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.
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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).
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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
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