The accuracy of traffic flow prediction significantly
impacts the operation of Intelligent Transportation Systems
(ITS). In this paper, we propose a Differential Time-variant (DT)
Traffic Flow Prediction method, which can remarkably improve
the accuracy and reduce the variance of traffic flow forecast
based on deep learning models. To extract the temporal trend
of the traffic flow at different locations, we apply data difference
to preprocess the raw traffic data. This method can better
eliminate the uncertainties of traffic flow series like volatility
and anomaly. Then, time information is introduced in the form
of One-Hot Encoding to effectively model the temporal patterns
of traffic flow. Necessary analysis is presented to demonstrate
the rationality. Three popular deep neural networks are applied
to test our method, and experimental results on PeMS data sets
indicate that it can make more accurate prediction compared
with the same model.