Sponsor
This research was supported by National Natural Science Foundation of China (Grant No. 62406306) and the State Key Laboratory of Internet of Things for Smart City (University of Macau) No. SKL-IoTSC(UM)-2024-2026/ORP/GA02/2023.
Published In
Mathematics
Document Type
Article
Publication Date
3-12-2025
Subjects
Arrival prediction, regional function detection, embedding
Abstract
With the development of transportation networks, countless trajectory data are accumulated, and understanding human mobility from traffic data could be helpful for smart cities, urban computing, and urban planning. Extracting valuable insights from traffic data, such as taxi trajectories, can significantly improve residents’ daily lives. There are many studies on spatiotemporal data mining. As we know, arrival prediction or regional function detection encompasses important tasks for traffic management and urban planning. However, trajectory data are often mutilated because of personal privacy and hardware limitations, i.e., we usually can only obtain partial trajectory information. In this paper, we develop an embedding method to predict the next arrival using the origin–destination (O-D) pair trajectory information and point of interest (POI) data. Moreover, the embedding information contains region latent features; thus, we also detect the regional function in this paper. Finally, we conduct a comprehensive experimental study on a real-world trajectory dataset. The experimental results demonstrate the benefit of predicting arrivals, and the embedding vectors can detect the regional function in a city.
Rights
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Locate the Document
DOI
10.3390/math13050746
Persistent Identifier
https://archives.pdx.edu/ds/psu/43113
Citation Details
Li, P., Wang, Z., Zhang, X., Wang, P., & Liu, K. (2025). Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data. Mathematics, 13(5), 746.
