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

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  • 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
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial Intelligence; Urban mobility; Deep Learning; Machine Learning ; sustainability;
    All these keywords.

    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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