Skip to main content
Springer Nature Link
Log in

MM-AQI: A Novel Framework to Understand the Associations Between Urban Traffic, Visual Pollution, and Air Pollution

  • Conference paper
  • First Online:
Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence (IEA/AIE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13343))

Abstract

Understanding the associations between different traffic factors (e.g., time, vehicles, trees, and people) and the air pollution in a particular region is a challenging problem of great concern in Intelligent Transportation Systems. Most previous works primarily focused on efficient prediction of air pollution levels the given traffic imagery data. To the best of our knowledge, there exists no study that tries to discover hidden associations (or correlation) that exist between the traffic factors and the air pollution towards predicting PM2.5 levels within a certain period of time. With this motivation, this paper proposes a novel framework that aims to discover hidden associations that exist between the traffic factors and the air pollution towards predicting air pollution level in short- and medium-term time. Our framework has the following six steps: (i) Extract features from the traffic images using any machine learning algorithm, (ii) generate a new dataset by joining the extracted features dataset and air pollution dataset using time, (iii) transform this new dataset into an uncertain temporal database using fuzzy rules, (iv) apply uncertain periodic-frequent pattern mining techniques to discover hidden associations between various traffic factors and air pollution, (v) estimate air pollution level from a given image using transfer learning on a pre-trained model, and (vi) predict air pollution level using estimated air pollution level and mined patterns dataset. Experimental results show that our method can estimate and predict air pollution level with high accuracy (from 77% to 98%).

Kazuki was the first to introduce the idea presented in this paper.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Visionair (2020). https://vision-air.github.io/

  2. Awan, F.M., Minerva, R., Crespi, N.: Improving road traffic forecasting using air pollution and atmospheric data: experiments based on LSTM recurrent neural networks. Sensors 20(13), 3749 (2020)

    Article  Google Scholar 

  3. Bedregal, B.C.: On interval fuzzy negations. Fuzzy Sets Syst. 161(17), 2290–2313 (2010)

    Article  MathSciNet  Google Scholar 

  4. Bo, Q., Yang, W., Rijal, N., Xie, Y., Feng, J., Zhang, J.: Particle pollution estimation from images using convolutional neural network and weather features. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3433–3437. IEEE (2018)

    Google Scholar 

  5. Cao, F., Bao, Q.: A survey on image semantic segmentation methods with convolutional neural network. In: 2020 International Conference on Communications, Information System and Computer Engineering (CISCE), pp. 458–462. IEEE (2020)

    Google Scholar 

  6. Dao, M.-S., Zettsu, K., Rage, U.K.: IMAGE-2-AQI: aware of the surrounding air qualification by a few images. In: Fujita, H., Selamat, A., Lin, J.C.-W., Ali, M. (eds.) IEA/AIE 2021. LNCS (LNAI), vol. 12799, pp. 335–346. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79463-7_28

    Chapter  Google Scholar 

  7. Ganji, A., Minet, L., Weichenthal, S., Hatzopoulou, M.: Predicting traffic-related air pollution using feature extraction from built environment images. Environ. Sci. Technol. 54(17), 10688–10699 (2020)

    Article  Google Scholar 

  8. Hien, T.T., Chi, N.D.T., Nguyen, N.T., Takenaka, N., Huy, D.H., et al.: Current status of fine particulate matter (pm2.5) in Vietnam’s most populous city, Ho Chi Minh city. Aerosol Air Qual. Res. 19(10), 2239–2251 (2019)

    Article  Google Scholar 

  9. Junfei, Q., Zengzeng, H., Shengli, D.: Prediction of pm2.5 concentration based on weighted bagging and image contrast-sensitive features. Stochastic Environ. Res. Risk Assess. 34(3–4), 561–573 (2020)

    Google Scholar 

  10. Ke, L., Tai, Y.W., Tang, C.K.: Deep occlusion-aware instance segmentation with overlapping bilayers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4019–4028 (2021)

    Google Scholar 

  11. La, T.V., Dao, M.S., Kazuki, Tejima, R.K.U., Zettsu, K.: Improving the awareness of sustainable smart cities by analyzing lifelog images and IoT air pollution data. In: IEEE Big Data, pp. 3589–3594 (2021)

