Abstract
Mining traffic congestion patterns is important for planning travel routes and optimizing traffic control in urban areas. However, existing methods ignore the spatio-temporal attributes of traffic flow data and the fuzziness of the concept of congestion itself, leading to defects and inaccuracies in the definition of traffic congestion. To this end, a novel traffic congestion pattern on the basis of spatio-temporal fuzzy co-location pattern is proposed, and a traffic congestion pattern mining system, named TCPMS-FCP, is developed. With TCPMS-FCP, travelers can choose appropriate travel routes to reduce travel time, while traffic management agencies can use the mined congestion patterns to improve the efficiency of traffic management and the level of traffic congestion control.
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Di, X., Yu, X., Zhu, C.: Traffic congestion prediction by spatiotemporal propagation patterns. In: 20th IEEE International Conference on Mobile Data Management (MDM), pp. 298–303 (2019)
Song, J., Zhao, C., Zhong, S., Nielsen, T., Prishchepov, A.: Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques. Comput. Environ. Urban Syst. 77, 101364 (2019)
Abbas, Z., Sottovia, P., Hassan, M., Foroni, D., Bortoli, S.: Real-time traffic jam detection and congestion reduction using streaming graph analytics. In: 8th IEEE International Conference on Big Data (IEEE BigData), pp. 3109–3118 (2020)
Zhang, J., Zheng, Y., Sun, J., Qi, D.: Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Trans. Knowl. Data Eng. 32(3), 468–478 (2020)
Pan, Z., et al.: Spatio-temporal meta learning for urban traffic prediction. IEEE Trans. Knowl. Data Eng. 34(3), 1462–1476 (2022)
He, Y., Wang, L., Fang, Y., Li, Y.: Discovering congestion propagation patterns by co-location pattern mining. In: U, L.H., Xie, H. (eds.) APWeb-WAIM 2018. LNCS, vol. 11268, pp. 46–55. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01298-4_5
Wang, L., Bao, X., Zhou, L.: Redundancy reduction for prevalent co-location patterns. IEEE Trans. Knowl. Data Eng. 30(1), 142–155 (2018)
Hu, Z., Wang, L., Tran, V., Chen, H.: Efficiently mining spatial co-location patterns utilizing fuzzy grid cliques. Inf. Sci. 592, 361–388 (2022)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (61966036), the Project of Innovative Research Team of Yunnan Province (2018HC019), the Yunnan Fundamental Research Project (202201AS070015), the Scientific Research Fund Project of Yunnan Provincial Department of Education (2021J0797), and the Scientific Research Fund Project of Dianchi College of Yunnan University (2022XYB12).
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Wang, X., Wang, J., Wang, L., Wang, S., Ding, L. (2022). TCPMS-FCP: A Traffic Congestion Pattern Mining System Based on Spatio-Temporal Fuzzy Co-location Patterns. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_47
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DOI: https://doi.org/10.1007/978-3-031-20891-1_47
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