A supervised learning approach to estimating the urban traffic state based on floating car data
by Marouane Mzibri; Abdelilah Maach; Driss Elghanami
International Journal of Multimedia Intelligence and Security (IJMIS), Vol. 3, No. 4, 2020

Abstract: Intelligent transportation systems aim to increase the efficiency of mobility through the use of new information and communication technologies. This paper presents a supervised learning approach to estimate the traffic state in the urban road networks. The data used in this work were collected from the GPS sensors installed on the connected vehicles. We validate the proposed model using the k-fold cross-validation method and we evaluate the prediction accuracy with the root mean square error and the mean absolute error metrics. The proposed model was evaluated by numerical simulation. The results obtained by estimating the mean travel time in the road network using the proposed method showed good accuracy with a ratio of the connected vehicles not exceeding 15%.

Online publication date:: Thu, 06-May-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Multimedia Intelligence and Security (IJMIS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com