A Trust-Aware Modeling Framework for Jakarta Smart Transportation Open Data

Authors

DOI:

https://doi.org/10.63158/journalisi.v8i5.1785

Keywords:

Jakarta Smart Transportation Open Data, Satu Data Jakarta, trust-aware modeling, trust scoring, anomaly detection, dataset construction

Abstract

Smart transportation open data can support urban data-quality assessment and operational-risk screening, but it does not contain verified cybersecurity labels. This study develops a preliminary trust-aware modeling dataset for Jakarta Smart Transportation Open Data using nine Satu Data Jakarta sources covering TransJakarta operations and stops, road-speed observations, ATCS and CCTV locations, busway ITS corridors, traffic violations, congestion-risk points, and modal share. The sources are harmonized into a record-level schema comprising node_id, data_type, period, location, value, trust, output, source_dataset, and raw_label. Researcher-defined, source-specific rules assign trust scores from recency, completeness, active status, speed, violation intensity, and congestion-risk status. The resulting dataset contains 2,790 records: 2,508 trusted/non-malicious and 282 anomalous/untrusted proxy records. A separate baseline-repository ANN replication is reported only as a trust-threshold pipeline-consistency check, not as Jakarta model performance. The heuristic labels require expert review, sensor and incident logs, and controlled attack simulation before cybersecurity use. The contribution is therefore dataset construction and an exploratory trust-risk modeling framework rather than validated attack detection.

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References

[1] M. H. Panahi Rizi and S. A. Hosseini Seno, “A systematic review of technologies and solutions to improve security and privacy protection of citizens in the smart city,” Internet Things, vol. 20, Art. no. 100584, Nov. 2022, doi: 10.1016/j.iot.2022.100584.

[2] E. Kenneally, “Intelligent data security and privacy for smart cities,” IEEE Internet Things Mag., vol. 2, no. 3, pp. 7–9, Sep. 2019, doi: 10.1109/MIOT.2019.8950958.

[3] S. Akhlaghi, M. B. Menhaj, and B. Masoumi, “A survey of the smart city data security and privacy-preserving approaches in the Internet of Things,” in Proc. 10th Int. Conf. Artif. Intell. Robot. (QICAR), Feb. 2024, pp. 379–385, doi: 10.1109/QICAR61538.2024.10496658.

[4] R. Sari, F. Anindra, R. Kosala, B. Ranti, and S. H. Supangkat, “Model development of smart transportation using the performance measurement of smart city result with IT balance scorecard and IPA matrix: Jakarta case,” in Proc. Int. Conf. Inf. Syst. Technol., 2019, pp. 199–205, doi: 10.5220/0009907901990205.

[5] A. Apriansyah, V. A. Nora, and A. Saufi, “Smart mobility concept for determining an integrated transportation network in Jakarta,” in Proc. Int. Conf. ICT Smart Soc. (ICISS), Sep. 2024, pp. 1–6, doi: 10.1109/ICISS62896.2024.10751138.

[6] M. Owais, J. Sumabrata, and N. Yusuf, “Gender-based service quality evaluation of multimodal public transportation in DKI Jakarta,” Smart City, vol. 4, no. 2, 2024, doi: 10.56940/sc.v4.i2.3.

[7] M. A. Athaya and R. A. Sembiring, “Analisis tata kelola smart mobility di Jakarta melalui aplikasi Jakarta Kini (JAKI) oleh Jakarta Smart City tahun 2024,” J. Gov. Policy, vol. 5, no. 1, pp. 134–145, Apr. 2025, doi: 10.21776/ub.jgp.2025.005.01.8.

[8] D. B. Bellastuti and R. Fathurrahman, “Application of smart mobility in the DKI Jakarta transportation system with KRL commuter line transportation mode,” Asian J. Soc. Humanit., vol. 2, no. 3, pp. 535–542, Dec. 2023, doi: 10.59888/ajosh.v2i03.155.

[9] D. Zubaida and S. Megawati, “Implementasi aplikasi Jakarta Kini (JAKI) dalam mewujudkan smart city di Unit Pelaksana Teknis Jakarta Smart City,” Publika, vol. 11, no. 3, pp. 2231–2242, Jun. 2023, doi: 10.26740/publika.v11n3.p2231-2242.

[10] S. Khan, S. Khan, A. Sulaiman, M. S. Al Reshan, H. Alshahrani, and A. Shaikh, “Deep neural network and trust management approach to secure smart transportation data in sustainable smart cities,” ICT Express, vol. 10, no. 5, pp. 1059–1065, Oct. 2024, doi: 10.1016/j.icte.2024.08.006.

[11] K. Kharoubi, S. Cherbal, and M. Akkal, “Machine learning and deep learning for enhanced DDoS detection in IoT-based intrusion detection system,” in Proc. 1st Int. Conf. Innov. Intell. Inf. Technol. (IC3IT), Dec. 2024, pp. 1–6, doi: 10.1109/IC3IT63743.2024.10869417.

