration, Pavement Management, Predictive ModelingAbstract
The rapid increase in vehicle numbers on Indonesia’s national and provincial roads has intensified traffic loads, accelerating flexible pavement deterioration. This condition is evident along the Ponorogo–Pacitan corridor, where pavement distress occurs earlier than the design service life. This study aims to develop a predictive model of flexible pavement deterioration based on traffic volume, expressed in passenger car units per hour (smp/h), and service age. Primary data were collected through field surveys using the Pavement Condition Index (PCI), while secondary data included traffic volume, maintenance history, and pavement age. A multivariate non-linear regression analysis was applied to examine variable relationships and construct the predictive model, which was validated using the coefficient of determination (R²) and error metrics (MSE, RMSE). Results indicate that traffic volume and service age significantly accelerate pavement distress, with R² values exceeding 0.90, confirming the model’s robustness. The proposed model captures the complex interaction between traffic growth and structural degradation, providing a scientific basis for maintenance planning. The novelty lies in integrating traffic volume and service age into a single non-linear predictive framework using regional road data from Indonesia. Findings highlight implications for sustainable pavement management, particularly in optimizing intervention timing for technical and economic efficiency, thereby enriching civil and environmental engineering literature and supporting data-driven infrastructure management.
References
Aden, T. S., Widyastuti, H., & Kartika, A. A. G. (2024). The Conceptual of Pavement Management System Based on IoT, Big Data, and Data Mining in Indonesia. E3S Web of Conferences, 517(7), 01001. https://doi.org/10.1051/e3sconf/202451701001
Al-Samahi, S., Zeiada, W., Al-Khateeb, G. G., Hamad, K., & Alnaqbi, A. (2024). A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions. Infrastructures, 9(10), 167. https://doi.org/10.3390/infrastructures9100167
Ali, Arshad, Xinagyu, Guo, & Radulescu, Magdalena. (2023). Nonlinear effects of urbanization routes (proportion of small cities, and proportion of large cities) on environmental degradation, evidence from China, India, Indonesia, the United States, and Brazil. Energy & Environment, 34(8), 3391–3416. https://doi.org/10.1177/0958305X231186843
Ardian, A. R. R., Handayani, D., & Marzuki, A. (2025). A Review: Vibration Caused by Transportation. The 8th Mechanical Engineering, Science and Technology International Conference, 42. https://doi.org/10.3390/engproc2025084042
Arifin, M. Z., Prabowo, P., & Wicaksono, A. D. (2024). Modeling of road damage prediction due to overload using HDM-4 method. EUREKA: Physics and Engineering, 1(1), 70–81. https://doi.org/10.21303/2461-4262.2024.003243
Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto, Y. (2021). Deep learning-based road damage detection and classification for multiple countries. Automation in Construction, 132(12), 103935. https://doi.org/10.1016/j.autcon.2021.103935
Asres, E., Ghebrab, T., & Ekwaro-Osire, S. (2021). Framework for Design of Sustainable Flexible Pavement. Infrastructures, 7(1), 6. https://doi.org/10.3390/infrastructures7010006
Assogba, O. C., Tan, Y., Sun, Z., Lushinga, N., & Bin, Z. (2021). Effect of vehicle speed and overload on dynamic response of semi-rigid base asphalt pavement. Road Materials and Pavement Design, 22(3), 572–602. https://doi.org/10.1080/14680629.2019.1614970
Cina, E., Elbasi, E., Elmazi, G., & AlArnaout, Z. (2025). The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and Opportunities. Sustainability, 17(11), 5148. https://doi.org/10.3390/su17115148
Fafurida, F., Solihah, D. M., & Marpaung, G. N. (2025). Tourism-Induced Environmental Degradation ASEAN Countries: Causes and Consequences. International Journal of Energy Economics and Policy, 15(3), 629–634. https://doi.org/10.32479/ijeep.19143
