Skip to main content

Estimating Time Lost on Semaphores with Deep Learning

  • 505 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1410)

Abstract

Traffic flow congestion is a very present problem on the daily life of citizens of big cities. Furthermore, it is growing by the day because of the increase of population. Furthermore, it has undesirable consequences such as an increase of air pollution levels and a worse life quality. Traditional solutions, such as investing on public transport, are less effective nowadays because of the COVID-19 pandemic. A good alternative are traffic flow optimization methods, e.g., signal on-off times optimization methods. However, these methods use traffic simulators that are very time consuming and typically act as a bottleneck for the optimization algorithm. In this work, we study if and how Deep Learning models could replace traffic simulators for a more performant alternative for its use on optimization methods. We design several network architectures and use them to predict vehicle and pedestrian time lost in a specific intersection of the city of Salamanca (Spain). The best of our models has an average Mean Absolute Error (MAE) lower than a second using 10-fold cross-validation. Finally, we discuss mechanisms to generalize our models to other intersections using only a reduced amount of data.

Keywords

  • Traffic flow optimization
  • Deep Learning
  • Traffic simulation

Francisco García Encinas’ research was partly supported by the Spanish Ministry of Education and Vocational Training (FPU Fellowship under Grant FPU19/02455). This work was supported by the project “Monitoring and tracking systems for the improvement of intelligent mobility and behavior analysis (SiMoMIAC)", financed by the Spanish Agencia Estatal de Investigación with the reference number PID2019-108883RB-C21/AEI/10.13039/501100011033.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-87687-6_4
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   149.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-87687-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   199.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Notes

  1. 1.

    https://www.eclipse.org/sumo/.

References

  1. Abdoos, M., Bazzan, A.L.: Hierarchical traffic signal optimization using reinforcement learning and traffic prediction with long-short term memory. Expert Syst. Appl. 171, 114580 (2021)

    CrossRef  Google Scholar 

  2. Aljohani, T.M., Ebrahim, A., Mohammed, O.: Real-time metadata-driven routing optimization for electric vehicle energy consumption minimization using deep reinforcement learning and markov chain model. Electric Power Syst. Res. 192, 106962 (2021)

    CrossRef  Google Scholar 

  3. Aslani, M., Seipel, S., Mesgari, M.S., Wiering, M.: Traffic signal optimization through discrete and continuous reinforcement learning with robustness analysis in downtown tehran. Adv. Eng. Inf. 38, 639–655 (2018)

    CrossRef  Google Scholar 

  4. Balta, M., Özçeík, Í.: A 3-stage fuzzy-decision tree model for traffic signal optimization in urban city via a sdn based vanet architecture. Future Gener. Comput. Syst. 104, 142–158 (2020)

    CrossRef  Google Scholar 

  5. Celtek, S.A., Durdu, A., Alı, M.E.M.: Real-time traffic signal control with swarm optimization methods. Measurement 166, 108206 (2020)

    CrossRef  Google Scholar 

  6. Dezani, H., Marranghello, N., Damiani, F.: Genetic algorithm-based traffic lights timing optimization and routes definition using petri net model of urban traffic flow. IFAC Proc. Vol. 47(3), 11326–11331 (2014)

    CrossRef  Google Scholar 

  7. Diallo, A., Lozenguez, G., Doniec, A., Mandiau, R.: Comparative evaluation of road traffic simulators based on modelers specifications: an application to intermodal mobility behaviors. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence, vol. 1: ICAART, pp. 265–272. INSTICC, SciTePress (2021). https://doi.org/10.5220/0010238302650272

  8. Essa, M., Sayed, T.: Self-learning adaptive traffic signal control for real-time safety optimization. Accid. Anal. Prevent. 146, 10571 (2020)

    CrossRef  Google Scholar 

  9. Filipowski, J., Kamiński, B., Mashatan, A., Prałat, P., Szufel, P.: Optimization of the cost of urban traffic through an online bidding platform for commuters. Econ. Transp. 25, 100208 (2021). https://doi.org/10.1016/j.ecotra.2021.100208, https://www.sciencedirect.com/science/article/pii/S2212012221000137

  10. Gong, Y., Abdel-Aty, M., Yuan, J., Cai, Q.: Multi-objective reinforcement learning approach for improving safety at intersections with adaptive traffic signal control. Accid. Anal. Prevent. 144, 105655 (2020)

    CrossRef  Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  12. Joo, H., Ahmed, S.H., Lim, Y.: Traffic signal control for smart cities using reinforcement learning. Comput. Commun 154, 324–330 (2020)

