Development of transilytics: A real-time, 3D web GIS-based passenger flow visualization and analysis system for transit planning

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The primary objective of the project is to develop an online transit data collection and analytics application, called Transilytics, to assist urban transit planning and operation. Transilytics will provide a low-cost real-time solution to data collection, visualization, analysis, and prediction of transit passengers' travel patterns, which are important information for evidence-based decision making in urban transit planning and operation. As an Ontario-based for-profit company, Physicalytics develops proprietary sensors and cloud-based data analytics platforms to monitor human behaviour and provide valuable and actionable insights about human interactions in physical locations. While the company has been growing steadily in several niche business areas, Physicalytics is now looking for new business opportunities in the urban transit industry. Its plug-and-play sensors and online dashboard platform can be used to collect transit passengers' travel data in real time. To turn this data collection capacity into a real business opportunity, Physicalytics is seeking a web GIS-based application that would allow real-time visualization and analysis of the travel data and further support transit travel pattern analysis and forecasting. The proposed research represents a perfect research collaboration between Physicalytics, who provides the hardware solution, and Ryerson University, who provides expertise in data visualization, transit data analytics, and transit planning and operation. The first key research challenge of the project is to develop a cost-effective way to anonymously track passengers' engagement on buses due to privacy concerns. To meet this challenge, proprietary sensors provided by Physicalytics will be reconfigured and placed in transit buses to anonymously collect passengers' boarding time, duration of staying on bus and spatial distribution inside the bus from passengers' smartphone signals. The second key research challenge is to develop a novel web GIS-based application that translates such travel