Foreseeing Congestion using LSTM on Urban Traffic Flow Clusters

Currently, Intelligent Transportation Systems (ITS), is revolutionizing the transportation industry. ITS incorporates advanced Internet of Things (IoT) technologies to implement Smart City. These technologies produce tremendous amount of real time data from diverse sources that can be used to solve transportation problems. In this paper, we focus on one such problem, traffic congestion in urban areas. A road segment affected by traffic affects the surrounding road segments. This is obvious. However, over a period of time, other roads not necessarily close in proximity to the congested road segment may also be affected. The congestion is not stationary. It is dynamic and it spreads. We address this issue by first formulating a similarity function using ideas from network theory. Using this similarity function, we then cluster the road points affected by traffic using affinity propagation clustering, a distributed message passing algorithm. Finally, we predict the effect of traffic on this cluster using long-short term memory neural network model. We evaluate and show the feasibility of our proposed clustering and prediction algorithm during peak and non-peak hours on open source traffic data set.