Predicting trajectory destinations based on diffusion model integrating spatiotemporal features and urban contexts

Real world driving trajectory dataset in Nanshan and Futian districts, Shenzhen, China, collected in October, 2017 by Amap platform. The dataset is used to predict trajectory destinations. Road network and POI statistics are utilized in this dataset, serving as urban contexts. The geometries are in Gauss-Kruger zone 38 (epsg:4526) with GCJ-02 latitude-longitude coordinate confusion.The published article is available (Hu et al., 2024) on International Journal of Digital Earth.The latest version of our code is available on GithubFile description:<code>code.zip</code>: code for model structure, data pipeline and training, testing procedure.<code>data.zip</code>: dataset and code for this study, including:<code>data.zip/embedding/</code>: the trained embeddings of road topology by LINE method.<code>data.zip/predict_model/</code>: the trained parameters of our model and baselines, with *.pth suffix for pytorch framework.<code>data.zip/roads/</code>: the shp file of road network. POI statistics are contained in <code>road_input.csv</code><code>data.zip/trajectories/</code>: driving trajectories of each day. <code>metadata.csv</code> contains the departure time, destination and other statistics.