  The interest in developing smart cities has increased dramatically in recent
years. In this context an intelligent transportation system depicts a major
topic. The forecast of traffic flow is indispensable for an efficient
intelligent transportation system. The traffic flow forecast is a difficult
task, due to its stochastic and non linear nature. Besides classical
statistical methods, neural networks are a promising possibility to predict
future traffic flow. In our work, this prediction is performed with various
recurrent neural networks. These are trained on measurements of induction
loops, which are placed in intersections of the city. We utilized data from
beginning of January to the end of July in 2018. Each model incorporates
sequences of the measured traffic flow from all sensors and predicts the future
traffic flow for each sensor simultaneously. A variety of model architectures,
forecast horizons and input data were investigated. Most often the vector
output model with gated recurrent units achieved the smallest error on the test
set over all considered prediction scenarios. Due to the small amount of data,
generalization of the trained models is limited.
