Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data

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 capability of CNNs and the advantages of LSTMs in processing time-series data, the aim is to achieve a traffic simulation close to a real scenario using limited data by modelingtravel time and space. The experimental results show that the method proposedinthisarticle is closer to the real data intermsoftheaveragetravelingdistance comparedwiththeuseofthemodulationmethodandthestatisticalestimationmethod. The new strategy we propose can significantly reduce the deviation of the model from the original data, thereby significantly reducing the basic error rate by about 50%.