Machine Learning Aided Simulation of Public Transport Utilization

Abstract

Despite having one of the most efficient transportation systems in the world, Singapore is still faced with congestion issues regularly, especially during peak hour periods, due to a number of reasons. We investigate some of the factors contributing to this issue and propose a simulator supplied with predictive travel times through congestion prediction, in order to evaluate and improve bus utilization through effective scheduling. We introduced a conceptual framework to integrate neural network models into simulation so as to improve real-time supply based on several possibilities of demands. This paper will delineate the steps taken to produce the simulator and discuss the evaluation of these models.

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