Added Title

Graph query language (GQL)-based geospatial intelligence: A novel approach to public transport simulated data modeling for route recommendation

Date of Publication


Document Type


Degree Name

Doctor of Philosophy in Electronics and Communications Engineering

Subject Categories

Electrical and Computer Engineering


Gokongwei College of Engineering


Electronics And Communications Engg


Outstanding Dissertation Award

Thesis Advisor

Edwin Sybingco

Defense Panel Chair

Argel Bandala

Defense Panel Member

Robert Billones
Elmer Dadios
Alexis Fillone
Raouf Naguib


Public transportation is the key economic driver of a country. The true measure of a progressive country is the number of people using the public transportation rather than of people riding private cars. In the Philippines, Western Visayas region (Region VI) is one of the regions which needs extensive support in public transport data organization. Its convoluted transport network can be attributed considerably from the multimodal nature of its public transport. Due to its complex network, data handling becomes a bottleneck for transport planners. Addressing this problem will help them move forward to much more important tasks such as improving transport service for passengers. One major factor to improve passengers’ commuting experience is the visibility of travel information such as public utility vehicle (PUV) route, distance, fare, and travel time. Provincial routes using public transit are currently not supported in Google Maps.

In this study, 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. For the strategic transit route recommendation insight, a heuristic utility function was used to take into account the desirability of multiple trip features (both quantitative and qualitative) using Logit model, and the optimal travel time with respect to a given road traffic condition, headway, and passenger demand.

With the framework and the underlying algorithms developed, the contributions of this study are: (1) make data organization scalable, (2) preconnect geospatial data, (3) analyze travel time considering in-vehicle and waiting time, and headway for trips with transfer rides, and (4) visualize results into graph and map form. Preconnecting data in public transport such as terminals, PUV stops, and facilities in conjunction with massive parallel processing (MPP) function, speeds up data analysis. This also enables expanded capability of a system to return answers to queries which need deeper insights.

Abstract Format







Vehicle routing problem; Query languages (Computer science); Transportation—Philippines— Visayan Islands; Geospatial data—Computer processing

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