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A deep learning approach to real-time parking availability prediction for smart cities

Author
Arjona, J.; Linares, M. P.; Casanovas, J.
Type of activity
Presentation of work at congresses
Name of edition
Second International Conference on Data Science, E-Learning and Information Systems 2019
Date of publication
2019
Presentation's date
2019-12-04
Book of congress proceedings
DATA'19: International Conference on Data Science, E-learning and Information Systems 2019: Dubai, United Arab Emirates: december, 2019: proceedings
First page
1
Last page
7
DOI
10.1145/3368691.3368707
URL
https://dl.acm.org/doi/10.1145/3368691.3368707 Open in new window
Abstract
Nowadays, urban traffic affects the quality of life in cities and metropolitan areas as the problem becomes ever more exacerbated by parking issues: congestion increases due to drivers looking for slots to park their vehicles. An Internet of Things approach permits drivers to know the parking space availability in real time through wireless networks of sensor devices. This research focuses on studying the data generated by parking systems in order to develop predictive models that generate forec...
Group of research
IMP - Information Modelling and Processing
inLab FIB

Participants