  The goal of this project is to introduce and present a machine learning
application that aims to improve the quality of life of people in Singapore. In
particular, we investigate the use of machine learning solutions to tackle the
problem of traffic congestion in Singapore. In layman's terms, we seek to make
Singapore (or any other city) a smoother place. To accomplish this aim, we
present an end-to-end system comprising of 1. A traffic density estimation
algorithm at traffic lights/junctions and 2. a suitable traffic signal control
algorithms that make use of the density information for better traffic control.
Traffic density estimation can be obtained from traffic junction images using
various machine learning techniques (combined with CV tools). After research
into various advanced machine learning methods, we decided on convolutional
neural networks (CNNs). We conducted experiments on our algorithms, using the
publicly available traffic camera dataset published by the Land Transport
Authority (LTA) to demonstrate the feasibility of this approach. With these
traffic density estimates, different traffic algorithms can be applied to
minimize congestion at traffic junctions in general.