[12] S. Hizal, U. Cavusoglu, and D. Akgun, “A novel deep learning-based intrusion detection system for IoT DDoS security,” Internet Things, vol. 28, Art. no. 101336, Dec. 2024, doi: 10.1016/j.iot.2024.101336.

[13] A. S. Dina, A. B. Siddique, and D. Manivannan, “A deep learning approach for intrusion detection in Internet of Things using focal loss function,” Internet Things, vol. 22, Art. no. 100699, Jul. 2023, doi: 10.1016/j.iot.2023.100699.

[14] V. Ravi, T. D. Pham, and M. Alazab, “Deep learning-based network intrusion detection system for Internet of Medical Things,” IEEE Internet Things Mag., vol. 6, no. 2, pp. 50–54, Jun. 2023, doi: 10.1109/IOTM.001.2300021.

[15] D. Agostinello, A. Genovese, and V. Piuri, “Anomaly-based intrusion detection system for DDoS attack with deep learning techniques,” in Proc. 20th Int. Conf. Secur. Cryptogr., 2023, pp. 267–275, doi: 10.5220/0012146100003555.

[16] G. Chukwukelu, A. Essien, A. Salami, and E. Utuk, “Comparative analysis of machine learning techniques for DDoS intrusion detection in IoT environments,” in Proc. 21st Int. Conf. Smart Bus. Technol., 2024, pp. 19–27, doi: 10.5220/0012765200003764.

[17] A. Hattak, F. Martinelli, F. Mercaldo, and A. Santone, “Leveraging deep learning for intrusion detection in IoT through visualized network data,” in Proc. 21st Int. Conf. Secur. Cryptogr., 2024, pp. 722–729, doi: 10.5220/0012768400003767.

[18] J. J. Shirley and M. Priya, “A comprehensive survey on ensemble machine learning approaches for detection of intrusion in IoT networks,” in Proc. Int. Conf. Innov. Eng. Technol. (ICIET), Jul. 2023, pp. 1–10, doi: 10.1109/ICIET57285.2023.10220795.

[19] H. M. Kelagadi, J. Chandra Sekhar, R. Rawat, N. Dhaliwal, S. Singh, and B. S. Saravana, “Misbehavior detection in the Internet of Vehicles using machine learning based situation identification,” in Proc. 8th Int. Conf. Commun. Electron. Syst. (ICCES), Jun. 2023, pp. 975–983, doi: 10.1109/ICCES57224.2023.10192779.

[20] A. Mahmood, Q. Z. Sheng, W. E. Zhang, Y. Wang, and S. Sagar, “Toward a distributed trust management system for misbehavior detection in the Internet of Vehicles,” ACM Trans. Cyber-Phys. Syst., vol. 7, no. 3, pp. 1–25, Jul. 2023, doi: 10.1145/3594637.

[21] Y. Zhang, C. Lv, C. Cheong, and Y. Cao, “An introduction to trust management in Internet of Vehicles,” in Communication, Computation and Perception Technologies for Internet of Vehicles. Singapore: Springer, 2023, pp. 245–260, doi: 10.1007/978-981-99-5439-1_13.

[22] A. Mahmood, Q. Z. Sheng, W. E. Zhang, and S. Yongchareon, “Trust–IoV: A distributed trust management system for misbehavior detection in the Internet of Vehicles,” in Trust Management in the Internet of Vehicles. Boca Raton, FL, USA: Chapman and Hall/CRC, 2023, pp. 33–66, doi: 10.1201/9781003365037-3.

[23] A. Mahmood, Q. Z. Sheng, W. E. Zhang, and S. Yongchareon, “Towards secure and resource efficient IoV networks: A hybrid trust management approach,” in Trust Management in the Internet of Vehicles. Boca Raton, FL, USA: Chapman and Hall/CRC, 2023, pp. 67–92, doi: 10.1201/9781003365037-4.

[24] R. Purkait, S. Tripathi, and S. Patnaik, “Location verification and stability with trust management for avoiding malicious vehicles in geographic routing for VANET,” in Proc. Smart City Challenges Outcomes Urban Transform. (SCOUT), Dec. 2021, pp. 243–248, doi: 10.1109/SCOUT54618.2021.00058.

[25] L. Mendiboure, M. A. Chalouf, and F. Krief, “Towards a blockchain-based SD-IoV for applications authentication and trust management,” in Internet of Vehicles—Technologies and Services, Lecture Notes in Computer Science, vol. 11253. Cham, Switzerland: Springer, 2018, pp. 265–277, doi: 10.1007/978-3-030-05081-8_19.

[26] Z. Chen et al., “UITDE: A UAV-assisted intelligent true data evaluation method for ubiquitous IoT systems in intelligent transportation of smart city,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 8, pp. 9597–9607, Aug. 2024, doi: 10.1109/TITS.2024.3373411.

