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In the realm of Intelligent Transportation Systems (ITS), accurate trafﬁc speed prediction plays an important role in trafﬁc control and management. The study on the prediction of trafﬁc speed has attracted considerable attention from many researchers in this ﬁeld in the past three decades. In recent years, deep learning-based methods have demonstrated their competitiveness to the time series analysis which is an essential part of trafﬁc prediction. These methods can efﬁciently capture the complex spatial dependency on road networks and non-linear trafﬁc conditions. We have adopted the convolutional neural network-based deep learning approach to trafﬁc speed prediction in our setting, based on its capability of handling multi-dimensional data efﬁciently. In practice,the trafﬁc data may not be recorded with a regular interval, due to many factors, like power failure, transmission errors,etc.,that could have an impact on the data collection. Given that some part of our dataset contains a large amount of missing values, we study the effectiveness of a multi-view approach to imputing the missing values so that various prediction models can apply. Experimental results showed that the performance of the trafﬁc speed prediction model improved signiﬁcantly after imputing the missing values with a multi-view approach, where the missing ratio is up to 50%.
Gutha, Sindhuja, "A deep learning approach to real-time short-term trafﬁc speed prediction with spatial-temporal features" (2019). Electronic Theses and Dissertations. 7704.