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A Novel Approach for Improved Pedestrian Walking Speed Prediction: Exploiting Proximity Correlation

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1964))

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Abstract

Accurately predicting pedestrian speed is crucial for analyzing pedestrian behavior and optimizing intelligent transportation systems. This paper investigates the feasibility of modeling pedestrian walking speed as a time series. Building upon previous research highlighting the spatio-temporal nearest neighbor correlation in pedestrian walking speed, we propose a deep learning method that leverages this correlation. Experimental results demonstrate the superiority of our approach over traditional methods in accurately predicting pedestrian walking speed and capturing temporal characteristics and trends. The findings of this study have significant implications for enhancing pedestrian traffic flow management, improving the pedestrian travel experience, and enhancing overall traffic safety. Future research can focus on exploring advanced time series methods and deep learning models to further enhance the accuracy and practicality of pedestrian walking speed prediction.

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Authors and Affiliations

  1. Wuhan Research Institute of Posts and Telecommunications, Wuhan, China

    Xiaohe Chen, Zhiyong Tao & Yuanzhen Zhou

  2. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China

    Xiaohe Chen & Mei Wang

Authors
  1. Xiaohe Chen
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  2. Zhiyong Tao
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  3. Mei Wang
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  4. Yuanzhen Zhou
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Corresponding author

Correspondence to Zhiyong Tao .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, X., Tao, Z., Wang, M., Zhou, Y. (2024). A Novel Approach for Improved Pedestrian Walking Speed Prediction: Exploiting Proximity Correlation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1964. Springer, Singapore. https://doi.org/10.1007/978-981-99-8141-0_29

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  • DOI: https://doi.org/10.1007/978-981-99-8141-0_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8140-3

  • Online ISBN: 978-981-99-8141-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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