Spatiotemporal Features Recognition and Prediction of Urban Hot Spots Based on Trajectory Data

Abstract

With the continuous progress of urbanization, the construction of cities is gradually accelerated, and the urban population density is constantly increasing. Hot spots represent regions with more trips, greater population flow and higher travel demand. With the increasing complexity of traffic data, traditional methods for predicting hot spots can no longer meet the current situation. This also provides opportunities for the development of deep learning techniques. Given the continuous expansion in scale and intricacy of trajectory data, its relevance has become increasingly significant in domains such as urban planning, traffic management, and business decision-making. Aiming at the hot spot identification problem of trajectory data, this paper, inspired by the traffic prediction model, applied the STGCN model to the urban hot spot prediction problem and improved it. The model consists of four parts: cluster processing, spatial feature extraction, time feature extraction and hot spot region prediction. The experimental outcomes demonstrate that the model exhibits superior predictive performance across diverse datasets compared to conventional methods, showcasing robust generalization capabilities and practical applicability.