ForouzeshNejad, A. A., Arabikhan, F., & Aheleroff, S. (2024). Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm. Machines, 12(12), 867. https://doi.org/10.3390/machines12120867
Guo, G., & Zhang, Z. (2022). Road damage detection algorithm for improved YOLOv5. Scientific Reports, 12(1), 15523. https://doi.org/10.1038/s41598-022-19674-8
Hariani, M. L., Taufik, A., Suparman, S., Kosasih, A., & Farhan, O. (2025). Road Damage Analysis On Inter-City Roads Using Pavement Condition Index (PCI) Approach In West Java Indonesia. Interdiciplinary Journal and Hummanity (INJURITY), 4(7), 534–548. https://doi.org/10.58631/injurity.v4i7.1464
Hartatik, E. S., Wasino, W., & Trihatmoko, E. (2022). Road Transportation Development and Land Use Changes in Semarang City, Central Java, Indonesia. Indonesian Journal of Geography, 54(3), 1–12. https://doi.org/10.22146/ijg.66195
Hook, W., & Replogle, M. (2022). Motorization and non-motorized transport in Asia: Transport system evolution in China, Japan and Indonesia. Land Use Policy, 13(1), 69–84. https://doi.org/10.1016/0264-8377(95)00025-9
Kabashkin, I. (2024). The Iceberg Model for Integrated Aircraft Health Monitoring Based on AI, Blockchain, and Data Analytics. Electronics, 13(19), 3822. https://doi.org/10.3390/electronics13193822
Lanori, T., & Supriyanto, B. H. (2023). Problems of the Impact of Modernization of Indonesia’s Public Transportation Equipment System. International Journal of Social Science, 2(5), 2163–2176. https://doi.org/10.53625/ijss.v2i5.4929
Lestari, A., Rahmawati, A., Shofiah, S., Siswanto, J., & Putra, B. H. R. (2025). Predictive Modeling of Microsleep Incidents in Indonesian Drivers Using Random Forest: A Data-Driven Approach for Road Safety Enhancement. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 4(2), 62–72. https://doi.org/10.20885/snati.v4.i2.39984
Li, S., Ma, T., & Wang, D. (2023). Photovoltaic pavement and solar road: A review and perspectives. Sustainable Energy Technologies and Assessments, 55, 102933. https://doi.org/10.1016/j.seta.2022.102933
Liu, Z., Gu, X., Ren, H., Zhou, Z., Wang, X., & Tang, S. (2022). Analysis of the dynamic responses of asphalt pavement based on full-scale accelerated testing and finite element simulation. Construction and Building Materials, 325, 126429. https://doi.org/10.1016/j.conbuildmat.2022.126429
Liu, Z., Yang, Q., & Gu, X. (2023). Assessment of Pavement Structural Conditions and Remaining Life Combining Accelerated Pavement Testing and Ground-Penetrating Radar. Remote Sensing, 15(18), 4620. https://doi.org/10.3390/rs15184620
Lukman, A. P., Mahendra, M. O., & Yudhzan, M. R. (2024). The relationship model between road surface roughness and functional condition values of the road surface using IKP method and IRI with roadroid application. Teknika: Jurnal Sains Dan Teknologi, 20(1), 46. https://doi.org/10.62870/tjst.v20i1.22673
Maryam, S. H., Bulgis, B., & Madami, R. (2023). Comparative Study of Performance between International Roughness Index (IRI), Pavement Condition Index (PCI), and Bina Marga Method on Roadways. PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik, 8(1), 97. https://doi.org/10.51557/pt_jiit.v8i1.2030
Matini, N., Qiao, Y., & Sias, J. E. (2022). Development of Time–Depth–Damage Functions for Flooded Flexible Pavements. Journal of Transportation Engineering, Part B: Pavements, 148(2), 4022011. https://doi.org/10.1061/JPEODX.0000352
Mirshekali, H., Santos, A. Q., & Shaker, H. R. (2023). A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids. Energies, 16(17), 6332. https://doi.org/10.3390/en16176332
Mohammed, A. E., Osama, M., Mohamed, M., & Tarek, Z. (2021). Integrative Evolutionary-Based Method for Modeling and Optimizing Budget Assignment of Bridge Maintenance Priorities. Journal of Construction Engineering and Management, 147(9), 4021100. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002113
Nadirgil, O. (2023). Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm. Journal of Environmental Management, 342(12), 118061. https://doi.org/10.1016/j.jenvman.2023.118061
