Abstract
Rail transit passenger flow is affected by many factors. In order to get a more suitable departure interval, the factors of passenger flow changes must be fully considered. Based on Huang Yan-Pei Thought, this paper analyzes the influencing factors of riding behavior, and uses neural network model to predict the behavior of potential travelers taking rail transit. At the same time, through the analysis of the spatial and temporal distribution of rail transit passenger flow, a multi-objective planning model is established based on the indexes of vehicle full load and passenger comfort, which is helpful for the reasonable arrangement of urban rail transit capacity.
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References
Li, Q., Yanjun, S., Jing, Z., Victor, S.S.: Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering. Expert Syst. Appl. 147, 113152 (2020). https://doi.org/10.1016/j.eswa.2019.113152
Hou, J., Li, Q., Tan, S., Meng, H., Zhang, S.: An intrusion tracking watermarking scheme. IEEE Access 7, 141438–141455 (2019). https://doi.org/10.1109/access.2019.2943493
Hou, J., Li, Q., Cui, S., et al.: Low-cohesion differential privacy protection for industrial internet. J. Comput. 7, 1–23 (2020). https://doi.org/10.1007/s11227-019-03122-y
Li, Q., Tian, Y., Wu, Q., Cao, H., Shen, H., Long, H.: A cloud-fog-edge closed-loop feedback security risk prediction method. IEEE Access 8(1), 29004–29020 (2020)
Li, Q., et al.: Safety risk monitoring of cyber-physical power systems based on ensemble learning algorithm. IEEE Access 7, 24788–24805 (2019)
Li, Q., Shunmei, M., Wang, S., Jing, Z., Jun, H.: Command-level anomaly detection for vehicle-road collaborative charging network. IEEE Access 7, 34910–34924 (2019)
Li, Q., Meng, S., Zhang, S., Hou, J., Qi, L.: Complex attack linkage decision-making in edge computing networks. IEEE Access 7, 12058–12072 (2019)
Li, Q., Wang, Y., Pu, Z., Wang, S., Zhang, W.: A time series association state analysis method in smart internet of electric vehicle charging network attack. Transp. Res. Rec. 2673, 217–228 (2019)
Cui, S., Li, T., Chen, S.C., Shyu, M.L., Li, Q.: DISL: Deep Isomorphic Substructure Learning for network representations. Knowl-Based Syst. 189, 105086 (2020). https://doi.org/10.1016/j.knosys.2019.105086
Shunmei, M., Li, Q., Zhang, J., Lin, W., Dou, W.: Temporal-aware and sparsity-tolerant hybrid collaborative recommendation method with privacy preservation. Concurr. Comput. Pract. Exp. 32(2), e5447 (2020). https://doi.org/10.1002/cpe.5447
Li, Q., Hou, J., Meng, S., Long, H.: GLIDE: a game theory and data-driven mimicking linkage intrusion detection for edge computing networks. Complexity, 7136160, 18 (2020). https://doi.org/10.1155/2020/7136160
Hou, J., Li, Q., Meng, S., Ni, Z., Chen, Y., Liu, Y.: A differential privacy protection random forest. IEEE Access 7, 130707–130720 (2019). https://doi.org/10.1109/access.2019.2939891
Li, Q., Yin, X., Meng, S., Liu, Y., Ying, Z.: A security event description of intelligent applications in edge-cloud environment. J. Cloud Comput. 9(1), 1–13 (2020). https://doi.org/10.1186/s13677-020-00171-0
Funding
This work was supported in part by the Key Research Base of Philosophy and Social Sciences in Jiangsu Universities: “Huang Yan-Pei Vocational Education Thought Research Society Academic Center”, 2020 Industrial Internet Innovation and Development Project from Ministry of Industry and Information Technology of China, 2018 Jiangsu Province Major Technical Research Project “Information Security Simulation System”, Fundamental Research Funds for the Central Universities (30918012204).
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Hou, J., Song, Y., Li, Q., Long, H., Jiang, J. (2020). Behavior Prediction and Its Design for Safe Departure Intervals Based on Huang Yan-Pei Thought. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_47
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DOI: https://doi.org/10.1007/978-981-15-9129-7_47
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