A K-Shape Clustering Based Transformer-Decoder Model for Predicting Multi-Step Potentials of Urban Mobility Field

Identifying and predicting the travel hotspots in urban areas can provide crucial support for building intelligent transportation systems. In this study, we propose to use the potentials of urban mobility field to identify the travel hotspots and develop a K-shape clustering transformer-decoder (KSC-TD) model to predict multi-step potentials. In the KSC-TD model, the K-shape clustering method is used to cluster the grids with similar potential time series, whereas the transformer-decoder model is trained for each cluster of grids by integrating the multi-head masked attention mechanism and the scheduled sampling strategy. The developed KSC-TD model is validated using the license plate recognition (LPR) data of Changsha (a major southern city of China). Results indicate that the proposed KSC-TD model outperforms nine benchmark models in predicting the multi-step potentials of urban mobility field, offering a new and effective approach for anticipating urban travel hotspots.