Skip to main content
SpringerLink
Log in

EcoLight: Eco-friendly Traffic Signal Control Driven by Urban Noise Prediction

  • Conference paper
  • First Online:
Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

Included in the following conference series:

Abstract

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. Traffic signal control plays a vital role in improving traffic flow in urban networks. Hence, optimizing cycle timing at many intersections is paramount to reducing congestion and increasing sustainability. In this paper, we introduce an alternative to conventional traffic signal control, namely EcoLight, that provides significant improvements in noise levels, CO2 emissions, and fuel consumption, resulting from the incorporation of future noise predictions. A Sequence to Sequence Long Short Term Memory (SeqtoSeq-LSTM) prediction model, combined with a deep reinforcement learning algorithm, allows the system to achieve higher efficiency than its competitors based on real-world data from Helsinki, Finland.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmad Rafidi, M.A., Abdul Hamid, A.H.: Synchronization of traffic light systems for maximum efficiency along jalan bukit gambier, penang, malaysia. SHS Web Conf. 11, 01006 (2014). https://doi.org/10.1051/shsconf/20141101016

  2. Ahmed, A.A., Pradhan, B., Chakraborty, S., Alamri, A., Lee, C.W.: An optimized deep neural network approach for vehicular traffic noise Trend modeling. IEEE Access 9(1995), 107375–107386 (2021). https://doi.org/10.1109/ACCESS.2021.3100855

    Article  Google Scholar 

  3. Alaidi, A.H., Aljazaery, I., Alrikabi, H., Mahmood, I., Abed, F.: Design and implementation of a smart traffic light management system controlled wirelessly by arduino. Int. J. Inter. Mobile Technol. (iJIM) 14(07), 32–40 (2020)

    Article  Google Scholar 

  4. Box, G.E.P., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65(332), 1509–1526 (1970). https://doi.org/10.1080/01621459.1970.10481180

    Article  MathSciNet  MATH  Google Scholar 

  5. Bravo, Y., Ferrer, J., Luque, G., Alba, E.: Smart mobility by optimizing the traffic lights: a new tool for traffic control centers. In: Alba, E., Chicano, F., Luque, G. (eds.) Smart-CT 2016. LNCS, vol. 9704, pp. 147–156. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39595-1_15

    Chapter  Google Scholar 

  6. CALSTART: Drive to zero’s zero-emission technology inventory (zeti) (2020). https://globaldrivetozero.org/tools/zero-emission-technology-inventory/

  7. Chen, C., et al.: Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3414–3421 (2020)

    Google Scholar 

  8. EEA: Road traffic remains biggest source of noise pollution in europe (2017). https://www.eea.europa.eu/highlights/road-traffic-remains-biggest-source

  9. Helsinki, E.O.: Helsinki region infoshare (May 2022). https://hri.fi/

  10. Khan, J., Ketzel, M., Jensen, S.S., Gulliver, J., Thysell, E., Hertel, O.: Comparison of Road Traffic Noise prediction models: CNOSSOS-EU, Nord 2000 and TRANEX. Environ. Pollut. 270, 116240 (2021). https://doi.org/10.1016/j.envpol.2020.116240

    Article  Google Scholar 

  11. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)

    Google Scholar 

  12. Le, T., Kovács, P., Walton, N., Vu, H.L., Andrew, L.L., Hoogendoorn, S.S.: Decentralized signal control for urban road networks. Trans. Res. Part C: Emer. Technol. 58, 431–450 (2015). https://doi.org/10.1016/j.trc.2014.11.009

    Article  Google Scholar 

  13. Liu, Q., Cai, Y., Jiang, H., Lu, J., Chen, L.: Traffic state prediction using ISOMAP manifold learning. Phys. A 506, 532–541 (2018). https://doi.org/10.1016/j.physa.2018.04.031

    Article  Google Scholar 

  14. Lonnrotinkatu: Helsinki metropolitan traffic noise dataset, January 2012. https://hri.fi/

  15. Ng, S.C., Kwok, C.P.: An intelligent traffic light system using object detection and evolutionary algorithm for alleviating traffic congestion in hong kong. Int. J. Comput. Intell. Syst. 13(1), 802–809 (2020). https://doi.org/10.2991/ijcis.d.200522.001

