Detail publikace

Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression

Originální název

Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression

Anglický název

Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression

Jazyk

en

Originální abstrakt

In this paper we propose a new method for travel time prediction using a support vector regression model (SVR). The inputs of the method are data from license plate detection systems and traffic sensors such as induction loops or radars placed in the area. This method is mainly designed to be capable of dealing with missing values in traffic data. It is able to create many different SVR models with different input variables. These models are dynamicaly switched according to which traffic variables are currently available. The proposed method was compared with a license plate based prediction approach. The results showed that the proposed method provides a prediction of better quality. Moreover, it is available for a longer period of time.

Anglický abstrakt

In this paper we propose a new method for travel time prediction using a support vector regression model (SVR). The inputs of the method are data from license plate detection systems and traffic sensors such as induction loops or radars placed in the area. This method is mainly designed to be capable of dealing with missing values in traffic data. It is able to create many different SVR models with different input variables. These models are dynamicaly switched according to which traffic variables are currently available. The proposed method was compared with a license plate based prediction approach. The results showed that the proposed method provides a prediction of better quality. Moreover, it is available for a longer period of time.

BibTex


@inproceedings{BUT111629,
  author="Jiří {Petrlík} and Otto {Fučík} and Lukáš {Sekanina}",
  title="Multiobjective Selection of Input Sensors for Travel Times Forecasting Using Support Vector Regression",
  annote="
In this paper we propose a new method for travel time prediction using a support
vector regression model (SVR). The inputs of the method are data from license
plate detection systems and traffic sensors such as induction loops or radars
placed in the area. This method is mainly designed to be capable of dealing with
missing values in traffic data. It is able to create many different SVR models
with different input variables. These models are dynamicaly switched according to
which traffic variables are currently available. The proposed method was compared
with a license plate based prediction approach. The results showed that the
proposed method provides a prediction of better quality. Moreover, it is
available for a longer period of time.",
  address="Institute of Electrical and Electronics Engineers",
  booktitle="2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings",
  chapter="111629",
  doi="10.1109/CIVTS.2014.7009472",
  edition="NEUVEDEN",
  howpublished="electronic, physical medium",
  institution="Institute of Electrical and Electronics Engineers",
  year="2014",
  month="december",
  pages="14--21",
  publisher="Institute of Electrical and Electronics Engineers",
  type="conference paper"
}