A data-driven neural network approach to simulate pedestrian movement
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DOI: 10.1016/j.physa.2018.06.045
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Cited by:
- Zhao, Xuedan & Xia, Long & Zhang, Jun & Song, Weiguo, 2020. "Artificial neural network based modeling on unidirectional and bidirectional pedestrian flow at straight corridors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
- Chen, Kai & Song, Xiao & Han, Daolin & Sun, Jinghan & Cui, Yong & Ren, Xiaoxiang, 2020. "Pedestrian behavior prediction model with a convolutional LSTM encoder–decoder," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
- Zhu, Yu & Chen, Tao & Ding, Ning & Chraibi, Mohcine & Fan, Wei-Cheng, 2021. "Follow people or signs? A novel way-finding method based on experiments and simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
- Yongshi Liu & Xiaodong Yu & Jianjun Zhao & Changchun Pan & Kai Sun, 2022. "Development of a Robust Data-Driven Soft Sensor for Multivariate Industrial Processes with Non-Gaussian Noise and Outliers," Mathematics, MDPI, vol. 10(20), pages 1-16, October.
- Shi Sun & Cheng Sun & Dorine C. Duives & Serge P. Hoogendoorn, 2023. "Neural network model for predicting variation in walking dynamics of pedestrians in social groups," Transportation, Springer, vol. 50(3), pages 837-868, June.
- Chen, Kai & Song, Xiao & Ren, Xiaoxiang, 2021. "Modeling social interaction and intention for pedestrian trajectory prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
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Keywords
Pedestrian flow; Artificial intelligence; Neural networks; Social force;All these keywords.
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