 | Up a level |
Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Chen, X., Zhang, C., Zhao, X.-L., Saunier, N., & Sun, L. (2025). Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization. Transportation Science, 18 pages. External link
Choi, S., Saunier, N., Zheng, Z., Trépanier, M., & Sun, L. (2025). Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting. Transportation Science, 13 pages. External link
Chen, X., Zhang, C., Chen, X., Saunier, N., & Sun, L. (2024). Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression. IEEE Transactions on Knowledge and Data Engineering, 36(2), 504-517. External link
Chen, X., Cheng, Z., Cai, H.Q., Saunier, N., & Sun, L. (2024). Laplacian Convolutional Representation for Traffic Time Series Imputation. IEEE Transactions on Knowledge and Data Engineering, 3419698 (13 pages). External link
Chen, X., Cheng, Z., Jin, J. G., Trépanier, M., & Sun, L. (2023). Probabilistic Forecasting of Bus Travel Time with a Bayesian Gaussian Mixture Model. Transportation Science, -. External link
Cheng, Z. C., Wang, X., Chen, X., Trépanier, M., & Sun, L. (2023, January). Bayesian Calibration of Traffic Flow Fundamental Diagrams with Gaussian Processes [Paper]. 102nd Annual meeting of the Transportation Research Board, Washington, D.C.. External link
Cheng, Z., Wang, X., Chen, X., Trépanier, M., & Sun, L. (2022). Bayesian calibration of traffic flow fundamental diagrams using Gaussian processes. IEEE Open Journal of Intelligent Transportation Systems, 3, 763-771. Available
Chen, X., & Sun, L. (2022). Bayesian temporal factorization for multidimensional time series prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 4659-4673. External link
Cheng, Z., Trépanier, M., & Sun, L. (2022). Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition. Transportation Science, 56(4), 904-918. External link
Cheng, Z., Trépanier, M., & Sun, L. (2022, November). Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition [Paper]. CASPT - Conference on Advanced Systems for Public Transport, Tel-Aviv, Israel. Unavailable
Cheng, Z., Trépanier, M., & Sun, L. (2021). Incorporating travel behavior regularity into passenger flow forecasting. Transportation Research Part C: Emerging Technologies, 128, 16 pages. External link
Chen, X., Lei, M., Saunier, N., & Sun, L. (2021). Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12301-12310. External link
Cheng, Z., Trépanier, M., & Sun, L. (2021). Probabilistic model for destination inference and travel pattern mining from smart card data. External link
Chen, X., Chen, Y., Saunier, N., & Sun, L. (2021). Scalable low-rank tensor learning for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 129, 13 pages. External link
Sun, L., Chen, X., He, Z., & Miranda-Moreno, L. F. (2023). Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior. Networks and Spatial Economics, 23(2), 407-428. External link
Wang, X., Cheng, Z., Trépanier, M., & Sun, L. (2021). Modeling bike-sharing demand using a regression model with spatially varying coefficients. Journal of Transport Geography, 93, 103059 (12 pages). External link
Wang, X., Cheng, Z., Trépanier, M., & Sun, L. (2021, January). Quantifying the effect of factors on bike-sharing demand: A regression model with spatially varying coefficients [Paper]. 100th Annual Meeting of the Transportation Research Board. Unavailable
