No abstract

More often than not, commuters are left stranded at pick-up spots – clueless about the availability and proximity of public transport vehicles hence the stigma of public transport being unreliable, especially in developing countries. This is a result of poorly managed fleets, caused by varying demands and rigid schedules. In this paper, we present an intelligent real-time transport information system to keep commuters informed about the status of buses currently in transit, and also provide an insight to bus managers based on ridership data and commuter behavior. The system is composed of three subsystems designed to cater for commuters, bus-drivers and bus managers respectively. This system is developed on the Backend-as-a-Service (BaaS) platform Firebase. Furthermore, a neural network is trained to provide predictions to bus managers on the expected ridership numbers per route. The trained model is integrated with a web application for bus managers. An Android application used by bus drivers collects the ridership data being fed to the network. The proposed system was evaluated with a real-world data set that contains the daily ridership on a per-route basis dating back to 2001. Evaluation results confirm the effectiveness of the new system in reducing the total mileage used to deliver commuters, reducing fuel costs, increasing the profit of bus operators, and increasing the percentage of satisfied ridership requests.

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.

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.