Irenbus - a real-time machine learning based public transport management system.
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Date
2020
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Abstract
The era of Big Data and the Internet of Things is upon us, and it is time for
developing countries to take advantage of and pragmatically apply these ideas to
solve real-world problems. As the saying goes, "data is the new oil" - we believe that
data can be used to power the transportation sector the same way traditional oil
does. Many problems faced daily by the public transportation sector can be resolved
or mitigated through the collection of appropriate data and application of predictive
analytics. In this body of work, we are primarily focused on problems affecting
public transport buses. These include the unavailability of real-time information to
commuters about the current status of a given bus or travel route; and the inability
of bus operators to efficiently assign available buses to routes for a given day based
on expected demand for a particular route. A cloud-based system was developed to
address the aforementioned. This system is composed of two subsystems, namely a
mobile application for commuters to provide the current location and availability of
a given bus and other related information, which can also be used by drivers so that
the bus can be tracked in real-time and collect ridership information throughout
the day, and a web application that serves as a dashboard for bus operators to
gain insights from the collected ridership data. These were all developed using
the Firebase Backend- as-a-Service (BaaS) platform and integrated with a machine
learning model trained on collected ridership data to predict the daily ridership for a
given route. Our novel system provides a holistic solution to problems in the public
transport sector, as it is highly scalable, cost-efficient and takes full advantage of
the currently available technologies in comparison with other previous work in this
topic.
Description
Masters Degree. University of KwaZulu-Natal, Durban.