Graph Database-modelled Public Transportation Data for Geographic Insight Web Application

Abstract

Public transportation is the key economic driver of a country. The true measure of a country's progress level is scaled on 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. Due to the complexity of a public transport network, handling of big data becomes a bottleneck for transport planners. Addressing this problem will help them move forward to more important tasks such as improving transport service for passengers. In this study, a framework was designed in modeling public transportation data. TigerGraph database was utilized to preconnect data and to allow acquisition of geospatial intelligence on route while Django-python was used as the web framework for the geographic insight web application. With the framework and software solution developed, the study intended to make data organization scalable, visualize data relationships, and preconnect data. 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 analysis.