Extracting and Predicting Taxi Hotspots in Spatiotemporal Dimensions Using Conditional Generative Adversarial Neural Networks

It is of practical importance to extract and predict taxi hotspots in urban traffic networks. However, the extraction of taxi hotspot is generally influenced by multiple sources of dependence, which has not been well recognized in the existing literature. This study aims to investigate how the integration of clustering models and deep learning approaches can learn and extract the network-wide taxi hotspots in both temporal and spatial dimensions. A density based spatiotemporal clustering algorithm with noise (DBSTCAN) was established to extract the historical taxi hotspots, which changed with time. A conditional generative adversarial network with long short-term memory structure (LSTM-CGAN) model was proposed for taxi hotspot prediction, which is capable of capturing the spatial and temporal variations of taxi hotspots simultaneously. Specifically, the DBSTCAN was applied to process the large-scaled geo-coded taxi pickup data into time-varying historical hotspot information. The proposed LSTM-CGAN model was then trained by the network-wide hotspot data. As illustrated in the numerical tests, it was found that the proposed LSTM-CGAN model provided comparable results with different model layouts and model with 4 LSTM layers in both generator and discriminator performed best. Moreover, four typical prediction approaches were selected to compare with the proposed LSTM-CGAN model. The comparative analyses showed that the proposed LSTM-CGAN model reduced the false positive rate from 5.20% to 0.56% and the false negative rate from 63.50% to 39.93%, and improved the section consistency from 41.78% to 73.43% and the area under the curve from 0.670 to 0.969. The comparison results indicated that the proposed LSTM-CGAN model outperformed all these benchmark methods and demonstrated great potential to enable many shared mobility applications.