Li, X. W. D. J., Ye, X., & Sun, Q. (2024). Leveraging Connected Vehicle Data for Near-Crash Detection and Analysis in Urban Environments. In arXiv preprint arXiv:2409.11341.
@unpublished{li2024leveraging,
title = {Leveraging Connected Vehicle Data for Near-Crash Detection and Analysis in Urban Environments},
author = {Li, Xinyu Wu Dayong(Jason) and Ye, Xinyue and Sun, Quan},
journal = {arXiv preprint arXiv:2409.11341},
year = {2024},
file = {Leveraging Connected Vehicle Data for Near-Crash and Analysis in Urban Environments.pdf}
}
Refereed journal articles
Li, X., Xu, Y., Zhang, X., Shi, W., Yue, Y., & Li, Q. (2023). Improving short-term bike sharing demand forecast through an irregular convolutional neural network. Transportation Research Part C: Emerging Technologies, 147, 103984.
@article{li2023improving,
title = {Improving short-term bike sharing demand forecast through an irregular convolutional neural network},
author = {Li, Xinyu and Xu, Yang and Zhang, Xiaohu and Shi, Wenzhong and Yue, Yang and Li, Qingquan},
journal = {Transportation research part C: emerging technologies},
volume = {147},
pages = {103984},
year = {2023},
publisher = {Elsevier},
doi = {https://doi.org/10.1016/j.trc.2022.103984},
file = {Improving short-term bike sharing demand forecast through an irregular convolutional neural network.pdf}
}
Xu, Y., Zou, D., Park, S., Li, Q., Zhou, S., & Li, X. (2022). Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea. Computers, Environment and Urban Systems, 92, 101753.
@article{xu2022understanding,
title = {Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea},
author = {Xu, Yang and Zou, Dan and Park, Sangwon and Li, Qiuping and Zhou, Suhong and Li, Xinyu},
journal = {Computers, Environment and Urban Systems},
volume = {92},
pages = {101753},
year = {2022},
publisher = {Elsevier},
doi = {https://10.1109/TITS.2021.3097240},
file = {Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea.pdf}
}
Li, X., Xu, Y., Chen, Q., Wang, L., Zhang, X., & Shi, W. (2021). Short-term forecast of bicycle usage in bike sharing systems: a spatial-temporal memory network. IEEE Transactions on Intelligent Transportation Systems, 23(8), 10923–10934.
@article{li2021short,
title = {Short-term forecast of bicycle usage in bike sharing systems: a spatial-temporal memory network},
author = {Li, Xinyu and Xu, Yang and Chen, Qi and Wang, Lei and Zhang, Xiaohu and Shi, Wenzhong},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {23},
number = {8},
pages = {10923--10934},
year = {2021},
publisher = {IEEE},
doi = {https://10.1109/TITS.2021.3097240},
file = {Short-Term_Forecast_of_Bicycle_Usage_in_Bike_Sharing_Systems_A_Spatial-Temporal_Memory_Network.pdf}
}
Xu, Y., Li, X., Shaw, S.-L., Lu, F., Yin, L., & Chen, B. Y. (2020). Effects of data preprocessing methods on addressing location uncertainty in mobile signaling data. Annals of the American Association of Geographers, 111(2), 515–539.
@article{xu2020effects,
title = {Effects of data preprocessing methods on addressing location uncertainty in mobile signaling data},
author = {Xu, Yang and Li, Xinyu and Shaw, Shih-Lung and Lu, Feng and Yin, Ling and Chen, Bi Yu},
journal = {Annals of the American Association of Geographers},
volume = {111},
number = {2},
pages = {515--539},
year = {2020},
publisher = {Taylor \& Francis},
doi = {https://doi.org/10.1080/24694452.2020.1773232},
file = {Effects of Data Preprocessing Methods on Addressing Location Uncertainty in Mobile Signaling Data}
}
Refereed conference proceedings
Quan Sun, Xinyu Li*, Dayong Wu, Tianchen Huang, Xinyue Ye and Wei Li. (2024). Assessing Volume Delay Function Accuracy Through Multi-Source Traffic Data: Insights from Connected Vehicle Data and Traffic Simulation Data. Accepted by the TRB Annual Meeting 2025.
@inproceedings{Li2025TRB,
author = {{Quan Sun, Xinyu Li*, Dayong Wu, Tianchen Huang, Xinyue Ye and Wei Li}},
title = {Assessing Volume Delay Function Accuracy Through Multi-Source Traffic Data: Insights from Connected Vehicle Data and Traffic Simulation Data},
note = {Accepted by the TRB Annual Meeting 2025},
year = {2024}
}
Xinyu Li, Y. X., & Zhang, R. (2023). CPGIS 2023 Programme. Accessed: 2024-09-03.
@inproceedings{CPGIS2023,
author = {Xinyu Li, Yang Xu and Zhang, Ruojing},
title = {CPGIS 2023 Programme},
year = {2023},
note = {Accessed: 2024-09-03},
file = {CPGIS_2023_programme.pdf}
}
This study proposes a deep learning model to predict short-term bike sharing usage through feature fusion in spatial and topological domains across two station-based bike-sharing systems in Chicago and New York. Based on the prediction results, the proposed model performs better than the baseline models. The results imply that the prediction model leveraging bike-sharing usage’s spatial and topological features outperforms the model only using spatial features.
Xinyu Li, Y. X. (2021). Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network. Proceedings of The 4th International Symposium on Multimodal Transportation. Webinar.
@inproceedings{Li2021Multimodal,
author = {Xinyu Li, Yang Xu},
title = {Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network},
booktitle = {Proceedings of The 4th International Symposium on Multimodal Transportation},
year = {2021},
note = {Webinar}
}
Xinyu Li, Y. X. (2021). Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network. Proceedings of The 2021 City+ Milan Conference. Webinar, Theme: Smart City Beyond.
@inproceedings{Li2021CityPlus,
author = {Xinyu Li, Yang Xu},
title = {Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network},
booktitle = {Proceedings of The 2021 City+ Milan Conference},
year = {2021},
address = {Milan, Italy},
note = {Webinar, Theme: Smart City Beyond}
}