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Pedestrian behavior prediction model with a convolutional LSTM encoder–decoder

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  • Chen, Kai
  • Song, Xiao
  • Han, Daolin
  • Sun, Jinghan
  • Cui, Yong
  • Ren, Xiaoxiang

Abstract

Pedestrian behavior modeling is a challenging problem especially in crowded transportation scenarios. Some recent studies have addressed this problem using deep neural network, but the accuracy of trajectory prediction is still not high because the internal structure of the typical deep neural network with long short-term memory (LSTM) is a one-dimensional vector, which destroys the spatial information around a pedestrian. Therefore, these models cannot fully learn spatial sensing behavior of pedestrians. To solve this, we recommend using multi-channel tensors to represent the environmental information of pedestrians. Meanwhile, the spatiotemporal interactions among the pedestrians are represented by convolution operations of these tensors. Then, an end-to-end fully convolutional LSTM encoder–decoder is designed, trained and tested. Finally, our approach is compared with existing LSTM-based methods using five crowded video sequences with public datasets. The results show that our method reduces the displacement offset error and provides more realistic trajectory prediction in manifold cases.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:phsmap:v:560:y:2020:i:c:s0378437120305926
    DOI: 10.1016/j.physa.2020.125132
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    References listed on IDEAS

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    1. Antonini, Gianluca & Bierlaire, Michel & Weber, Mats, 2006. "Discrete choice models of pedestrian walking behavior," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 667-687, September.
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    3. Shahhoseini, Zahra & Sarvi, Majid, 2019. "Pedestrian crowd flows in shared spaces: Investigating the impact of geometry based on micro and macro scale measures," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 57-87.
    4. Song, Xiao & Ma, Liang & Ma, Yaofei & Yang, Chen & Ji, Hang, 2016. "Selfishness- and Selflessness-based models of pedestrian room evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 455-466.
    5. Song, Xiao & Han, Daolin & Sun, Jinghan & Zhang, Zenghui, 2018. "A data-driven neural network approach to simulate pedestrian movement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 827-844.
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    Cited by:

    1. Ma, Changxi & Liu, Tao, 2024. "Demand forecasting of shared bicycles based on combined deep learning models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    2. Korbmacher, Raphael & Dang, Huu-Tu & Tordeux, Antoine, 2024. "Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).

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