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Dynamic Path Planning Based on Traffic Flow Prediction and Traffic Light Status

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14487))

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Abstract

Traffic flow prediction and path planning are crucial components of effective intelligent transportation systems research. The intelligent transportation system can optimize vehicle driving routes by utilizing predicted traffic flow data for each road segment and considering the periodic changes in traffic light patterns at intersections. However, most studies on traffic flow prediction have overlooked the frequency domain information of traffic flow sequences, resulting in a lack of effective modeling of this vital frequency domain information. Furthermore, existing path planning approaches only consider factors such as traffic density and road length in decision-making, neglecting the influence of traffic light status on vehicle travel time. We propose a traffic flow prediction model called mWDN-LSTM-ARIMA to address these issues, incorporating frequency feature extraction and residual testing. Additionally, we present a path planning method that leverages the traffic flow predictions from mWDN-LSTM-ARIMA and takes into account the periodic transformation law of traffic lights at urban intersections. Our experimental results validate the effectiveness of the proposed approach in reducing the average travel time and waiting count of vehicles.

This work was supported by National Natural Science Foundation of China (No. 62272357), Key Research and Development Program of Hubei (No. 2022BAA052), Key Research and Development Program of Hainan (No. ZDYF2021GXJS014), Science Foundation of Chongqing of China (cstc2021jcyj-msxm4262), and Research Project of Chongqing Research Institute of Wuhan University of Technology (ZD2021-04, ZL2021-05).

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China

    Weiyang Chen, Bingyi Liu, Weizhen Han & Bin Song

  2. Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, 572000, China

    Weiyang Chen & Weizhen Han

  3. Chongqing Research Institute, Wuhan University of Technology, Chongqing, 401135, China

    Bingyi Liu

  4. School of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Gaolei Li

Authors
  1. Weiyang Chen
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  2. Bingyi Liu
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  3. Weizhen Han
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  4. Gaolei Li
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  5. Bin Song
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Corresponding author

Correspondence to Bingyi Liu .

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Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zahir Tari

  2. Tianjin University, Tianjin, China

    Keqiu Li

  3. University of Arizona, Tucson, AZ, USA

    Hongyi Wu

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Chen, W., Liu, B., Han, W., Li, G., Song, B. (2024). Dynamic Path Planning Based on Traffic Flow Prediction and Traffic Light Status. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14487. Springer, Singapore. https://doi.org/10.1007/978-981-97-0834-5_24

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  • DOI: https://doi.org/10.1007/978-981-97-0834-5_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0833-8

  • Online ISBN: 978-981-97-0834-5

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