Satu Matrix — Origin-Destination Analytics

Problem

Understanding commuter movement across Greater Jakarta requires combining large volumes of data from multiple public transportation operators, each with different data sources, patterns, and operational contexts. Without a unified analytical platform, transportation planners faced challenges in identifying origin-destination patterns, understanding peak travel periods, comparing transportation modes, and detecting unusual movement patterns. These limitations made it harder to use commuter data effectively when planning network expansion, infrastructure investment, and transportation policies.

Developed Satu Matrix, a web-based Origin-Destination analytics platform for the Badan Pengelola Transportasi Jabodetabek (BPTJ), Ministry of Transportation, to analyze commuter movement across the Greater Jakarta metropolitan area. The platform integrates transportation data from multiple public transit operators, including TransJakarta, MRT, LRT, KCI, and Damri, and transforms large volumes of movement data into interactive visualizations and analytical insights. Satu Matrix provides flow maps for visualizing origin-destination movements, heatmaps for identifying travel intensity by time and location, anomaly detection for identifying unusual movement patterns, and transportation-mode breakdowns for analyzing commuter behavior across different transit systems. The platform serves as a decision-support tool for transportation planners, providing a unified view of commuter movement patterns to support evidence-based decisions around public transportation planning and network development.

What I did

Developed the web-based Origin-Destination analytics platform for BPTJ.
Integrated commuter and transportation data from multiple public transit operators, including TransJakarta, MRT, LRT, KCI, and Damri.
Built data visualization components for analyzing origin-destination flows and commuter movement patterns.
Developed interactive flow maps capable of visualizing large numbers of simultaneous passenger movements between origin-destination pairs.
Implemented time- and location-based heatmaps to visualize travel intensity and identify peak movement patterns.
Developed transportation mode breakdowns to analyze commuter movement across different transit systems.
Implemented anomaly detection capabilities to highlight unusual or unexpected movement patterns.
Transformed complex and high-volume transportation datasets into interactive, decision-oriented visualizations.
Worked on geospatial visualization and location-based analytics to help planners understand commuter movements across the Jabodetabek region.
Collaborated with stakeholders to translate transportation planning requirements into analytical features and dashboard experiences.
Contributed to a decision-support platform designed to help government planners evaluate transportation patterns and plan future network development.

Outcomes

Consolidated transportation data from multiple public transit operators into a unified analytical platform.
Enabled transportation planners to visualize complex commuter movements through interactive flow maps and geospatial visualizations.
Provided greater visibility into peak travel periods, high-intensity movement areas, and origin-destination patterns.
Enabled analysis of commuter behavior across different transportation modes and operator networks.
Improved the ability to identify unusual movement patterns and potential anomalies through data-driven analysis.
Transformed large-scale transportation data into actionable visual insights for transportation planning.
Provided government planners with a centralized decision-support tool for evidence-based transportation network planning and development.

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