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Traffic Flow Prediction with Swiss Open Data: A Deep Learning Approach

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Electronic Government (EGOV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14130))

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Abstract

Open government data (OGD) are provided by the public sector and governments in an open, freely accessible format. Among various types of OGD, dynamic data generated by sensors, such as traffic data, can be utilized to develop innovative artificial intelligence (AI) algorithms and applications. As AI algorithms, specifically Deep Neural Networks, necessitate large amounts of data, dynamic OGD datasets serve as supplemental resources to existing traffic datasets, used for performance comparison and benchmarking. This work examines the effectiveness of using open traffic data from the Swiss open data portal to develop a Graph Neural Network (GNN) model for traffic forecasting. To this end, the objective of this study is to probe the extent to which dynamic OGD can enhance the accuracy and efficiency of traffic forecasting models, and more critically, to investigate the potential of this data in driving the development of cutting-edge AI models for traffic flow prediction. We posit that strategic utilization of such data has the potential to catalyze a transformative shift in the realm of traffic management and control, by fostering intelligent solutions that effectively leverage the predictive capabilities of AI models. The results indicate that the GNN-based algorithm is effective in predicting future traffic flow, outperforming two traditional baselines for time series forecasting.

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Notes

  1. 1.

    https://data.europa.eu/en.

  2. 2.

    https://www.data.gov.gr/datasets/.

  3. 3.

    https://www.vegvesen.no/trafikkdata/api/.

  4. 4.

    https://api.trafikinfo.trafikverket.se.

  5. 5.

    https://opentransportdata.swiss/en/.

  6. 6.

    https://www.datex2.eu/.

  7. 7.

    https://cloud.google.com/.

  8. 8.

    https://pems.dot.ca.gov/.

References

  1. Agafonov, A.: Traffic flow prediction using graph convolution neural networks. In: 2020 10th International Conference on Information Science and Technology (ICIST), pp. 91–95 (2020). https://doi.org/10.1109/ICIST49303.2020.9201971

  2. Ata, A., Khan, M.A., Abbas, S., Khan, M.S., Ahmad, G.: Adaptive IoT empowered smart road traffic congestion control system using supervised machine learning algorithm. Comput. J. 64(11), 1672–1679 (2021)

    Article  Google Scholar 

  3. Attard, J., Orlandi, F., Scerri, S., Auer, S.: A systematic review of open government data initiatives. Gov. Inf. Q. 32(4), 399–418 (2015). https://doi.org/10.1016/j.giq.2015.07.006

    Article  Google Scholar 

  4. Bachechi, C., Rollo, F., Po, L.: Detection and classification of sensor anomalies for simulating urban traffic scenarios. Cluster Comput. 25(4), 2793–2817 (2022). https://doi.org/10.1007/s10586-021-03445-7

    Article  Google Scholar 

  5. Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017). https://doi.org/10.1109/MSP.2017.2693418

    Article  Google Scholar 

  6. Bui, K.H.N., Cho, J., Yi, H.: Spatial-temporal graph neural network for traffic forecasting: an overview and open research issues. Appl. Intell. 52(3), 2763–2774 (2022). https://doi.org/10.1007/s10489-021-02587-w

    Article  Google Scholar 

  7. Cox, P., Alemanno, G.: Directive 2003/98/EC of the European parliament and of the council of 17 November 2003 on the re-use of public sector information. Off. J. Eur. Union 46, 1–156 (2003)

    Google Scholar 

  8. Cui, Z., Ke, R., Pu, Z., Wang, Y.: Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction (2018). https://doi.org/10.48550/ARXIV.1801.02143

  9. Ermagun, A., Levinson, D.: Spatiotemporal traffic forecasting: review and proposed directions. Transp. Rev. 38(6), 786–814 (2018). https://doi.org/10.1080/01441647.2018.1442887

    Article  Google Scholar 

  10. Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328 (2016). https://doi.org/10.1109/YAC.2016.7804912

  11. Gregurić, M., Vujić, M., Alexopoulos, C., Miletić, M.: Application of deep reinforcement learning in traffic signal control: an overview and impact of open traffic data. Appl. Sci. 10(11), 4011 (2020). https://doi.org/10.3390/app10114011

    Article  Google Scholar 

  12. Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 922–929 (2019). https://doi.org/10.1609/aaai.v33i01.3301922

  13. Jiang, W., Luo, J.: Graph neural network for traffic forecasting: a survey. Expert Syst. Appl. 207, 117921 (2022). https://doi.org/10.1016/j.eswa.2022.117921

