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

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ID: 286003
2022
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Abstract
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.
Reference Key
persistent_1760657288_68f17f88033dc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Guillermo, Marielet A.
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdd_ece/4
Keywords Keywords not found

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