Integrated Data Visualization Dashboard as a Decision Support System in Monitoring Overdimensional and Overload Violations in Indonesia

Agus Sugianto; Yuli Adam Prasetyo; Yolanda Hafitzhah

doi:10.70184/f8rkh413

Authors

Agus Sugianto Information Systems Study Program, Faculty of Industrial Engineering, Telkom University, Bandung https://orcid.org/0009-0005-0480-6882
Yuli Adam Prasetyo Information Systems Study Program, Faculty of Industrial Engineering, Telkom University, Bandung
Yolanda Hafitzhah Information Systems Study Program, Faculty of Industrial Engineering, Telkom University, Bandung

Data Visualization Dashboard, Decision Support System, Over Dimension Over Load, Weigh in Motion, Intelligent Transportation System

Abstract

Purpose: Over Dimension Over Load (ODOL) violations in highway-based freight transportation in Indonesia contribute to infrastructure damage worth Rp43 trillion per year and rank as the second largest cause of national traffic accidents, yet existing monitoring remains partial, fragmented across agencies, and unable to support real-time, data-driven decisions. This study aims to advance understanding of multi-source data integration patterns, analytic design principles, and hierarchical interface architectures that together constitute a conceptual Decision Support System (DSS) framework for comprehensive ODOL surveillance in Indonesia.

Research Design and Methodology: The method used is a Systematic Literature Review following the PRISMA protocol, sourced from Scopus, producing 30 indexed articles meeting inclusion criteria within 2022 to 2026.

Findings and Discussion: The synthesis resulted in an architectural model, the Integrated ODOL Surveillance Dashboard (IOSD), comprising three functional layers: a multi-source data acquisition and integration layer; an analytics layer with violation detection, machine learning-based prediction, spatial analytics, and model interpretability modules; and an interactive visualization layer customized by user role. The model integrates Weigh in Motion, Automatic Number Plate Recognition, AI-based computer vision, and geographic information system data into a single real-time DSS platform spanning field operations to national policy formulation. Comparative insights from international heavy-vehicle monitoring practices further inform the model's institutional design.

Implications: This research contributes a digital, accurate, cross-agency technical framework for ODOL supervision and opens a follow-up agenda for empirical validation of the IOSD prototype under real operational conditions within Indonesia's UPPKB network.

Author Biographies

Agus Sugianto, Information Systems Study Program, Faculty of Industrial Engineering, Telkom University, Bandung

With over 18 years of experience in the IT industry, I specialize in financial technology, telecommunications, AI, and IoT. I have a proven track record of leading innovative projects and integrating cutting-edge technologies to drive business growth and operational efficiency. My expertise includes IT infrastructure, fintech solutions, AI and machine learning applications, and IoT technology integration. Recognized for my leadership and strategic vision, I am passionate about leveraging technology to solve complex problems and create value.

Yuli Adam Prasetyo, Information Systems Study Program, Faculty of Industrial Engineering, Telkom University, Bandung

Y. Adam Prasetyo serves as an Associate Professor at Telkom University, where he is affiliated with the Digital Enterprise System and Technology Research Group and the Center of Excellence (CoE) Smart City. Drawing on more than 15 years of research experience, his work focuses on the intersection of technology and urban development, specifically in smart city architecture frameworks and platform development. In addition to his academic research, Prasetyo maintains a strong professional footprint by leading Enterprise Architecture and IT Governance initiatives for various private sector companies.

References

A. Widyanti, J. M. Gananda, T. Yudhistira, W. Weningtyas, L. P. Bowo, and S. Nugoroho, “Over-Dimension and Over-Load (ODOL) truck in highways: Prevalence and modeling intention to operate ODOL truck, lesson learned from Indonesia,” Transp. Res. Interdiscip. Perspect., vol. 29, p. 101320, 2025, doi: https://doi.org/10.1016/j.trip.2024.101320.

E. Abdurachman, A. A. Suryobuwono, S. Sholihah, and L. Lesmini, “Rethinking Freight Transport: A Systemic Policy Model for Curbing Over Dimension Over Load (ODOL) in Indonesia,” Siber J. Transp. Logist., vol. 3, pp. 142–157, Nov. 2025, doi: 10.38035/sjtl.v3i3.565.