Nizamani, S. M., Nizamani, M. M., & Nizamani, R. A. (2025). An integrated deep learning based approach for traffic maintenance prediction with geographic information system data. In U. A. Bhatti, M. M. Nizamani, Y. Wang, & H. B. T.-D. L. for E. O. and C. M. Tang (Eds.), Deep Learning for Earth Observation and Climate Monitoring (pp. 167–179). Elsevier. https://doi.org/10.1016/B978-0-443-24712-5.00009-9
Nur Iman, A. H., Hariwibowo, F., & Rubangga, R. (2025). The Effect of on-Street Parking on Traffic Performance in Commercial Areas of Small Cities in Indonesia (Case Study: St. Merdeka Utara Cirebon). Jurnal Indonesia Sosial Teknologi, 6(2), 999–1019. https://doi.org/10.59141/jist.v6i2.8972
Nurhasanah, R., Rifai, A. I., Taufik, M., & Isradi, M. (2024). The Perception of User for Road Damage: A Case Majalengka-West Java. OPSearch: American Journal of Open Research, 3(10), 258–267. https://doi.org/10.58811/opsearch.v3i10.136
Nurhidayat, A., & Kamarudin, K. H. Bin. (2024). A Systematic Review of the Impact Overload on Road Pavement Batu City, Indonesia. International Journal of Transport Development and Integration, 8(1), 49–60. https://doi.org/10.18280/ijtdi.080105
Nyirandayisabye, R., Li, H., Dong, Q., Hakuzweyezu, T., & Nkinahamira, F. (2022). Automatic pavement damage predictions using various machine learning algorithms: Evaluation and comparison. Results in Engineering, 16(1), 100657. https://doi.org/10.1016/j.rineng.2022.100657
Ou, J., Zhang, J., Li, H., & Duan, B. (2025). Road damage prediction and intelligent maintenance methods based on stacking ensemble learning. Advanced Engineering Informatics, 66(1), 103466. https://doi.org/10.1016/j.aei.2025.103466
Pamungkas, R. W., Witjaksana, B., & Purnama, J. (2025). Analysis of Construction Costs For The KH Ahmad Dahlan Pohjentrek Road Section In Pasuruan City Considering Traffic Growth. Journal of Social Research, 4(3), 501–511. https://doi.org/10.55324/josr.v4i3.2473
Prathap, R. C., Velayudhan, S., & Mathew, S. (2025). Impact of overloaded axles on the pavement service life: a case study of a two-lane national highway. International Journal of Pavement Engineering, 26(1), 2490222. https://doi.org/10.1080/10298436.2025.2490222
Purwanto, I. N., Widodo, A., Sholihah, Q., & Anwar, M. R. (2023). Traffic Congestion Modeling and Impact on the Work Environment at Brawijaya University. Transactions of the Chinese Society of Agricultural Machinery, 54(12), 10–25. https://doi.org/10.62321/issn.1000-1298.2023.12.02
Rachman, M. N. F. A., Rifai, A. I., Rijaluddin, A., & Prasetijo, J. (2025). The Analysis of Rural Road Distress with Indonesian Standard: A Case of Majalengka-West Java. The 8th Mechanical Engineering, Science and Technology International Conference, 58. https://doi.org/10.3390/engproc2025084058
Radwan, M. M., Mousa, A., & Zahran, E. M. M. (2024). Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index. Sustainability, 16(8), 3158. https://doi.org/10.3390/su16083158
Ruchiyat, F., & Prabowo, Y. S. (2025). Cost-Benefit Analysis of Rigid Pavement Road Construction Using Exponential, NPV, and ERR Methods (Case Study: Cimanying–Jiput Road Section, Pandeglang, Banten). Jurnal Teknik Sipil Cendekia, 6(2), 374–392. https://doi.org/10.51988/jtsc.v6i2.342
Sandamal, K., Shashiprabha, S., Muttil, N., & Rathnayake, U. (2023). Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance. Sustainability, 15(12), 9617. https://doi.org/10.3390/su15129617
Santos, D., Saias, J., Quaresma, P., & Nogueira, V. B. (2021). Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction. Computers, 10(12), 157. https://doi.org/10.3390/computers10120157
Sari, Y. I., & Rahman, E. (2021). Roads for communities, not commodities: A qualitative study of the consequences of road development in Papua, Indonesia. Human Ecology Review, 27(1), 139–161. https://search.informit.org/doi/10.3316/informit.280588246613552
