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Estimating Time Lost on Semaphores with Deep Learning

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New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence (DiTTEt 2021)

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

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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.

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.

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Notes

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Authors and Affiliations

  1. Expert Systems and Applications Lab—ESALAB, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008, Salamanca, Spain

    Francisco García Encinas, Helena Hernández Payo & Juan Francisco de Paz Santana

  2. Data Mining Research Group—MiDa, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008, Salamanca, Spain

    María N. Moreno García

  3. Artificial Intelligence Department, Polytechnic University of Madrid, Bloque 2, Despacho 2101, Campus Montegancedo, 28660, Boadilla del Monte, Madrid, Spain

    Javier Bajo Pérez

Authors
  1. Francisco García Encinas
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  2. Helena Hernández Payo
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  3. Juan Francisco de Paz Santana
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  4. María N. Moreno García
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  5. Javier Bajo Pérez
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Corresponding author

Correspondence to Francisco García Encinas .

Editor information

Editors and Affiliations

  1. Faculty of Science, University of Salamanca, Salamanca, Spain

    Juan F. de Paz Santana

  2. Facultad de Informática, Universidad Pontificia de Salamanca, Salamanca, Spain

    Daniel H. de la Iglesia

  3. Facultad de Informática, Pontifical University of Salamanca, Salamanca, Spain

    Alfonso José López Rivero

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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

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