A. Kurniawan and S. Munandar, “Pengaruh Penindakan Pelanggaran Kendaraan Overdimensi Dan Overloading Terhadap Angka Kecelakaan Lalu Lintas Di Wilayah Hukum Polresta Bukittinggi (Studi Kasus Polresta Bukittinggi),” J. Huk. Yustisi, vol. 13, no. 1, pp. 500–513, 2026, [Online]. Available: https://doi.org/10.32832/yustisi.v13i1.22733

W. Suntoro, S. Maemunah, M. Sari, D. Subastian, and I. Ilham, “Strategies to Enhance Supervision and Control of Freight Transport in Over Dimension Over Loading (ODOL),” Ilomata Int. J. Manag., vol. 7, no. 2, pp. 871–875, Apr. 2026, doi: 10.61194/ijjm.v7i2.2128.

A. R. Asrib, M. J. Rahman, M. S. Sitorus, H. Rahman, and M. R. Hasrul, “Evaluasi Kepatuhan Kendaraan Terhadap Peraturan Overdimension Overloading di UPPKB PallanggaEvaluasi Kepatuhan Kendaraan Terhadap Peraturan Overdimension Overloading di UPPKB Pallangga,” J. Tek. Sipil MACCA, vol. 11, no. 1, pp. 53–62, 2026, [Online]. Available: https://doi.org/10.33096/5ws0fr44

J. Ramadani, H. Hakzah, and A. Andriyani, “Analisis Faktor Penyebab Pelanggaran Overdimesion Overloading (ODOL) Pada Moda Angkutan Barang (Studi Kasus: UPPKB Datae dan Maccopa),” J. Apl. Tek. Sipil, vol. 24, no. 1, pp. 61–68, 2026.

J. A. Pratama and A. Susanti, “Analisis Penanganan Permasalahan Overdimension Overloading di Ruas Jalan Mojokerto–Surabaya,” J. Media Publ. Terap. Transp., vol. 1, no. 1, pp. 203–212, 2023, [Online]. Available: https://doi.org/10.26740/mitrans.v1n2.p203-212

Y. Ma, A. Amiri, E. Hassini, and S. Razavi, “Transportation data visualization with a focus on freight: a literature review,” Transp. Plan. Technol., vol. 45, no. 4, pp. 358–401, 2022, [Online]. Available: https://doi.org/10.1080/03081060.2022.2111430

A. Muliasari, Y. Karyanto, D. R. Insiyanda, and R. Marlia, “Potensi kecelakaan kendaraan over dimension/overloading (odol) pada area tikungan berdasarkan persentase berat muatan dan kondisi alinyemen horizontal suatu area jalan,” J. Baruna Horiz., vol. 5, no. 2, pp. 109–117, 2022, [Online]. Available: https://doi.org/10.52310/jbhorizon.v5i2.89

S. Febriani and M. Mintarsih, “Penegakan Hukum Dalam Kebijakan Zero Overdimension dan Overloading Terhadap Pengangkutan Barang: Law Enforcement in Zero Over Dimension and Over Loading Policy for Transportation of Goods,” Reformasi Huk., vol. 27, no. 2, pp. 42–50, Apr. 2023, doi: 10.46257/jrh.v27i1.603.

J. Gajda, P. Burnos, R. Sroka, and M. Daniol, “Accuracy Maps of Weigh-In-Motion Systems for Direct Enforcement,” Electron., vol. 12, no. 7, 2023, doi: 10.3390/electronics12071621.

B. Szinyéri and B. Kovari, “Handling data loss caused by strain gauge failure in Bridge Weigh-in-Motion systems using deep learning,” Neural Comput. Appl. (, vol. 38, 2026, [Online]. Available: https://doi.org/10.1007/s00521-026-11895-6

M. T. Obaidat and L. D. Alomari, “Real-time multi-source AI, computer vision, and GIS fusion for data-driven urban traffic management,” Transp. Res. Interdiscip. Perspect., vol. 36, no. November 2025, 2026, doi: 10.1016/j.trip.2026.101895.

L. Delfini, B. Spahiu, and G. Vizzari, “Visual Analytics for Sustainable Mobility: Usability Evaluation and Knowledge Acquisition for Mobility-as-a-Service (MaaS) Data Exploration,” Digital, vol. 4, no. 4, pp. 821–845, 2024, doi: 10.3390/digital4040041.

