Abstract
Urban cycling is a sustainable mode of transportation in large cities, and it offers many advantages. It is an eco-friendly means of transport that is accessible to the population and easy to use. Additionally, it is more economical than other means of transportation. Urban cycling is beneficial for physical health and mental well-being. Achieving sustainable mobility and the evolution towards smart cities demands a comprehensive analysis of all the essential aspects that enable their inclusion. Road safety is particularly important, which must be prioritized to ensure safe transportation and reduce the incidence of road accidents. In order to help reduce the number of accidents that urban cyclists are involved in, this work proposes an alternative solution in the form of an intelligent computational assistant that utilizes simplified machine learning to detect potential risks of unexpected collisions. This technological approach serves as a helpful alternative to the current problem. Through our methodology, we were able to identify the problem involved in the research, design and development of the solution proposal, collect and analyze data, and obtain preliminary results. These results experimentally demonstrate how the proposed model outperforms most state-of-the-art Siamese network models that use a similarity layer based on the Euclidean or Mahanthan distances for small sets of images.
Supported by Project 20220268, Programa Institucional de Formación de Investigadores (PIFI), Instituto Politécnico Nacional (IPN), México.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Caterini, A.L., Chang, D.E.: Deep Neural Networks in a Mathematical Framework, 1st edn. Springer, New York (2018). https://doi.org/10.1007/978-3-319-75304-1
Chen, Z., Fu, Y., Zhang, Y., Jiang, Y.G., Xue, X., Sigal, L.: Multi-level semantic feature augmentation for one-shot learning. IEEE Trans. Image Process. 28(9), 4594–4605 (2019). https://doi.org/10.1109/TIP.2019.2910052
y Christian Paulina Mendoza Torres, R.H.S.: Metodología de la Investigación: Las rutas cuantitativa, cualitativa y mixta. McGraw-Hill Interamericana (2018)
Cuomo, S., Di Cola, V.S., Giampaolo, F., Rozza, G., Raissi, M., Piccialli, F.: Scientific machine learning through physics–informed neural networks: where we are and what’s next. J. Sci. Comput. 92(3), 88 (2022). https://doi.org/10.1007/s10915-022-01939-z
Galán, R., Calle, M., García, J.M.: Análisis de variables que influencian la accidentalidad ciclista: desarrollo de modelos y diseño de una herramienta de ayuda. In: XIII Congreso de Ingeniería de Organización Barcelona-Terrassa, 2-4 September 2009, pp. 696–703. Asociación para el Desarrollo de la Ingeniería de Organización - ADINGOR (2009)
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, pp. 770–778 (2016)
Hernández-Herrera, A., Espino, E.R., Álvarez Vargas, R., Ponce, V.H.P.: Una exploración sobre el aprendizaje automático simplificado: Generalización a partir de algunos ejemplos. Komputer Sapiens 3, 36–41(13) (2021)
Hilmkil, A., Ivarsson, O., Johansson, M., Kuylenstierna, D., van Erp, T.: Towards machine learning on data from professional cyclists (2018)
INEGI: Estadísticas a propósito del Día de Muertos, DATOS NACIONALES. Technical report, Instituto Nacional de Estadística y Geografía, México (2019)
ITDP: Manual Ciclociudades I. la movilidad en bicicleta como política pública. In: Manual Ciclociudades, vol. 1, p. 62. Instituto de Políticas para el Transporte y el Desarrollo, México D.F. (2011)
Khosla, P., et al.: Supervised contrastive learning (2021)
Koch, G.R.: Siamese neural networks for one-shot image recognition. In: Proceedings of the 32nd International Conference on Machine Learning (2015)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report 0, University of Toronto, Toronto, Ontario (2009). https://www.cs.toronto.edu/kriz/learning-features-2009-TR.pdf
Lee, S.W., O’Doherty, J.P., Shimojo, S.: Neural computations mediating one-shot learning in the human brain. PLOS Biol. 13, 1–36 (2015). https://doi.org/10.1371/journal.pbio.1002137
Li, X., Yu, L., Fu, C.W., Fang, M., Heng, P.A.: Revisiting metric learning for few-shot image classification. Neurocomputing 406, 49–58 (2020). https://doi.org/10.1016/j.neucom.2020.04.040, https://www.sciencedirect.com/science/article/pii/S092523122030607X
Loquercio, A., Maqueda, A.I., del Blanco, C.R., Scaramuzza, D.: DroNet: learning to fly by driving. IEEE Robot. Autom. Lett. 3(2), 1088–1095 (2018). https://doi.org/10.1109/LRA.2018.2795643
López Gómez, L.: La bicicleta como medio de transporte en la movilidad sustentable. Technical report, Dirección General de Análisis Legislativo, Senado de la República, México (2018)
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, 28 June–2 July 2011, pp. 689–696. Omnipress (2011)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https://proceedings.neurips.cc/paper/2017/file/cb8da6767461f2812ae4290eac7cbc42-Paper.pdf
Srivastava, N., Salakhutdinov, R.: Multimodal learning with deep Boltzmann machines. J. Mach. Learn. Res. 15(1), 2949–2980 (2014)
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc. (2016). https://proceedings.neurips.cc/paper/2016/file/90e1357833654983612fb05e3ec9148c-Paper.pdf
Vision Zero Network: What is vision zero? (2022). https://visionzeronetwork.org/about/what-is-vision-zero/
WHO: Global status report on road safety 2018. Technical report, World Health Organization, Geneva (2018)
Zhao, H., et al.: Unsupervised deep learning to explore streetscape factors associated with urban cyclist safety. In: Qu, X., Zhen, L., Howlett, R.J., Jain, L.C. (eds.) Smart Transportation Systems 2019, pp. 155–164. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-8683-1_16
Acknowledgements
The authors are thankful for the financial support of the projects to the Secretería de Investigación y Posgrado del Instituto Politécnico Nacional with grant numbers: 20220268, 20232264, 20221089 and 20232570, as well as the support from Comisión de Operación y Fomento de Actividades Académicas, BEIFI Program and Consejo Nacional de Humanidades Ciencia y Tecnología (CONAHCYT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hernández-Herrera, A., Rubio-Espino, E., Álvarez-Vargas, R., Ponce-Ponce, V.H. (2024). Intelligent Urban Cycling Assistance Based on Simplified Machine Learning. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-Cities 2023. Communications in Computer and Information Science, vol 1938. Springer, Cham. https://doi.org/10.1007/978-3-031-52517-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-52517-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-52516-2
Online ISBN: 978-3-031-52517-9
eBook Packages: Computer ScienceComputer Science (R0)