The objective of the proposed study is to predict traffic speeds at a route level so that the traffic management has a chance to operate proactively. A distributed file system and parallel computing platform is used to store the big data sets of statewide traffic and weather data in a fault-tolerant way and process the big data in a timely manner. Traffic speed prediction problem is studied at two levels, and two deep networks are proposed accordingly: a fully convolutional deep network for long-term speed prediction and a hybrid long short-term memory (LSTM) network for short-term speed prediction. The fully convolutional deep network utilizes both weather information and historical traffic speeds to make long-term traffic speed predictions, and a trained model can be transferred to predict traffic speed at any spatial-temporal scale. The hybrid LSTM network utilizes the previous traffic speeds on the current day as well as historical traffic speeds to make short-term speed predictions, and a trained model can be used to predict speeds at any timestamps ahead in a streaming fashion. The proposed long-term and short-term traffic speed prediction models can be combined as a multilayer decision supporting system to provide traffic management an opportunity to operate proactively.

Traffic speed prediction using big data enabled deep learning

A Deep Ensemble Learning Method for Short-Term Travel Time Prediction

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A Deep Ensemble Learning Method for Short-Term Travel Time Prediction