J. González-Villa et al., “Decision-support system for safety and security assessment and management in smart cities,” Multimed. Tools Appl., vol. 83, no. 22, pp. 61971–61994, 2024, doi: 10.1007/s11042-023-16020-6.

M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” bmj, vol. 372, 2021.

S. Kraus et al., “Literature reviews as independent studies: guidelines for academic practice,” Rev. Manag. Sci., vol. 16, no. 8, pp. 2577–2595, 2022, doi: 10.1007/s11846-022-00588-8.

S. C. Simamora, V. Gaffar, and M. Arief, “Systematic literatur review dengan metode prisma: dampak teknologi blockchain terhadap periklanan digital,” J. Ilm. M-Progress, vol. 14, no. 1, pp. 1–11, 2024, [Online]. Available: https://doi.org/10.35968/m-pu.v14i1.1182

A. Rahmalia, “Evaluasi Penerapan Sistem Informasi Manajemen pada Rumah Sakit Menggunakan Metode HOT-Fit: Tinjauan Pustaka Sistematis,” J. Ilm. IT CIDA, vol. 9, no. 2, pp. 85–96, 2023, [Online]. Available: https://doi.org/10.55635/jic.v9i2.193

M. Arnold, M. Goldschmitt, and T. Rigotti, “Dealing with information overload: a comprehensive review,” Front. Psychol., vol. 14, no. June, 2023, doi: 10.3389/fpsyg.2023.1122200.

R. Munadi, D. N. Ramadan, Sussi, N. Fitriyanti, and H. H. Nuha, “Ensemble Machine Learning Approach for Traffic Congestion and Travel Time Prediction in Urban Bus Rapid Transit Systems: A Case Study of Trans Metro Bandung,” Internet of Things, vol. 7, no. 1, pp. 1–19, 2026, doi: 10.3390/iot7010022.

J. Gajda, R. Sroka, P. Burnos, and M. Daniol, “Long-Term Assessment of the Properties of Load Sensors Applied in Weigh-in-Motion Systems,” Sensors, vol. 25, no. 8, pp. 1–17, 2025, doi: 10.3390/s25082421.

A. Ryguła, K. Brzozowski, M. Grygierek, and A. Socha, “Analysis of Stability and Variability in Sensor Readings from a Vehicle Weigh-in-Motion Station,” Sensors, vol. 24, no. 24, 2024, doi: 10.3390/s24248178.

N. Ravichandran, D. Losanno, M. R. Pecce, and F. Parisi, “Site-specific traffic modelling and simulation for a major Italian highway based on weigh-in-motion systems accounting for gross vehicle weight limitations,” J. Civ. Struct. Heal. Monit., vol. 14, no. 8, pp. 1739–1763, 2024, doi: 10.1007/s13349-024-00809-6.

S. Zhao, J. Yang, Z. Tang, Q. Li, and Z. Xing, “Methodological Study on the Influence of Truck Driving State on the Accuracy of Weigh-in-Motion System,” Inf., vol. 13, no. 3, 2022, doi: 10.3390/info13030130.

S. Jiang, Y. Sun, W. Wong, Y. Xu, and X. Zhao, “Real-Time Urban Traffic Monitoring Using Transit Buses as Probes,” Transp. Res. Rec., vol. 2679, no. 2, pp. 219–236, 2025, doi: 10.1177/03611981241260708.

Z. Feng et al., “TrafPS: A shapley-based visual analytics approach to interpret traffic,” Comput. Vis. Media, vol. 10, no. 6, pp. 1101–1119, 2024, doi: 10.1007/s41095-023-0351-7.

I. Tsouros et al., “From Raw Data to Informed Decisions: The Development of an Online Data Repository and Visualization Dashboard for Transportation Data,” Procedia Comput. Sci., vol. 231, no. 2023, pp. 161–167, 2024, doi: 10.1016/j.procs.2023.12.188.

D. Baltzer, J. H. Haunert, and A. Forsch, “Visualizing the Influence of New Public Transport Infrastructure on Travel Times,” KN - J. Cartogr. Geogr. Inf., vol. 74, no. 2, pp. 107–119, 2024, doi: 10.1007/s42489-024-00167-9.

Z. Deng et al., “Visual comparative analytics of multimodal transportation,” Vis. Informatics, vol. 9, no. 1, pp. 18–30, 2025, doi: 10.1016/j.visinf.2025.01.001.