    Article  Google Scholar 

  14. Kaffash, S., Nguyen, A.T., Zhu, J.: Big data algorithms and applications in intelligent transportation system: a review and bibliometric analysis. Int. J. Prod. Econ. 231, 107868 (2021). https://doi.org/10.1016/j.ijpe.2020.107868

    Article  Google Scholar 

  15. Kalampokis, E., Karacapilidis, N., Tsakalidis, D., Tarabanis, K.: Artificial intelligence and blockchain technologies in the public sector: a research projects perspective. In: Janssen, M., et al. (eds.) EGOV 2022. LNCS, vol. 13391, pp. 323–335. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-15086-9_21

    Chapter  Google Scholar 

  16. Kalampokis, E., Tambouris, E., Tarabanis, K.: A classification scheme for open government data: towards linking decentralised data. Int. J. Web Eng. Technol. 6(3), 266–285 (2011)

    Article  Google Scholar 

  17. Kalampokis, E., Tambouris, E., Tarabanis, K.: Open government data: a stage model. In: Janssen, M., Scholl, H.J., Wimmer, M.A., Tan, Y. (eds.) EGOV 2011. LNCS, vol. 6846, pp. 235–246. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22878-0_20

    Chapter  Google Scholar 

  18. Karamanou, A., Brimos, P., Kalampokis, E., Tarabanis, K.: Exploring the quality of dynamic open government data using statistical and machine learning methods. Sensors 22(24), 9684 (2022). https://doi.org/10.3390/s22249684

    Article  Google Scholar 

  19. Karamanou, A., Brimos, P., Kalampokis, E., Tarabanis, K.: Exploring the quality of dynamic open government data for developing data intelligence applications: the case of attica traffic data. In: Proceedings of the 26th Pan-Hellenic Conference on Informatics, PCI 2022, pp. 102–109. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3575879.3575974

  20. Karamanou, A., Kalampokis, E., Tarabanis, K.: Linked open government data to predict and explain house prices: the case of scottish statistics portal. Big Data Res. 30, 100355 (2022)

    Article  Google Scholar 

  21. Karamanou, A., Kalampokis, E., Tarabanis, K.: Integrated statistical indicators from scottish linked open government data. Data Brief 46, 108779 (2023)

    Article  Google Scholar 

  22. Lana, I., Del Ser, J., Velez, M., Vlahogianni, E.I.: Road traffic forecasting: recent advances and new challenges. IEEE Intell. Transp. Syst. Mag. 10(2), 93–109 (2018). https://doi.org/10.1109/MITS.2018.2806634

    Article  Google Scholar 

  23. Leviäkangas, P., Molarius, R.: Open government data policy and value added-evidence on transport safety agency case. Technol. Soc. 63, 101389 (2020)

    Article  Google Scholar 

  24. Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International Conference on Learning Representations (2017)

    Google Scholar 

  25. Lourenço, R.P.: An analysis of open government portals: a perspective of transparency for accountability. Gov. Inf. Q. 32(3), 323–332 (2015)

    Article  Google Scholar 

  26. Mahrez, Z., Sabir, E., Badidi, E., Saad, W., Sadik, M.: Smart urban mobility: when mobility systems meet smart data. IEEE Trans. Intell. Transp. Syst. 23(7), 6222–6239 (2022). https://doi.org/10.1109/TITS.2021.3084907

    Article  Google Scholar 

  27. Navarro-Espinoza, A., et al.: Traffic flow prediction for smart traffic lights using machine learning algorithms. Technologies 10(1), 5 (2022). https://doi.org/10.3390/technologies10010005

    Article  Google Scholar 

  28. Nikiforova, A.: Smarter open government data for society 5.0: are your open data smart enough? Sensors 21(15), 5204 (2021)

    Article  Google Scholar 

  29. Pang, X., Wang, C., Huang, G.: A short-term traffic flow forecasting method based on a three-layer k-nearest neighbor non-parametric regression algorithm. J. Transp. Technol. 6, 200–206 (2016). https://doi.org/10.4236/jtts.2016.64020

    Article  Google Scholar 

  30. European Parliament: Directive (EU) 2019/1024 of the European parliament and of the council of 20 June 2019 on open data and the re-use of public sector information (recast). Off. J. Eur. Union 172, 56–83 (2019)

