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EcoLight+: a novel multi-modal data fusion for enhanced eco-friendly traffic signal control driven by urban traffic noise prediction

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Abstract

Urban traffic congestion is of utmost importance for modern societies due to population and economic growth. Thus, it contributes to environmental problems like increasing greenhouse gas emissions and noise pollution. Improved traffic flow in urban networks relies heavily on traffic signal control. Hence, optimizing cycle timing at many intersections is paramount to reducing congestion and increasing sustainability. This paper introduces an alternative to conventional traffic signal control, EcoLight+, which incorporates future noise predictions with the deep dueling Q-network reinforcement Learning algorithm to reduce noise levels, CO\({_{2}}\) emissions, and fuel consumption. An innovative data fusion approach is also proposed to improve our LSTM-based noise prediction model by integrating heterogeneous data from different sources. Our proposed solution allows the system to achieve higher efficiency than its competitors based on real-world data from Tallinn, Estonia.

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Notes

  1. https://inrix.com/scorecard/.

  2. Link to the source code: https://github.com/doua-ounoughi/EcoLightPlus.

  3. https://ristmikud.tallinn.ee/index.php/cams.

  4. https://ristmikud.tallinn.ee/index.php/temperature.

  5. https://www.tomtom.com.

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Acknowledgements

This work was supported by grants to TalTech - TalTech Industrial (H2020, grant No 952410), Estonian Research Council (PRG1573), and EU-Astra - TUT Development Plan for 2016-2022 (ASTRA) reg no. 2014-2020.4.1.16-0032.

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Authors and Affiliations

  1. Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

    Chahinez Ounoughi & Sadok Ben Yahia

  2. LR11ES14, Faculté des Sciences de Tunis, Université de Tunis El Manar, 2093, Tunis, Tunisia

    Chahinez Ounoughi

  3. Faculty of Engineering, Çanakkale Onsekiz Mart University, Çanakkale, Turkey

    Doua Ounoughi

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  1. Chahinez Ounoughi
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  2. Doua Ounoughi
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  3. Sadok Ben Yahia
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CO: Conceptualization, Methodology, Software, Validation, Visualization, Writing - Review & Editing. DO: Methodology, Software. SBY: Review & Editing.

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Correspondence to Chahinez Ounoughi.

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Ounoughi, C., Ounoughi, D. & Ben Yahia, S. EcoLight+: a novel multi-modal data fusion for enhanced eco-friendly traffic signal control driven by urban traffic noise prediction. Knowl Inf Syst 65, 5309–5329 (2023). https://doi.org/10.1007/s10115-023-01938-y

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