Explore smart transportation open data trust modeling through a published research reference related to Transportation & Urban / Traffic Engineering. This M.Tech resource summarizes the available source and suggests questions to validate before choosing an implementation.
Smart transportation open data trust modeling: source and project details
| Research overview |
This project guide shows how to build and test a trust‑aware model for smart‑transportation open data, using Jakarta as an example. Open‑data portals like **Satu Data Jakarta** give useful information for city planning and traffic control, but they do not include verified cybersecurity or integrity labels. **What the study does** – It combines nine different open‑data sources into one consistent record format.
The sources are: • TransJakarta operations data • Road‑speed measurements • ATCS/CCTV locations • Busway ITS corridor data • Traffic‑violation records • Congestion‑risk points • Modal‑share statistics – The unified schema records the following fields for each entry: `node_id, data_type, period, location, value, trust, output, source_dataset, raw_label`. – Trust scores are calculated with researcher‑defined, source‑specific heuristic rules. The rules consider factors such as: • How recent the data are • Whether the data are complete • If the source is currently active • Speed values • Intensity of violations • Congestion‑risk status – The final dataset contains **2,790 records**.
Of these, **2,508** are labeled as trusted or non‑malicious, and **282** are marked as anomalous or untrusted proxies. – An artificial neural network (ANN) is run on the data to check that the processing pipeline works consistently. **Why it matters for M.Tech students** – It provides a clear step‑by‑step method for merging diverse data sets. – It shows how to create heuristic rules that assign trust values. – It offers a practical example of exploratory trust‑risk modeling for urban transportation systems.
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| Reference Paper |
A Trust-Aware Modeling Framework for Jakarta Smart Transportation Open Data |
| Publication year (source record) |
2026 |
| Source document classification |
Research reference (type not independently verified) |
| Discovered via |
OpenAlex metadata |
| Authors (source metadata) |
Setyawan Widyarto, Riki, Sulaiman Said Omar Al-Hinai, Dur Soomro |
| Publication type (source metadata) |
article |
| Journal / conference (source metadata) |
Journal of Information Systems and Informatics |
| DOI (from source metadata) |
10.63158/journalisi.v8i5.1785 |
| Metadata consistency check |
Publisher landing-page metadata compared with indexed record; original PDF text not independently verified. |
| Evidence reviewed by generator |
Indexed abstract/description only; original full text, author names in PDF and methods require editorial verification. |
| Domain |
Civil Engineering |
| Sub-Domain |
Transportation & Urban / Traffic Engineering |
| Research source |
View research source |
| Get Help |
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Project or research direction
This is a source-backed research starting point, not a tested, ready-made student project. Any implementation requires independent feasibility, safety and results verification.
Possible tools or components to evaluate
Python, Pandas, NumPy, Scikit-learn, TensorFlow/Keras, GIS tools (QGIS/ArcGIS)
Suggested verification steps
- Read the cited paper and check which methods, parameters and results it actually reports.
- Define a measurable project objective and list equipment or software you can access.
- Validate the proposed approach independently before claiming results, working code or experimental success.
Implementation ideas above are planning suggestions, not claims that the cited researchers built or tested those exact extensions.
Explore related M.Tech project topics or browse the engineering resource library for different methods and reference papers.
