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

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.