Grab-Posisi - Southeast Asia’s First Comprehensive GPS Trajectory Dataset

Summary

Grab released Grab-Posisi, a GPS trajectory dataset covering Singapore and Jakarta designed to overcome the poor coverage, low sampling rates, and missing contextual metadata common in existing public datasets. Captured in April 2019 from drivers in transit, the dataset comprises 84,000 trajectories, over 80 million GPS pings, and more than 1 million kilometres recorded at a one-second sampling frequency. Each entry in the 2 GB Apache Parquet dataset records geographic coordinates, timestamp, accuracy radius, bearing, speed, mobile operating system, and vehicle mode. To protect privacy, driver personal information is encrypted and trip start and end locations are removed. The data enables applications including automated road network reconstruction, map matching, real-time traffic forecasting, and municipal infrastructure planning.

Context

Existing GPS datasets often suffer from poor spatial coverage, low sampling rates, outdated records, and a lack of contextual attributes like accuracy level, bearing, and speed, demanding substantial data cleaning.

Approach / What changed

Grab published Grab-Posisi, an anonymised 2 GB Apache Parquet dataset containing 84,000 trajectories recorded at one-second intervals with rich contextual attributes, device labels, and transport mode classifications.

Takeaways

• Grab-Posisi contains 84,000 trajectories covering over 1 million kilometres with an average trip duration of 21.5 minutes and a one-second sampling rate.
• Each GPS ping includes contextual attributes such as horizontal accuracy radius, travel bearing relative to true north, speed, device operating system (Android or iOS), and vehicle mode (Car or Motorcycle).
• Driver personal data is encrypted and true trip start and end coordinates are removed to maintain driver privacy.

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