T. T. Nguyen, S. C. Calvert, G. Li, and H. van Lint, “Interpretable Representation and Customizable Retrieval of Traffic Congestion Patterns Using Causal Graph-Based Feature Associations,” Data Sci. Transp., vol. 6, no. 3, pp. 1–18, 2024, doi: 10.1007/s42421-024-00106-0.

A. M. Alobaidallah, A. Alqahtany, and K. M. Maniruzzaman, “Safety Effectiveness of Automated Traffic Enforcement Systems: A Critical Analysis of Existing Challenges and Solutions,” Futur. Transp., vol. 5, no. 1, 2025, doi: 10.3390/futuretransp5010025.

M. H. Callefi, G. M. D. Ganga, M. Godinho Filho, E. Ribeiro da Silva, L. Osiro, and V. Reis, “A roadmap for prioritising technology-enabled capabilities in road freight transportation management,” Int. J. Logist. Res. Appl., vol. 29, no. 1, pp. 71–96, 2026, doi: 10.1080/13675567.2024.2389069.

J. Gao, H. Liu, and Y. Zhang, “Intelligent traffic safety cloud supervision system based on Internet of vehicles technology,” Electron. Res. Arch., vol. 31, no. 11, pp. 6564–6584, 2023, doi: 10.3934/ERA.2023332.

M. P. M. Hagmanns, S. Lamberty, A. Fazekas, and M. Oeser, “Enhancing Weigh-in-Motion Systems Accuracy by Considering Camera-Captured Wheel Oscillations,” Sensors, vol. 24, no. 24, pp. 1–17, 2024, doi: 10.3390/s24248151.

M. H. Callefi, S. Y. Shiramizu, L. A. De Carvalho, M. Thürer, S. L. Cotrim, and L. D. Del Rosso Calache, “Exploring barriers to ICT adoption in road freight operations,” Procedia Comput. Sci., vol. 253, pp. 1702–1711, 2025, doi: 10.1016/j.procs.2025.01.232.

Y. Jung and J. Lee, “Optimal Weigh-in-Motion Planning for Multiple Stakeholders,” Systems, vol. 12, no. 12, 2024, doi: 10.3390/systems12120557.

Y. Jung, D. Mizutani, and J. Lee, “Weigh-In-Motion Placement for Overloaded Truck Enforcement Considering Traffic Loadings and Disruptions,” Sustain., vol. 17, no. 3, pp. 1–14, 2025, doi: 10.3390/su17030826.

V. K. Adigopula and A. J. B. Kumar, “Investigating the effect of overloaded vehicles on asphalt pavement: a case study in India,” Discov. Civ. Eng., vol. 2, no. 1, 2025, doi: 10.1007/s44290-025-00263-3.

P. T. Lam et al., “A vision-based weigh-in-motion approach for vehicle load tracking and identification,” Comput. Civ. Infrastruct. Eng., vol. 40, no. 11, pp. 1466–1489, 2025, doi: 10.1111/mice.13461.

D. Li, M. Liu, L. Yang, H. Wei, and J. Guo, “A non-contact identification method of overweight vehicles based on computer vision and deep learning,” Comput. Civ. Infrastruct. Eng., vol. 39, no. 22, pp. 3452–3476, 2024, doi: 10.1111/mice.13299.

X. Liu, Y. Li, L. Sizemore, X. Xie, and J. Wu, “A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 10, pp. 13323–13338, 2024, doi: 10.1109/TITS.2024.3431452.

M. Miftahulkhair, M. Z. Arifin, and F. R. Sutikno, “Revealing the Impact of Losses on Flexible Pavement Due To Vehicle Overloading,” Eastern-European J. Enterp. Technol., vol. 2, no. 1(128), pp. 55–63, 2024, doi: 10.15587/1729-4061.2024.299653.

H. Wen, Y. Du, Z. Chen, and S. Zhao, “Analysis of Factors Contributing to the Injury Severity of Overloaded-Truck-Related Crashes on Mountainous Highways in China,” Int. J. Environ. Res. Public Health, vol. 19, no. 7, 2022, doi: 10.3390/ijerph19074244.

H. Yang, Y. Yang, G. Zhao, Y. Guo, and L. Wang, “Development and Temperature Correction of Piezoelectric Ceramic Sensor for Traffic Weighing-In-Motion,” Sensors, vol. 23, no. 9, 2023, doi: 10.3390/s23094312.