Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 20, Issue 3, Pages 1000-1009Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2018.2836141
Keywords
Deep learning; traffic flow prediction; sensitivity to loss of data
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The deployment of intelligent transport systems requires efficient means of assessing the traffic situation. This involves gathering real traffic data from the road network and predicting the evolution of traffic parameters, in many cases based on incomplete or false data from vehicle detectors. Traffic flows in the network follow spatiotemporal patterns and this characteristic is used to suppress the impact of missing or erroneous data. The application of multilayer perceptrons and deep learning networks using autoencoders for the prediction task is evaluated. Prediction sensitivity to false data is estimated using traffic data from an urban traffic network.
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