  Traffic flow prediction is an important research issue to avoid traffic
congestion in transportation systems. Traffic congestion avoiding can be
achieved by knowing traffic flow and then conducting transportation planning.
Achieving traffic flow prediction is challenging as the prediction is affected
by many complex factors such as inter-region traffic, vehicles' relations, and
sudden events. However, as the mobile data of vehicles has been widely
collected by sensor-embedded devices in transportation systems, it is possible
to predict the traffic flow by analysing mobile data. This study proposes a
deep learning based prediction algorithm, DeepTFP, to collectively predict the
traffic flow on each and every traffic road of a city. This algorithm uses
three deep residual neural networks to model temporal closeness, period, and
trend properties of traffic flow. Each residual neural network consists of a
branch of residual convolutional units. DeepTFP aggregates the outputs of the
three residual neural networks to optimize the parameters of a time series
prediction model. Contrast experiments on mobile time series data from the
transportation system of England demonstrate that the proposed DeepTFP
outperforms the Long Short-Term Memory (LSTM) architecture based method in
prediction accuracy.
