Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial

Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence

Antonio Gutierrez-Lythgoe

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Artificial Intelligence; Urban mobility; Deep Learning; Machine Learning; sustainability (search for similar items in EconPapers)
JEL-codes: C45 C53 Q56 R41 R42 (search for similar items in EconPapers)
Date: 2023-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-env and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:117103

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