Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data
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 signif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/5/746 |
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| author | Pengjiang Li Zaitian Wang Xinhao Zhang Pengfei Wang Kunpeng Liu |
| author_facet | Pengjiang Li Zaitian Wang Xinhao Zhang Pengfei Wang Kunpeng Liu |
| author_sort | Pengjiang Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aff18ba8f3554f16ae55eea541b2db6a |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-aff18ba8f3554f16ae55eea541b2db6a2025-08-20T02:59:15ZengMDPI AGMathematics2227-73902025-02-0113574610.3390/math13050746Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility DataPengjiang Li0Zaitian Wang1Xinhao Zhang2Pengfei Wang3Kunpeng Liu4Computer Network Information Center, Chinese Academy of Sciences, Beijing 100045, ChinaComputer Network Information Center, Chinese Academy of Sciences, Beijing 100045, ChinaDepartment of Computer Science, Portland State University, Portland, OR 97201, USAComputer Network Information Center, Chinese Academy of Sciences, Beijing 100045, ChinaDepartment of Computer Science, Portland State University, Portland, OR 97201, USAWith 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.https://www.mdpi.com/2227-7390/13/5/746arrival predictionregional function detectionembedding |
| spellingShingle | Pengjiang Li Zaitian Wang Xinhao Zhang Pengfei Wang Kunpeng Liu Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data Mathematics arrival prediction regional function detection embedding |
| title | Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data |
| title_full | Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data |
| title_fullStr | Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data |
| title_full_unstemmed | Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data |
| title_short | Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data |
| title_sort | next arrival and destination prediction via spatiotemporal embedding with urban geography and human mobility data |
| topic | arrival prediction regional function detection embedding |
| url | https://www.mdpi.com/2227-7390/13/5/746 |
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