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Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial
[Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence]

Author

Listed:
  • Gutierrez-Lythgoe, Antonio

Abstract

The evolution of cities has led to changes in urban mobility patterns, including an increased number of trips, longer and more dispersed routes. Therefore, it is crucial to study urban mobility efficiently to promote sustainability and well-being. In this context, we reviewed the existing literature on the applications of artificial intelligence (AI) in urban mobility research, specifically focusing on Deep Learning techniques such as CNN and LSTM models. These AI tools are being used to address the challenges of urban mobility research and offer new possibilities for tackling the pressing issues faced by cities, such as sustainability in transportation. AI can contribute to improving sustainability by predicting real-time traffic, optimizing transportation efficiency, and informing public policies that promote sustainable modes of transportation. In this study, we propose a Random Forest model for predicting demand for sustainable urban mobility based on machine learning, achieving accurate and consistent predictions. Overall, the application of AI in urban mobility research presents a unique opportunity to advance towards more sustainable, livable cities and resilient societies.

Suggested Citation

  • Gutierrez-Lythgoe, Antonio, 2023. "Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial [Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence]," MPRA Paper 117103, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:117103
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    References listed on IDEAS

    as
    1. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    2. José Ignacio Gimenez‐Nadal & José Alberto Molina & Jorge Velilla, 2019. "Modelling commuting time in the US: Bootstrapping techniques to avoid overfitting," Papers in Regional Science, Wiley Blackwell, vol. 98(4), pages 1667-1684, August.
    3. Giménez-Nadal, José Ignacio & Gracia-Lázaro, Carlos & Molina, José Alberto, 2022. "Increasing the use of public bicycles: Efficiency and demand," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 745-754.
    4. J. I. Gimenez-Nadal & J. A. Molina & J. Velilla, 2022. "Commuting time and sickness absence of US workers," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(3), pages 691-719, August.
    5. José Alberto Molina & J. Ignacio Giménez-Nadal & Jorge Velilla, 2020. "Sustainable Commuting: Results from a Social Approach and International Evidence on Carpooling," Sustainability, MDPI, vol. 12(22), pages 1-12, November.
    6. Ruben Talavera-Garcia & Gustavo Romanillos & Daniela Arias-Molinares, 2021. "Examining spatio-temporal mobility patterns of bike-sharing systems: the case of BiciMAD (Madrid)," Journal of Maps, Taylor & Francis Journals, vol. 17(1), pages 7-13, January.
    7. Giménez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2021. "Two-way commuting: Asymmetries from time use surveys," Journal of Transport Geography, Elsevier, vol. 95(C).
    8. J. Ignacio Gimenez-Nadal & José Alberto Molina, 2014. "Commuting Time and Labour Supply in the Netherlands A Time Use Study," Journal of Transport Economics and Policy, University of Bath, vol. 48(3), pages 409-426, September.
    9. Giménez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2020. "Commuting and self-employment in Western Europe," Journal of Transport Geography, Elsevier, vol. 88(C).
    10. Lambert, Karras J. & Fegley, Tate, 2023. "Economic Calculation in Light of Advances in Big Data and Artificial Intelligence," Journal of Economic Behavior & Organization, Elsevier, vol. 206(C), pages 243-250.
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    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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