Grab-Posisi-L: A Labelled GPS Trajectory Dataset for Map Matching in Southeast Asia

Abstract

Map matching has long been a fundamental yet challenging problem. However, there are currently only a few public small-scale map matching benchmark datasets. Both the GPS trajectories and the road network in the existing map matching datasets are represented by location only, which cannot support the development of data-driven and semantic-enriched map matching algorithms that have increasingly emerged in recent years. To bridge the gap, we present the first large-scale attribute-rich map matching benchmark dataset covering two cities in Southeast Asia (i.e., Singapore and Jakarta). Our GPS trajectories contain rich contextual information including the accuracy level, bearing, speed, and transport mode in addition to the latitude and longitude geo-coordinates. The underlying road network is a snapshot of the OpenStreetMap where roads are associated with rich attributes such as road type, speed limit, etc. To ensure the quality of our dataset, the annotation of the map-matched routes has been conducted by a team of professional map operators. Analysis on our dataset provides new insights into the challenges and opportunities in map matching algorithms.