Predictive Simulation of Public Transportation Using Deep Learning

Abstract

Traffic congestion has been one of the most common issues faced today as the rising population and growing economy calls for higher demands in efficient transportation. Looking into the public transport system in Singapore, we investigate its efficiency through a simple simulation and introduced predictive travel times to simulate ahead in future so as to identify congestion issues well in advance. Public transport operators can then utilize the reports to apply strategic resolutions in order to mitigate or avoid those issues beforehand. A deep neural network regression model was proposed to predict congestion, which is then used to compute future travel times. Experiments showed that the proposed methods are able to inject a better sense of realism into future simulations.