A predictive data-driven model for traffic-jams forecasting in Smart Santader city-scale testbed

Treboux, Jerome (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Jara, Antonio J (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Dufour, Luc (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Genoud, Dominique (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

In this paper, a model for traffic jam prediction using data about traffic, weather and noise is presented. It is based on data coming from a smart city in Spain called Santander. The project in this city is called ”Smart Santander” and provides a platform for large-scale experiment based on realtime data. This paper demonstrates the possibility of predicting traffic jams and is a basis to integrate in projects to improve the quality of services. In this work, a cross validation method to ratify our training set is proposed. Data intelligence analysis techniques are used for the prediction with an implementation of Neural Network and Decision Tree algorithms. These algorithms are using different parameters coming from Smart Santander and other external sources. Furthermore, a cross validation process is also integrated to improve the final result. The traffic jam prediction for the next 15 minutes reached an accuracy of 99.95%.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Orleans, USA, 9-12 March 2015
Date:
Orleans, USA
9-12 March 2015
2015
Pagination:
5 p.
Published in:
Proceedings of Wireless Communications and Networking Conference Workshops (WCNCW) 2015
DOI:
ISSN:
978-1-4799-8760-3
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2015-11-12, last modified 2019-06-11

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