Neural Network-based Model for Traffic Prediction in the City of Valencia

There are many models that attempt to predict vehicular speed in urban and interurban roads, the noise pollution caused by traffic in cities, or even the traffic flow based on historical data from cameras or from people's mobile phones. Such information can be useful for administration authorities, and for researchers attempting to improve the living conditions of citizens. In this context, the aim of the present study is to design a model capable of predicting the traffic flow in the city of Valencia, Spain, based on data collected by electromagnetic loops distributed throughout the city. With a good traffic prediction, it will be possible to foresee possible traffic jams, and also to trigger countermeasures to mitigate them. Therefore, two models based on two recurrent neural networks of Long Short-Term Memory (LSTM) type have been designed to predict the traffic flow in the different streets of Valencia at the different hours of the day. We also study the influence of the specific characteristics used on the accuracy of the model. The results of our experiments show that, despite the high heterogeneity in terms of per-street traffic behaviour, it is possible to reach useful prediction models with low errors.