An Intelligent Machine Learning-Based Real-Time Public Transport System

Skhosana, Menzi; Ezugwu, Absalom E.; Rana, Nadim; Abdulhamid, Shafií Muhammad

doi:10.1007/978-3-030-58817-5_47

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…This system is accessible in the form of a mobile application for bus users and drivers, and a web application for bus operators [14]. Second, an approach to incorporate machine learning models into the system to enable a deeper understanding of the collected data and foresight [15]. Third, an approach to efficiently collect daily ridership data -unlike some previous work in this topic [16] -through the use of only a mobile application without the use of cumbersome hardware modules.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

Skhosana¹,

Ezugwu²

2021

Preprint

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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. 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 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.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

Skhosana¹,

Ezugwu²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This system is accessible in a mobile application for bus users and drivers and a web application for bus operators [14]. The second is an approach to better incorporate machine learning models into the system to understand the collected data and foresight [15]. Third, an approach to efficiently collect daily ridership data, unlike some previous work in this topic [16], that uses only a mobile application without using cumbersome hardware modules.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Machine Learning-Based Public Transport Bus-Passenger Information System

Skhosana¹,

Ezugwu²

2021

IJSDP

Self Cite

View full text Add to dashboard Cite

The problems faced daily by the public transportation sector can be addressed or mitigated by collecting appropriate data and applying predictive analytics. This paper primarily focuses on problems affecting the 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 is developed to address the aforementioned issues. The proposed system comprises two subsystems, namely mobile and web applications interfaces. The mobile application interface provides commuters with the current location and availability of a given bus and other related information, and it is also used by drivers so that the bus can be tracked in real-time and collect ridership information throughout the day. Moreover, the web application serves as a dashboard for bus operators to gain insights from the collected ridership data. The new integrated system was 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. The novel system provides a holistic solution to problems in the public transport sector. It is highly scalable, cost-efficient, and takes full advantage of the current technologies compared to other related application platforms.

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Artificial intelligence for improving public transport: a mapping study

Jevinger,

Zhao,

Persson

et al. 2023

Public Transp

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The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

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An Intelligent Machine Learning-Based Real-Time Public Transport System

Cited by 6 publications

References 11 publications

A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

A Real-Time Machine Learning-Based Public Transport Bus-Passenger Information System

Artificial intelligence for improving public transport: a mapping study

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