GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
Source abstract
Abstract
Signaling data are records of the interactions of users’ mobile phones with their nearest cellular stations, which could provide long-term and continuous-time location data of large-scale citizens, and therefore have great potential in intelligent transportation, smart cities, and urban sensing. However, utilizing the raw signaling data often suffers from two problems: (1) Low positioning accuracy. Since the signaling data only describes the interaction between the user and the mobile base station, they can only restore users’ approximate geographical location. (2) Poor data quality. Due to the limitations of mobile signals, user signaling may be missing and drifting. To address the above issues, we propose a graph-enhanced trajectory recovery network, GenTrajRec, to recover precise trajectories from signaling data. GenTrajRec encodes signaling data through spatiotemporal encoders and enhances the traveling semantics by constructing a signaling transition graph. In fusing the spatiotemporal information as well as the deep traveling semantics, GenTrajRec can well tackle the challenge of poor data quality, and recover precise trajectory from raw signaling data. Extensive experiments have been conducted on two real-world datasets from Mobile Signaling and Geolife, and the results confirm the effectiveness of our approach, and the positioning accuracy can be improved from 315 m per point to 82 m per point for signaling data using our network.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Data Management and Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing 100191, China
Funding
National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities
References (42)
Show source-provided references
1. Yao, L., Bao, J., Ding, F., Zhang, N., and Tong, E. (2021, January 13–15). Research on traffic flow forecast based on cellular signaling data. Proceedings of the 2021 IEEE International Conference on Smart Internet of Things (SmartIoT), Jeju, Republic of Korea.10.1109/SmartIoT52359.2021.00038 ↗
2. Spatial-temporal cellular traffic prediction for 5G and beyond: A graph neural networks-based approach10.1109/TII.2022.3182768 ↗
3. Generative adversarial networks: An overview10.1109/MSP.2017.2765202 ↗
4. Intelligent trajectory inference through cellular signaling data10.1109/TCCN.2019.2961660 ↗
5. TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data10.1016/j.trc.2021.103257 ↗
6. Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (2016, January 27–30). Social lstm: Human trajectory prediction in crowded spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.110 ↗
7. Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., and Alahi, A. (2018, January 18–23). Social gan: Socially acceptable trajectories with generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00240 ↗
8. Zhang, G., Yu, Z., Jin, D., and Li, Y. (2022, January 14–18). Physics-infused machine learning for crowd simulation. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.10.1145/3534678.3539440 ↗
9. Zhao, H., Gao, J., Lan, T., Sun, C., Sapp, B., Varadarajan, B., Shen, Y., Shen, Y., Chai, Y., and Schmid, C. (2021, January 8–11). Tnt: Target-driven trajectory prediction. Proceedings of the Conference on Robot Learning, PMLR, London, UK.
10. Chai, Y., Sapp, B., Bansal, M., and Anguelov, D. (2019). Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. arXiv.
11. Welch, G., and Bishop, G. (1995). An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill Department of Computer Science.
12. Hammersley, J. (2013). Monte Carlo Methods, Springer Science & Business Media.
13. Support-vector networks10.1007/BF00994018 ↗
14. A fast learning algorithm for deep belief nets10.1162/neco.2006.18.7.1527 ↗
15. Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network10.1109/TITS.2022.3146300 ↗
16. Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., and Jin, D. (2018, January 23–27). Deepmove: Predicting human mobility with attentional recurrent networks. Proceedings of the 2018 World Wide Web Conference, Lyon, France.10.1145/3178876.3186058 ↗
17. Deep trajectory recovery with fine-grained calibration using kalman filter
18. Xia, T., Qi, Y., Feng, J., Xu, F., Sun, F., Guo, D., and Li, Y. (2021, January 2–9). Attnmove: History enhanced trajectory recovery via attentional network. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.10.1609/aaai.v35i5.16577 ↗
19. Sun, H., Yang, C., Deng, L., Zhou, F., Huang, F., and Zheng, K. (2021, January 1–5). Periodicmove: Shift-aware human mobility recovery with graph neural network. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Event.10.1145/3459637.3482284 ↗
20. Ren, H., Ruan, S., Li, Y., Bao, J., Meng, C., Li, R., and Zheng, Y. (2021, January 14–18). Mtrajrec: Map-constrained trajectory recovery via seq2seq multi-task learning. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore.10.1145/3447548.3467238 ↗
21. Chen, Y., Zhang, H., Sun, W., and Zheng, B. (2023, January 3–7). Rntrajrec: Road network enhanced trajectory recovery with spatial-temporal transformer. Proceedings of the 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, USA.10.1109/ICDE55515.2023.00069 ↗
22. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv.
23. Yao, D., Hu, H., Du, L., Cong, G., Han, S., and Bi, J. (2022, January 14–18). Trajgat: A graph-based long-term dependency modeling approach for trajectory similarity computation. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.10.1145/3534678.3539358 ↗
24. Liang, Y., Ouyang, K., Wang, Y., Liu, X., Chen, H., Zhang, J., Zheng, Y., and Zimmermann, R. (2022, January 17–21). TrajFormer: Efficient trajectory classification with transformers. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA.10.1145/3511808.3557481 ↗
25. Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., and Zhang, C. (2020, January 6–10). Connecting the dots: Multivariate time series forecasting with graph neural networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event.10.1145/3394486.3403118 ↗
26. Generic dynamic graph convolutional network for traffic flow forecasting10.1016/j.inffus.2023.101946 ↗
27. Ariyo, A.A., Adewumi, A.O., and Ayo, C.K. (2014, January 26–28). Stock price prediction using the ARIMA model. Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Cambridge, UK.10.1109/UKSim.2014.67 ↗
28. A review of recurrent neural networks: LSTM cells and network architectures10.1162/neco_a_01199 ↗
29. Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv.
30. Temporal fusion transformers for interpretable multi-horizon time series forecasting10.1016/j.ijforecast.2021.03.012 ↗
31. Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 2–9). Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.10.1609/aaai.v35i12.17325 ↗
32. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
33. Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R. (2022, January 17–23). Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. Proceedings of the International Conference on Machine Learning, PMLR, Baltimore, MD, USA.
34. Zeng, A., Chen, M., Zhang, L., and Xu, Q. (2023, January 20–27). Are transformers effective for time series forecasting?. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.
35. Mlp-mixer: An all-mlp architecture for vision
36. Chen, S.A., Li, C.L., Yoder, N., Arik, S.O., and Pfister, T. (2023). Tsmixer: An all-mlp architecture for time series forecasting. arXiv.
37. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
38. Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.c. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Adv. Neural Inf. Process. Syst., 28.
39. Grover, A., and Leskovec, J. (2016, January 13–17). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939754 ↗
40. Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11–13). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Fort Lauderdale, FL, USA.
41. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30.
42. GeoLife: A collaborative social networking service among user, location and trajectory