Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data

Pengjiang Li, Zaitian Wang, Xinhao Zhang, Pengfei Wang and Kunpeng Liu ()
Additional contact information
Pengjiang Li: Computer Network Information Center, Chinese Academy of Sciences, Beijing 100045, China
Zaitian Wang: Computer Network Information Center, Chinese Academy of Sciences, Beijing 100045, China
Xinhao Zhang: Department of Computer Science, Portland State University, Portland, OR 97201, USA
Pengfei Wang: Computer Network Information Center, Chinese Academy of Sciences, Beijing 100045, China
Kunpeng Liu: Department of Computer Science, Portland State University, Portland, OR 97201, USA

Mathematics, 2025, vol. 13, issue 5, 1-14

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.

Keywords: arrival prediction; regional function detection; embedding (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/5/746/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/5/746/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:5:p:746-:d:1599238

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().