    Article  Google Scholar 

  16. Ounoughi, C., Yeferny, T., Ben Yahia, S.: Zed-tte: zone embedding and deep neural network based travel time estimation approach. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–10 (2021). https://doi.org/10.1109/IJCNN52387.2021.9533456

  17. Salin, S.: Petssa: Priority-driven enhanced traffic signal scheduling algorithm, May 2022. https://github.com/habe33/tammsaare-sopruse

  18. Sanvicente, E., Kielmanowicz, D., Rodenbach, J., Chicco, A., Ramos, E.: Key technology and social innovation drivers for car sharing. deliverable 2.2 of the stars h2020 project. Tech. rep. (2020)

    Google Scholar 

  19. Singh, D., Upadhyay, R., Pannu, H.S., Leray, D.: Development of an adaptive neuro fuzzy inference system based vehicular traffic noise prediction model. J. Ambient. Intell. Humaniz. Comput. 12(2), 2685–2701 (2021). https://doi.org/10.1007/s12652-020-02431-y

    Article  Google Scholar 

  20. Staab, J., Schady, A., Weigand, M., Lakes, T., Taubenböck, H.: Predicting traffic noise using land-use regression-a scalable approach. J. Ex. Sci. Environ. Epidemiol. 32, 1–12 (2021). https://doi.org/10.1038/s41370-021-00355-z

    Article  Google Scholar 

  21. SUMO: Simulation of urban mobility, May 2022. https://sumo.dlr.de/docs/index.html

  22. Wei, H., Chen, C., Zheng, G., Wu, K., Gayah, V., Xu, K., Li, Z.: Presslight: learning max pressure control to coordinate traffic signals in arterial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019. pp. 1290–1298. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3292500.3330949, https://doi.org/10.1145/3292500.3330949

  23. Wei, H., et al.: Colight: learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 1913–1922. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3357384.3357902, https://doi.org/10.1145/3357384.3357902

  24. Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: a reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 2496–2505. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3219819.3220096

  25. Xiong, Y., Zheng, G., Xu, K., Li, Z.: Learning traffic signal control from demonstrations. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 2289–2292. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3357384.3358079, https://doi.org/10.1145/3357384.3358079

  26. Zang, X., Yao, H., Zheng, G., Xu, N., Xu, K., Li, Z.: Metalight: value-based meta-reinforcement learning for traffic signal control. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1153–1160 (2020)

    Google Scholar 

  27. Zhang, B., Zhao, C.: Dynamic turning force prediction and feature parameters extraction of machine tool based on arma and hht. Proc. Institu. Mech. Eng. Part C: J. Mech. Eng. Sci. 234(5), 1044–1056 (2020)

    Article  Google Scholar 

  28. Zhang, H., Liu, C., Zhang, W., Zheng, G., Yu, Y.: Generalight: improving environment generalization of traffic signal control via meta reinforcement learning. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1783–1792 (2020)

    Google Scholar 

  29. Zhang, X., Kuehnelt, H., De Roeck, W.: Traffic noise prediction applying multivariate bi-directional recurrent neural network. Appli. Sci. (Switzerland) 11(6) (2021). https://doi.org/10.3390/app11062714

  30. Zheng, G., et al.: Learning phase competition for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 pp. 1963–1972. Association for Computing Machinery, New York, (2019). https://doi.org/10.1145/3357384.3357900, https://doi.org/10.1145/3357384.3357900

Download references

Acknowledgment

This work was supported by grants to TalTech - TalTech Industrial (H2020, grant No 952410) and Estonian Research Council (PRG1573).

Author information

Authors and Affiliations

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

    Chahinez Ounoughi & Sadok Ben Yahia

  2. Université de Tunis El Manar, Faculté des Sciences de Tunis, LR11ES14, 2092, Tunis, Tunisia

    Chahinez Ounoughi & Ghofrane Touibi

Authors
  1. Chahinez Ounoughi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghofrane Touibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sadok Ben Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chahinez Ounoughi .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ounoughi, C., Touibi, G., Yahia, S.B. (2022). EcoLight: Eco-friendly Traffic Signal Control Driven by Urban Noise Prediction. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-12423-5_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12422-8

  • Online ISBN: 978-3-031-12423-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation