Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data
Adi Alhudhaif,
Kemal Polat
Abstract:Currently, most traffic simulations require residents’ travel plans as input data; however, in real scenarios, it is difficult to obtain real residents’ travel behavior data for various reasons, such as a large amount of data and the protection of residents’ privacy. This study proposes a method combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) for analyzing and compensating spatiotemporal features in residents’ travel data. By exploiting the spatial feature extraction … Show more
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