[27] Aarushi, A. Chauhan, S. Wadhawan, A. Kaur, and S. Sharma, “The convergence of blockchain and IoT in secure smart cities: A survey of applications, challenges, and opportunities,” Secur. Priv., vol. 8, no. 3, May 2025, doi: 10.1002/spy2.70046.

[28] A. Jabbari, “Securing smart transportation: Blockchain and IoT for sustainable cities,” in Proc. IEEE Int. Smart Cities Conf. (ISC2), Oct. 2024, pp. 1–5, doi: 10.1109/ISC260477.2024.11004209.

[29] R. Qamar, S. Siraj, and B. A. Zardari, “Cyber security for smart transportation system,” in Driving Green Transportation System Through Artificial Intelligence and Automation. Cham, Switzerland: Springer, 2025, pp. 619–636, doi: 10.1007/978-3-031-72617-0_32.

[30] C. Korkuc, Y. Genc, and E. Afacan, “Blockchain technology and cyber security in intelligent transportation systems,” in AIoT for Smart Transportation. United Kingdom: Institution of Engineering and Technology, 2025, pp. 215–234, doi: 10.1049/PBTR047E_ch11.

[31] “Data penindakan pelanggaran lalu lintas dan angkutan jalan tahun 2017,” Satu Data Jakarta. Accessed: Aug. 24, 2026. [Online]. Available: https://satudata.jakarta.go.id/open-data/detail?kategori=dataset&page_url=data-penindakan-pelanggaran-lalu-lintas-dan-angkutan-jalan-tahun-2017&data_no=1

[32] “Kecepatan rata-rata di 41 koridor jalan utama pada jam sibuk,” Satu Data Jakarta. Accessed: Aug. 24, 2026. [Online]. Available: https://satudata.jakarta.go.id/open-data/detail?kategori=dataset&page_url=kecepatan-rata-rata-di-41-empat-puluh-satu-koridor-jalan-utama-pada-jam-sibuk

[33] “Portal Satu Data Jakarta—Data terbuka DKI Jakarta,” Satu Data Jakarta, 2026. [Online]. Available: https://satudata.jakarta.go.id

[34] “Data halte bus Transjakarta,” Satu Data Indonesia. Accessed: Aug. 24, 2026. [Online]. Available: https://data.go.id/dataset/dataset/data-halte-bus-transjakarta

[35] “Data lokasi lampu lalu lintas ATCS terintegrasi aktif tahun 2022,” Satu Data Indonesia. Accessed: Aug. 24, 2026. [Online]. Available: https://data.go.id/dataset/dataset/data-lokasi-lampu-lalu-lintas-atcs-atcs-terintegrasi-aktif-tahun-2022

[36] “Data pemetaan lokasi CCTV Dinas Perhubungan Provinsi DKI Jakarta,” Satu Data Indonesia. Accessed: Aug. 24, 2026. [Online]. Available: https://data.go.id/dataset/dataset/data-pemetaan-lokasi-cctv-dinas-perhubungan-provinsi-dki-jakarta

[37] “Data jumlah bus yang beroperasi dan jumlah penumpang layanan Transjakarta,” Satu Data Indonesia. Accessed: Aug. 24, 2026. [Online]. Available: https://data.go.id/dataset/dataset/data-jumlah-bus-yang-beroperasi-dan-jumlah-penumpang-layanan-transjakarta

[38] “Data lampu lalu lintas terintegrasi Intelligent Transport System Area Traffic Control System (ITS ATCS),” Satu Data Indonesia. Accessed: Aug. 24, 2026. [Online]. Available: https://data.go.id/dataset/dataset/data-lampu-lalu-lintas-terintegrasi-intelligent-transport-system-area-traffic-control-system-it

[39] “Data titik rawan kemacetan di DKI Jakarta,” Satu Data Indonesia. Accessed: Aug. 24, 2026. [Online]. Available: https://data.go.id/dataset/dataset/data-titik-rawan-kemacetan-di-dki-jakarta

[40] “Persentase perjalanan penduduk menggunakan moda transportasi publik,” Satu Data Indonesia. Accessed: Aug. 24, 2026. [Online]. Available: https://data.go.id/dataset/dataset/persentase-perjalanan-penduduk-menggunakan-moda-transportasi-publik

[41] A. Attiaoui, M. Elmachkour, A. Kobbane, M. Ayaida, and H. Tembine, “Trust-aware and game-theoretic cooperative detection of misbehavior in connected vehicles,” Internet Things, vol. 34, Art. no. 101799, Nov. 2025, doi: 10.1016/j.iot.2025.101799.

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Published

2026-10-24

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Vol. 8 No. 5 (2026): October

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How to Cite

[1]
S. Widyarto, Riki, S. S. O. Al-Hinai, and D. Soomro, “A Trust-Aware Modeling Framework for Jakarta Smart Transportation Open Data”, journalisi, vol. 8, no. 5, pp. 5832–5859, Oct. 2026, doi: 10.63158/journalisi.v8i5.1785.