    Google Scholar 

  31. Pereira, G.V., Macadar, M.A., Luciano, E.M., Testa, M.G.: Delivering public value through open government data initiatives in a Smart City context. Inf. Syst. Front. 19(2), 213–229 (2016). https://doi.org/10.1007/s10796-016-9673-7

    Article  Google Scholar 

  32. Qi, Y., Cheng, Z.: Research on traffic congestion forecast based on deep learning. Information 14(2), 108 (2023). https://doi.org/10.3390/info14020108

    Article  Google Scholar 

  33. Ruijer, E., Grimmelikhuijsen, S., Meijer, A.: Open data for democracy: developing a theoretical framework for open data use. Gov. Inf. Q. 34(1), 45–52 (2017)

    Article  Google Scholar 

  34. Sun, S., Zhang, C., Yu, G.: A Bayesian network approach to traffic flow forecasting. IEEE Trans. Intell. Transp. Syst. 7(1), 124–132 (2006). https://doi.org/10.1109/TITS.2006.869623

    Article  Google Scholar 

  35. Varga, N., Bokor, L., Takács, A., Kovács, J., Virág, L.: An architecture proposal for V2X communication-centric traffic light controller systems. In: 2017 15th International Conference on ITS Telecommunications (ITST), pp. 1–7. IEEE (2017)

    Google Scholar 

  36. Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003). https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)

    Article  Google Scholar 

  37. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2021). https://doi.org/10.1109/tnnls.2020.2978386

    Article  MathSciNet  Google Scholar 

  38. Yang, Z., Ha, S., Kankanhalli, A., Um, S.: Understanding the determinants of the intention to innovate with open government data among potential commercial innovators: a risk perspective. Internet Res. (2022, ahead-of-print)

    Google Scholar 

  39. Yao, Z., Shao, C., Gao, Y.: Research on methods of short-term traffic forecasting based on support vector regression. J. Beijing Jiaotong Univ. 30(3), 19–22 (2006)

    Google Scholar 

  40. Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization (2018). https://doi.org/10.24963/ijcai.2018/505

  41. Zhang, J., Puron-Cid, G., Gil-Garcia, J.R.: Creating public value through open government: perspectives, experiences and applications. Inf. Polity 20(2–3), 97–101 (2015)

    Article  Google Scholar 

  42. Zhang, Y., Cheng, T., Ren, Y., Xie, K.: A novel residual graph convolution deep learning model for short-term network-based traffic forecasting. Int. J. Geogr. Inf. Sci. 34(5), 969–995 (2020). https://doi.org/10.1080/13658816.2019.1697879

    Article  Google Scholar 

  43. Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2020). https://doi.org/10.1109/tits.2019.2935152

    Article  Google Scholar 

  44. Zhou, Q., Chen, N., Lin, S.: FASTNN: a deep learning approach for traffic flow prediction considering spatiotemporal features. Sensors 22(18), 6921 (2022). https://doi.org/10.3390/s22186921

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Information Systems Lab, Department of Business Administration, University of Macedonia, 54636, Thessaloniki, Greece

    Petros Brimos, Areti Karamanou, Evangelos Kalampokis & Konstantinos Tarabanis

Authors
  1. Petros Brimos
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  2. Areti Karamanou
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  3. Evangelos Kalampokis
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  4. Konstantinos Tarabanis
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Corresponding author

Correspondence to Petros Brimos .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Ida Lindgren

  2. Corvinus University of Budapest, Budapest, Hungary

    Csaba Csáki

  3. University of Macedonia, Thessaloniki, Greece

    Evangelos Kalampokis

  4. Delft University of Technology, Delft, The Netherlands

    Marijn Janssen

  5. University for Continuing Education Krems, Krems an der Donau, Austria

    Gabriela Viale Pereira

  6. Vienna University of Economics and Business, Vienna, Austria

    Shefali Virkar

  7. University of Macedonia, Thessaloniki, Greece

    Efthimios Tambouris

  8. Delft University of Technology, Delft, The Netherlands

    Anneke Zuiderwijk

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Brimos, P., Karamanou, A., Kalampokis, E., Tarabanis, K. (2023). Traffic Flow Prediction with Swiss Open Data: A Deep Learning Approach. In: Lindgren, I., et al. Electronic Government. EGOV 2023. Lecture Notes in Computer Science, vol 14130. Springer, Cham. https://doi.org/10.1007/978-3-031-41138-0_20

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  • DOI: https://doi.org/10.1007/978-3-031-41138-0_20

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  • Online ISBN: 978-3-031-41138-0

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