Setiawan, I. C., Indarto, & Deendarlianto. (2021). Quantitative analysis of automobile sector in Indonesian automotive roadmap for achieving national oil and CO2 emission reduction targets by 2030. Energy Policy, 150(5), 112135. https://doi.org/10.1016/j.enpol.2021.112135
Shah, R., Pai, N., Thomas, G., Jha, S., Mittal, V., Shirvni, K., & Liang, H. (2025). Machine Learning in Wear Prediction. Journal of Tribology, 147(4), 040801. https://doi.org/10.1115/1.4066865
Shaikh, S. G., Mahajan, D. U., Shaikh, M. N. S., & Wadekar, A. P. (2022). Scientific Study of Asphalt Road Surface Distress and their Role in the Design of Flexible Pavements. International Journal of Engineering Trends and Technology, 70(1), 220–232. https://doi.org/10.14445/22315381/IJETT-V70I1P227
Shehadeh, A., Alshboul, O., Al Mamlook, R. E., & Hamedat, O. (2021). Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction, 129(12), 103827. https://doi.org/10.1016/j.autcon.2021.103827
Shtayat, A., Moridpour, S., Best, B., & Rumi, S. (2022). An Overview of Pavement Degradation Prediction Models. Journal of Advanced Transportation, 22(1), 1–15. https://doi.org/10.1155/2022/7783588
Sugiarto, S., Saleh, S. M., Darma, Y., Rusdi, M., A’yuni, Q., Fazila, T. S., & Rahma, R. (2024). Base saturation flow rate (BSFR) and its effect on performance of pretimed signalized intersection with non-lane based urban heterogeneous traffic. PLOS ONE, 19(7), e0306112. https://doi.org/10.1371/journal.pone.0306112
Teopilus, C. D., & Amrozi, M. R. F. (2023). The Evaluation of Pavement Condition Assessment Methods for Road Assets in Coastal Areas. INERSIA Lnformasi Dan Ekspose Hasil Riset Teknik Sipil Dan Arsitektur, 19(2), 183–193. https://doi.org/10.21831/inersia.v19i2.61089
Trestanto, F., Hadiwardoyo, S. P., Tambunan, H. F., & Lumingkewas, R. H. (2024). Correlation between Deformation and Volumetric Characteristics in the Aging Condition of Hot Mix Asphalt. E3S Web of Conferences, 517(17), 12002. https://doi.org/10.1051/e3sconf/202451712002
Wan, F., Sun, C., He, H., Lei, G., Xu, L., & Xiao, T. (2022). YOLO-LRDD: a lightweight method for road damage detection based on improved YOLOv5s. EURASIP Journal on Advances in Signal Processing, 2022(1), 98. https://doi.org/10.1186/s13634-022-00931-x
Wang, J., & Gao, R. X. (2022). Innovative smart scheduling and predictive maintenance techniques. In D. B. T.-D. and O. of P. N. for M. P. in the E. of C. T. Mourtzis (Ed.), Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology (pp. 181–207). Elsevier. https://doi.org/10.1016/B978-0-12-823657-4.00007-5
Wang, S., Fan, Y., Jin, S., Takyi-Aninakwa, P., & Fernandez, C. (2023). Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries. Reliability Engineering & System Safety, 230(12), 108920. https://doi.org/10.1016/j.ress.2022.108920
Yogi, O., Antonius, & Rochim, A. (2025). An Artificial Neural Network Approach for Predicting Pavement Distress: A Case Study Toward Sustainable Road Maintenance. Advance Sustainable Science Engineering and Technology, 7(3), 02503019. https://doi.org/10.26877/asset.v7i3.2133
Ziar, A., Ulfat, S., Serat, Z., & Armal, M. A. (2023). Cost-Effectiveness Analysis of Design Methods for Rigid and Flexible Pavement: A Case Study of Urban Road. Archives of Advanced Engineering Science, 2(3), 134–141. https://doi.org/10.47852/bonviewAAES32021264
Zou, Y., Xie, J., Zheng, S., Liu, W., Tang, Y., Tian, W., Deng, X., Wu, L., Zhang, Y., Wong, C.-W., Tan, D., Liu, Q., & Xie, X. (2022). Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery. International Journal of Surgery, 107(12), 106936. https://doi.org/10.1016/j.ijsu.2022.106936
Zukhruf, F., Adi Kuntoro, A., Faturohman, T., Alfadhila Permana, S., Adha Rahman Leksono, A., Maulina, R., & Prasetyo Suharjo, I. (2025). Reinforcement learning-based model for road network maintenance and rehabilitation programming considering flood occurrences. International Journal of Pavement Engineering, 26(1), 2466805. https://doi.org/10.1080/10298436.2025.2466805
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