    Google Scholar 

  12. Liang, L., Gong, P.: Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Sci. Rep. 10(1), 1–13 (2020)

    Article  Google Scholar 

  13. Liu, L., Liu, W., Zheng, Y., Ma, H., Zhang, C.: Third-eye: a mobilephone-enabled crowdsensing system for air quality monitoring. Proc. ACM Interactive Mob. Wearable Ubiquitous Technol. 2(1), 1–26 (2018)

    Google Scholar 

  14. Ma, J., Li, K., Han, Y., Yang, J.: Image-based air pollution estimation using hybrid convolutional neural network. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 471–476. IEEE (2018)

    Google Scholar 

  15. Mao, J., Phommasak, U., Watanabe, S., Shioya, H.: Detecting foggy images and estimating the haze degree factor. J. Comput. Sci. Syst. Biol. 7, 226–228 (2014)

    Article  Google Scholar 

  16. Nguyen-Tai, T.L., Nguyen, D.H., Nguyen, M.T., Nguyen, T.D., Dang, T.H., Dao, M.S.: MNR-HCM data: a personal lifelog and surrounding environment dataset in Ho-Chi-Minh city, Viet Nam. In: Proceedings of the 2020 on Intelligent Cross-Data Analysis and Retrieval Workshop, pp. 21–26 (2020)

    Google Scholar 

  17. Núñez-Alonso, D., Pérez-Arribas, L.V., Manzoor, S., Cáceres, J.O.: Statistical tools for air pollution assessment: multivariate and spatial analysis studies in the Madrid region. J. Anal. Methods Chem. 2019, 9753927 (2019)

    Article  Google Scholar 

  18. Pochwała, S., Anweiler, S., Deptuła, A., Gardecki, A., Lewandowski, P., Przysiężniuk, D.: Optimization of air pollution measurements with unmanned aerial vehicle low-cost sensor based on an inductive knowledge management method. Optim. Eng. 22(3), 1783–1805 (2021). https://doi.org/10.1007/s11081-021-09668-2

    Article  Google Scholar 

  19. Uday Kiran, R., Likhitha, P., Dao, M.-S., Zettsu, K., Zhang, J.: Discovering periodic-frequent patterns in uncertain temporal databases. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. CCIS, vol. 1516, pp. 710–718. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92307-5_83

    Chapter  Google Scholar 

  20. Rijal, N., Gutta, R.T., Cao, T., Lin, J., Bo, Q., Zhang, J.: Ensemble of deep neural networks for estimating particulate matter from images. In: 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), pp. 733–738. IEEE (2018)

    Google Scholar 

  21. Shan, Y., Wang, X., Wang, Z., Liang, L., Li, J., Sun, J.: The pattern and mechanism of air pollution in developed coastal areas of china: From the perspective of urban agglomeration. PLoS ONE 15(9), 1–21 (2020)

    Article  Google Scholar 

  22. Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)

    Google Scholar 

  23. Wu, D., Gong, J., Liang, J., Sun, J., Zhang, G.: Analyzing the influence of urban street greening and street buildings on summertime air pollution based on street view image data. ISPRS Int. J. Geo Inf. 9(9), 500 (2020)

    Article  Google Scholar 

  24. Zhao, P., Dao, M.S., Nguyen, T., Nguyen, T.B., Duc-Tien, D.N., Gurrin, C.: Overview of mediaeval 2020 insights for wellbeing: multimodal personal health lifelog data analysis. In: Proceedings of the MediaEval 2020 Workshop, vol. 2882. CEUR-WS.org (2020)

    Google Scholar 

Download references

Acknowledgements

We thank Prof. Rage Uday Kiran, supervisor of Kazuki, for sharing his ideas and expertise on fuzzy sets, uncertain database creation, and mining patterns.

Author information

Authors and Affiliations

  1. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Kazuki Tejima

  2. National Institute of Information and Communications Technology, Koganei, Japan

    Minh-Son Dao & Koji Zettsu

Authors
  1. Kazuki Tejima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minh-Son Dao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koji Zettsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minh-Son Dao .

Editor information

Editors and Affiliations

  1. i-SOMET, Inc., Morioka-shi, Iwate, Japan

    Hamido Fujita

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tejima, K., Dao, MS., Zettsu, K. (2022). MM-AQI: A Novel Framework to Understand the Associations Between Urban Traffic, Visual Pollution, and Air Pollution. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08530-7_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08529-1

  • Online ISBN: 978-3-031-08530-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics