Data-Driven Traffic for Infrastructure Planning: An LSTM Approach Using Indonesian Road-Vehicle Trends
DOI:
https://doi.org/10.63158/journalisi.v8i2.1516Keywords:
Exploratory forecasting, Infrastructure planning, LSTM, Time series forecasting, Traffic load ratio, Transport infrastructureAbstract
The rapid growth of motorized vehicles in Indonesia, unmatched by proportional expansion in road infrastructure, has intensified pressure on the national transportation system. This study examines the application of a Long Short-Term Memory (LSTM) model to analyze and forecast the national traffic load ratio, defined as the ratio of total motorized vehicles to total road length. Annual aggregate data from the Indonesian Central Bureau of Statistics (BPS) for the period 2016–2023 were used in the analysis. The results indicate that the model achieved a strong fit on the training data, with RMSE = 0.3652 and MAE = 0.3617, but performed substantially worse on the test data, with RMSE = 1.7585 and MAE = 1.7585. This discrepancy suggests overfitting, largely attributable to the extremely limited sample size. As such, the findings should be interpreted as exploratory rather than as evidence of reliable forecasting performance. Despite these limitations, the model projects a continued upward trend in national infrastructure pressure over the next five years. These findings provide an initial data-driven indication that transportation infrastructure demand in Indonesia is likely to intensify, while also underscoring the need for future research using larger datasets and baseline model comparisons before policy-level application can be justified.
Downloads
References
[1] C. W. Sunie Rahardja, “Kuantifikasi Parameter Lalu Lintas Kendaraan Bermotor Di Jalan Jenderal Sudirman Jakarta Pusat,” FaST-Sains dan Teknologi, vol. 6, no. 1, 2022.
[2] G. Yannis and A. Chaziris, “Transport System and Infrastructure,” Transportation Research Procedia, vol. 60, no. 2021, pp. 6–11, 2022, doi: 10.1016/j.trpro.2021.12.002.
[3] N. P. S. E. Setyarini and F. I. Taubi, “Evaluasi Kondisi Ruas Jalan Tomang Raya Dengan Akj Untuk Mencapai Jalan Berkeselamatan,” Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan, vol. 6, no. 2, pp. 291–300, 2022, doi: 10.24912/jmstkik.v6i2.18453.
[4] M. Chang, Z. Ding, L. Guo, and Z. Zhao, “Traffic Propagation in Road Network from a Data-Driven Analysis Perspective,” Journal of Transportation Engineering, Part A: Systems, vol. 149, no. 2, Feb. 2023, doi: 10.1061/JTEPBS.0000758.
[5] I. Stan, D. A. Ghere, P. I. Dan, and R. Potolea, “Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding,” Applied Sciences (Switzerland), vol. 13, no. 4, Feb. 2023, doi: 10.3390/app13042143.
[6] M. Syahbandi, A. N. R. Mardiah, and S. E. Wijaya, “Designing Sustainable Transportation Strategy in Covid-19: Jabodetabek Commuter Community Movement in Indonesia,” International Journal for Disaster and Development Interface, vol. 2, no. 1, Jul. 2022, doi: 10.53824/ijddi.v2i1.20.
[7] D. Li and J. Lasenby, “Mitigating urban motorway congestion and emissions via active traffic management,” Research in Transportation Business and Management, vol. 48, Jun. 2023, doi: 10.1016/j.rtbm.2022.100789.
[8] D. L. Guidoni, G. Maia, F. S. H. Souza, L. A. Villas, and A. A. F. Loureiro, “Vehicular Traffic Management Based on Traffic Engineering for Vehicular Ad Hoc Networks,” IEEE Access, vol. 8, pp. 45167–45183, 2020, doi: 10.1109/ACCESS.2020.2978700.
[9] T. Dongare, D. Huljute, P. Jadhav, A. Lad, and P. S. Marawar, “A Review on Traffic Management and Road Analysis of Porwal Road,” International Journal for Research in Applied Science and Engineering Technology, vol. 11, no. 1, pp. 881–884, Jan. 2023, doi: 10.22214/ijraset.2023.48508.
[10] E. Prih Raharjo, K. Risma Devi Kartika, and I. Kadek Surya Putra Adidana, “Application of Transport Demand Management in Addressing Economic Losses Due to Traffic Congestion (Case Study of Gilimanuk Port During The 2024 Eid Holiday),” Jurnal Teknologi Transportasi dan Logistik, vol. 5, no. 2, 2024.
[11] T. Afrin and N. Yodo, “A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System,” Sustainability, vol. 12, no. 11, p. 4660, Jun. 2020, doi: 10.3390/su12114660.
[12] P. K. Baskar and H. Kaluvan, “Long short-term memory (LSTM) recurrent neural network (RNN) based traffic forecasting for intelligent transportation,” 2022, p. 020039. doi: 10.1063/5.0083590.
[13] K. Elmazi, D. Elmazi, E. Musta, F. Mehmeti, and F. Hidri, “An Intelligent Transportation Systems-Based Machine Learning-Enhanced Traffic Prediction Model using Time Series Analysis and Regression Techniques*,” in 2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), IEEE, Sep. 2024, pp. 1–6. doi: 10.1109/INISTA62901.2024.10683864.
[14] T. Lv, Y. Wang, J. Yuan, Y. Wu, and L. Zhang, “Prediction and Application of Network Business Traffic based on LSTM,” in Journal of Physics: Conference Series, Institute of Physics, 2022. doi: 10.1088/1742-6596/2289/1/012011.
[15] G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artificial Intelligence Review, vol. 53, no. 8, pp. 5929–5955, Dec. 2020, doi: 10.1007/s10462-020-09838-1.
[16] S.-H. Noh, “Analysis of Gradient Vanishing of RNNs and Performance Comparison,” Information, vol. 12, no. 11, p. 442, Oct. 2021, doi: 10.3390/info12110442.
[17] Q. Wang, R.-Q. Peng, J.-Q. Wang, Z. Li, and H.-B. Qu, “NEWLSTM: An Optimized Long Short-Term Memory Language Model for Sequence Prediction,” IEEE Access, vol. 8, pp. 65395–65401, 2020, doi: 10.1109/ACCESS.2020.2985418.
[18] A. Isah et al., “Digital Twins Temporal Dependencies-Based on Time Series Using Multivariate Long Short-Term Memory,” Electronics, vol. 12, no. 19, p. 4187, Oct. 2023, doi: 10.3390/electronics12194187.
[19] D. H. Shin, K. Chung, and R. C. Park, “Prediction of Traffic Congestion Based on LSTM through Correction of Missing Temporal and Spatial Data,” IEEE Access, vol. 8, pp. 150784–150796, 2020, doi: 10.1109/ACCESS.2020.3016469.
[20] K. M. Hussain, “Optimizing Urban Traffic Flows Using Advanced Data-Driven Machine Learning Models”, Artificial Intell. Cyb., vol. 2, pp. 37–49, Aug. 2025, doi: 10.61356/j.aics.2025.2570.
[21] S. Reza, M. C. Ferreira, J. J. M. Machado, and J. M. R. S. Tavares, “Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory,” Applied Sciences (Switzerland), vol. 12, no. 10, May 2022, doi: 10.3390/app12105149.
[22] C. Ma, G. Dai, and J. Zhou, “Short-Term Traffic Flow Prediction for Urban Road Sections Based on Time Series Analysis and LSTM_BILSTM Method,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5615–5624, Jun. 2022, doi: 10.1109/TITS.2021.3055258.
[23] J. Li, G. Xu, Y. Cheng, and B. Huang, “Short-Time Traffic Flow Prediction Based on K-means++ and LSTM,” in 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), IEEE, Dec. 2022, pp. 286–293. doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00043.
[24] B. Fernandes, F. Silva, H. Alaiz-Moreton, P. Novais, J. Neves, and C. Analide, “Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches,” Informatica (Netherlands), vol. 31, no. 4, pp. 723–749, 2020, doi: 10.15388/20-INFOR431.
[25] D. Liu and A. Wei, “Regulated LSTM Artificial Neural Networks for Option Risks,” FinTech, vol. 1, no. 2, pp. 180–190, Jun. 2022, doi: 10.3390/fintech1020014.
[26] S. Ranjan, Y. C. Kim, N. Ranjan, S. Bhandari, and H. Kim, “Large-Scale Road Network Traffic Congestion Prediction Based on Recurrent High-Resolution Network,” Applied Sciences (Switzerland), vol. 13, no. 9, May 2023, doi: 10.3390/app13095512.
[27] X. Ma, Y. Li, Z. Cui, and Y. Wang, “Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models,” 2020. doi: 10.1109/TITS.2020.2984813.
[28] M. Srinath, “Vehicular Traffic Flow Prediction Model Deep Learning,” International Journal for Research in Applied Science and Engineering Technology, vol. 11, no. 7, pp. 109–112, Jul. 2023, doi: 10.22214/ijraset.2023.54576.
[29] Q. Wu, Q. Fu, and M. Nie, “Graph Wavelet Long Short-Term Memory Neural Network: A Novel Spatial-Temporal Network for Traffic Prediction,” Journal of Physics: Conference Series, vol. 1549, no. 4, p. 042070, Jun. 2020, doi: 10.1088/1742-6596/1549/4/042070.
[30] Z. Cui, K. Henrickson, S. A. Biancardo, Z. Pu, and Y. Wang, “Establishing Multisource Data-Integration Framework for Transportation Data Analytics,” Journal of Transportation Engineering, Part A: Systems, vol. 146, no. 5, May 2020, doi: 10.1061/JTEPBS.0000331.
[31] S. Ma and M. Zhao, “Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model,” Computational Intelligence and Neuroscience, vol. 2022, 2022, doi: 10.1155/2022/7079045.
[32] Z. Wang, X. Su, and Z. Ding, “Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 10, pp. 6561–6571, Oct. 2021, doi: 10.1109/TITS.2020.2995546.
[33] L. Qu, M. Zhang, Z. Li, and W. Li, “Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction,” Journal of Advanced Transportation, vol. 2020, 2020, doi: 10.1155/2020/8899478.
[34] H. Xiao, J. Xiao, Y. Shi, X. Deng, and Y. Yang, “Traffic Speed Sequence Prediction by Adaptive Weighted Long Short-Term Memory With Classification-Type Loss,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2677, no. 8, pp. 219–233, Aug. 2023, doi: 10.1177/03611981231155911.
[35] J. Bi, X. Zhang, H. Yuan, J. Zhang, and M. Zhou, “A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1869–1879, Jul. 2022, doi: 10.1109/TASE.2021.3077537.
[36] H. N. Bhandari, B. Rimal, N. R. Pokhrel, R. Rimal, and K. R. Dahal, “LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling,” Software Impacts, vol. 14, p. 100396, Dec. 2022, doi: 10.1016/j.simpa.2022.100396.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
