IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v643y2024ics0378437124003042.html
   My bibliography  Save this article

Density control in pedestrian evacuation with incorrect feedback information: Data correction

Author

Listed:
  • Liu, Tundong
  • Gao, Fengqiang
  • Zhou, Weihong
  • Yan, Yuyue

Abstract

Improving evacuation efficiency is a central concern in evacuation simulation research. Incorrect feedback information can affect the effectiveness of evacuation control in partially observable evacuation. In this paper, we introduce a framework for evacuation guidance control, emphasizing data prediction and correction to mitigate the impact of abnormal observed data. The framework is built upon force-driven Cellular Automaton (CA) models and employs a data correction module to rectify abnormal information. Guided by this framework, we implement the data correction module’s functionality using Back Propagation (BP) neural networks. We utilize historical simulation data to train the BP network, obtaining a model for correcting abnormal data. Additionally, we integrate the data correction model with density control algorithms to facilitate pedestrian flow management in abnormal evacuation scenarios. Subsequently, we conduct two comparative simulation experiments to verify the algorithm’s effectiveness. One experiment utilizes an abnormal data replacement method, while the other employs a data correction method. The results show that the method of abnormal data replacement is simple, but it cannot improve the control efficiency in abnormal evacuation scenarios. The data correction method proposed in this paper can effectively improve evacuation efficiency and alleviate the congestion at exits caused by abnormal data, which reduces evacuation efficiency. The results are expected to provide insights into improving evacuation systems’ efficiency and personnel safety.

Suggested Citation

  • Liu, Tundong & Gao, Fengqiang & Zhou, Weihong & Yan, Yuyue, 2024. "Density control in pedestrian evacuation with incorrect feedback information: Data correction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    • Handle: RePEc:eee:phsmap:v:643:y:2024:i:c:s0378437124003042
    DOI: 10.1016/j.physa.2024.129795
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124003042
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129795?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3227-3241, June.
    2. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    3. He, Zhichao & Shen, Kaixin & Lan, Meng & Weng, Wenguo, 2024. "An evacuation path planning method for multi-hazard accidents in chemical industries based on risk perception," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Zhang, Dezhen & Huang, Gaoyue & Ji, Chengtao & Liu, Huiying & Tang, Ying, 2021. "Pedestrian evacuation modeling and simulation in multi-exit scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    5. Chen, Liang & Guo, Zhi-Liang & Wang, Tao & Li, Chuan-Yao & Tang, Tie-Qiao, 2023. "An evacuation guidance model for heterogeneous populations in large-scale pedestrian facilities with multiple exits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 620(C).
    6. Gao, Fengqiang & Yan, Yuyue & Chen, Zhihao & Zheng, Linxiao & Ren, Huan, 2022. "Effect of density control in partially observable asymmetric-exit evacuation under guidance: Strategic suggestion under time delay," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    7. Ren, Huan & Yan, Yuyue & Gao, Fengqiang, 2021. "Variable guiding strategies in multi-exits evacuation: Pursuing balanced pedestrian densities," Applied Mathematics and Computation, Elsevier, vol. 397(C).
    8. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramtin Moeini & Kamran Nasiri & Seyed Hossein Hosseini, 2024. "Predicting the Water Inflow Into the Dam Reservoir Using the Hybrid Intelligent GP-ANN- NSGA-II Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4137-4159, September.
    2. Li, Shuguang & Leng, Yuchi & Chaturvedi, Rishabh & Dutta, Ashit Kumar & Abdullaeva, Barno Sayfutdinovna & Fouad, Yasser, 2024. "Sustainable freshwater/energy supply through geothermal-centered layout tailored with humidification-dehumidification desalination unit; Optimized by regression machine learning techniques," Energy, Elsevier, vol. 303(C).
    3. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    4. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Diego Lopez-Bernal & David Balderas & Pedro Ponce & Arturo Molina, 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems," Future Internet, MDPI, vol. 13(8), pages 1-14, July.
    6. Jing Bai & Jiahui Wang & Jin Ran & Xingyuan Li & Chuang Tu, 2024. "An Improved Neural Network Algorithm for Energy Consumption Forecasting," Sustainability, MDPI, vol. 16(21), pages 1-19, October.
    7. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
    8. Teklebrhan Negash & Erik Möllerström & Fredric Ottermo, 2020. "An Assessment of Wind Energy Potential for the Three Topographic Regions of Eritrea," Energies, MDPI, vol. 13(7), pages 1-12, April.
    9. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    10. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    11. Marcin Rabe & Dalia Streimikiene & Yuriy Bilan, 2020. "Model of Optimization of Wind Energy Production in the Light of Legal Changes in Poland," Energies, MDPI, vol. 13(7), pages 1-15, March.
    12. Oh, Jiyoung & Min, Daiki, 2024. "Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics," Energy, Elsevier, vol. 300(C).
    13. He, Yan-Lin & Wang, Ping-Jiang & Zhang, Ming-Qing & Zhu, Qun-Xiong & Xu, Yuan, 2018. "A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry," Energy, Elsevier, vol. 147(C), pages 418-427.
    14. Hadi Sasana & Achma Hendra Setiawan & Fitri Ariyanti & Imam Ghozali, 2017. "The Effect of Energy Subsidy on the Environmental Quality in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 7(5), pages 245-249.
    15. Sajjad M. Vatanchi & Hossein Etemadfard & Mahmoud F. Maghrebi & Rouzbeh Shad, 2023. "A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM and CNN-GRU-LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4769-4785, September.
    16. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    17. Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.
    18. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    19. Bibhuti Bhusan Sahoo & Sovan Sankalp & Ozgur Kisi, 2023. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4271-4292, September.
    20. Liu, Jiaming & Zhang, Hui & Ding, Ning & Li, Yuntao, 2024. "A modified social force model for sudden attack evacuation based on Yerkes–Dodson law and the tendency toward low risk areas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:643:y:2024:i:c:s0378437124003042. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.