Graph Query Language (GQL)-structured Algorithms for Geospatial Intelligence on Public Transportation

Massive data are generated and processed every day. Insights from this big data make significant difference in one's way of living. But insight is one thing and intelligence is another. To draw out an intelligence, one must be able to discern patterns and deeper relationships among data. Intelligence on the road comes in various forms and applies in different segments of the public transportation. This study focused on geospatial intelligence that will benefit the passengers in their commuting experience with the intention of restoring their confidence in using the mass transit. A bi-directional unweighted path cost search and geodesic distance priority algorithms were structured using graph query language (GQL) to query the framework developed using TigerGraph database for: public transit routes from a given source and destination locations, and facilities within the specified distance from a location, respectively. Graph database was selected because it naturally represents geospatial data, and it focuses on relationships. To visualize the intelligence obtained, output was overlaid into maps and exhibited through Jupyter notebook. The goal of this study is to empower the public transport sector with geospatial intelligence by introducing and proving the viability of developing GQL-structured algorithms in answering its persistent priority areas.