Detail publikace

Estimation of traffic density map using evolutionary algorithm

Originální název

Estimation of traffic density map using evolutionary algorithm

Anglický název

Estimation of traffic density map using evolutionary algorithm

Jazyk

en

Originální abstrakt

The traffic density map (TDM) represents the density of road network traffic as the number of vehicles per a specific time interval. TDMs are used by traffic experts as a base documentation for planning a new infrastructure (long-term) or by drivers for showing a current trafic status (short-term). We propose two methods for estimation of missing density values in TDMs. In the first method, the problem is formulated relatively strictly in terms of quadratic programming (QP) and a QP solver is utilized to find a solution. The second, more general method is based on a multiobjective genetic algorithm which allows us to find a reasonable compromise among several objectives that a traffic expert may formulate. These two methods can work automatically or they can be used by a traffic expert for an iterative density estimation. Results of experimental evaluation based on real and randomly generated data are presented.

Anglický abstrakt

The traffic density map (TDM) represents the density of road network traffic as the number of vehicles per a specific time interval. TDMs are used by traffic experts as a base documentation for planning a new infrastructure (long-term) or by drivers for showing a current trafic status (short-term). We propose two methods for estimation of missing density values in TDMs. In the first method, the problem is formulated relatively strictly in terms of quadratic programming (QP) and a QP solver is utilized to find a solution. The second, more general method is based on a multiobjective genetic algorithm which allows us to find a reasonable compromise among several objectives that a traffic expert may formulate. These two methods can work automatically or they can be used by a traffic expert for an iterative density estimation. Results of experimental evaluation based on real and randomly generated data are presented.

BibTex


@inproceedings{BUT91286,
  author="Jiří {Petrlík} and Pavol {Korček} and Otto {Fučík} and Marián {Beszédeš} and Lukáš {Sekanina}",
  title="Estimation of traffic density map using evolutionary algorithm",
  annote="The traffic density map (TDM) represents the density of road network traffic as
the number of vehicles per a specific time interval. TDMs are used by traffic
experts as a base documentation for planning a new infrastructure (long-term) or
by drivers for showing a current trafic status (short-term). We propose two
methods for estimation of missing density values in TDMs. In the first method,
the problem is formulated relatively strictly in terms of quadratic programming
(QP) and a QP solver is utilized to find a solution. The second, more general
method is based on a multiobjective genetic algorithm which allows us to find
a reasonable compromise among several objectives that a traffic expert may
formulate. These two methods can work automatically or they can be used by
a traffic expert for an iterative density estimation. Results of experimental
evaluation based on real and randomly generated data are presented.",
  address="IEEE Intelligent Transportation Systems Society",
  booktitle="Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems",
  chapter="91286",
  doi="10.1109/ITSC.2012.6338757",
  edition="NEUVEDEN",
  howpublished="electronic, physical medium",
  institution="IEEE Intelligent Transportation Systems Society",
  year="2012",
  month="september",
  pages="632--637",
  publisher="IEEE Intelligent Transportation Systems Society",
  type="conference paper"
}