Improving parking availability information using deep learning techniques
Urban traffic currently affects the quality of life in cities and metropolitan areas as the problem becomes ever more aggravated by parking issues: congestion increases due to individuals looking for slotsto park their vehicles. An Internet of Things approach allows drivers to know the state of the...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/343786 |
| Acceso en línea: | https://hdl.handle.net/2117/343786 https://dx.doi.org/10.1016/j.trpro.2020.03.113 |
| Access Level: | acceso abierto |
| Palabra clave: | Urban transportation Deep learning Electronic villages (Computer networks)) Automobile parking parking availability forecast deep learning smart cities Transport urbà Aprenentatge profund Ciutats digitals (Xarxes d'ordinadors) Automòbils -- Aparcament Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports |
| Sumario: | Urban traffic currently affects the quality of life in cities and metropolitan areas as the problem becomes ever more aggravated by parking issues: congestion increases due to individuals looking for slotsto park their vehicles. An Internet of Things approach allows drivers to know the state of the parkingsystem in real time through wireless networks of sensor devices. This work focuses on studying the data generated by parking systems in order to develop predictive models that generate forecasted information. This can be useful in improving the managementof parking areas, especially on-streetparking, while having an important effect on urban traffic. This research begins by looking at thestate of the art in predictive methods based on machine learning for time series. Similar studies and proposed solutions for parking predictionare described in terms of the technology and current state-of-the-art predictive models. This paper then introduces the recurrent neural network methodsthat were usedin this research,namely Long Short-Term Memory and Gated Recurrent Unit, as well as the models developed according to real scenarios in different cities. In order to improve the quality of the models, exogenous variables like hourly weather and calendar effects are taken into account,and the baseline models are compared to the models that usedthis information. Finally, the preliminary encouraging results are described, followed by suggestions for corresponding future work |
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