    CrossRef  Google Scholar 

  13. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2014)

    Google Scholar 

  14. Koh, S., et al.: Real-time deep reinforcement learning based vehicle navigation. Appl. Soft Comput. 96, 106694 (2020)

    CrossRef  Google Scholar 

  15. Li, Z., Yu, H., Zhang, G., Dong, S., Xu, C.Z.: Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning. Transp. Res. Part C Emerg. Technol. 125, 103059 (2021)

    CrossRef  Google Scholar 

  16. Lu, J., Li, B., Li, H., Al-Barakani, A.: Expansion of city scale, traffic modes, traffic congestion, and air pollution. Cities 108, 102974 (2021)

    CrossRef  Google Scholar 

  17. Ma, W., Wan, L., Yu, C., Zou, L., Zheng, J.: Multi-objective optimization of traffic signals based on vehicle trajectory data at isolated intersections. Transp. Res. Part C Emerg. Technol 120, 102821 (2020)

    CrossRef  Google Scholar 

  18. Maske, H., Chu, T., Kalabić, U.: Control of traffic light timing using decentralized deep reinforcement learning. IFAC-PapersOnLine 53(2), 14936–14941 (2020)

    CrossRef  Google Scholar 

  19. Nadrian, H., Taghdisi, M.H., Pouyesh, K., Khazaee-Pool, M., Babazadeh, T.: “i am sick and tired of this congestion’’: Perceptions of sanandaj inhabitants on the family mental health impacts of urban traffic jam. J. Transp. Health 14, 100587 (2019)

    CrossRef  Google Scholar 

  20. Niroumand, R., Tajalli, M., Hajibabai, L., Hajbabaie, A.: Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream. Transp. Res. Part C Emerg. Technol 116, 102659 (2020)

    CrossRef  Google Scholar 

  21. Odeh, S.M., Mora, A.M., Moreno, M.N., Merelo, J.J.: A hybrid fuzzy genetic algorithm for an adaptive traffic signal system. Adv. Fuzzy Syst. 2015, 378156 (2015)

    Google Scholar 

  22. Pell, A., Meingast, A., Schauer, O.: Trends in real-time traffic simulation. Transp. Res. Procedia 25, 1477–1484 (2017)

    CrossRef  Google Scholar 

  23. Tajalli, M., Hajbabaie, A.: Distributed optimization and coordination algorithms for dynamic speed optimization of connected and autonomous vehicles in urban street networks. Transp. Res. Part C Emerg. Technol 95, 497–515 (2018)

    CrossRef  Google Scholar 

  24. Tiikkaja, H., Viri, R.: The effects of covid-19 epidemic on public transport ridership and frequencies. a case study from tampere, Finland. Transp. Res. Interdisc. Perspect. 10, 100348 (2021). https://doi.org/10.1016/j.trip.2021.100348, https://www.sciencedirect.com/science/article/pii/S2590198221000555

  25. Walraven, E., Spaan, M.T., Bakker, B.: Traffic flow optimization: a reinforcement learning approach. Eng. Appl. Artif. Intell 52, 203–212 (2016)

    CrossRef  Google Scholar 

  26. Wang, T., Cao, J., Hussain, A.: Adaptive traffic signal control for large-scale scenario with cooperative group-based multi-agent reinforcement learning. Transp. Res. Part C Emerg. Technol. 125, 103046 (2021)

    CrossRef  Google Scholar 

  27. Wu, J., Chen, X.Y., Zhang, H., Xiong, L.D., Lei, H., Deng, S.H.: Hyperparameter optimization for machine learning models based on bayesian optimizationb. J. Electron. Sci. Technol. 17(1), 26–40 (2019). https://doi.org/10.11989/JEST.1674-862X.80904120, https://www.sciencedirect.com/science/article/pii/S1674862X19300047

  28. Yang, S., Yang, B., Kang, Z., Deng, L.: Ihg-ma: inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control. Neural Netw. 139, 265–277 (2021)

    CrossRef  Google Scholar 

  29. Zhang, Y., Su, R.: An optimization model and traffic light control scheme for heterogeneous traffic systems. Transp. Res. Part C Emerg. Technol. 124, 102911 (2021)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francisco García Encinas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

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

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

García Encinas, F., Hernández Payo, H., de Paz Santana, J.F., Moreno García, M.N., Bajo Pérez, J. (2022). Estimating Time Lost on Semaphores with Deep Learning. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-87687-6_4

Download citation