Publication detail

Advanced Optimization Method for Improving the Urban Traffic Management

FUJDIAK, R. MAŠEK, P. MLÝNEK, P. MIŠUREC, J. MUTHANNA, A.

Original Title

Advanced Optimization Method for Improving the Urban Traffic Management

Czech Title

Advanced Optimization Method for Improving the Urban Traffic Management

English Title

Advanced Optimization Method for Improving the Urban Traffic Management

Type

conference paper

Language

en

Original Abstract

The Smart City as a concept of future cities anticipates the smart and efficient traffic management. Current situation of traffic management did not offer a sufficient solution and it is not wise to use the current technology to improve the traffic situation on the city roads. This paper deals with advanced methods for optimization, the genetic algorithms, for using in urban traffic management. Implementation of genetic algorithm and also implementation of classical static solution were provided. We try to prove the advantages of modern optimization methods, which could bring more fluent traffic to the cities and solve the current challenges as i.e. high emissions, big delay, higher probability of accidents. The paper provides comparison measurements of static and dynamic solution in discrete time, discussion of the possible implementation in praxis and evaluation of the advantages and disadvantages for both methods.

Czech abstract

The Smart City as a concept of future cities anticipates the smart and efficient traffic management. Current situation of traffic management did not offer a sufficient solution and it is not wise to use the current technology to improve the traffic situation on the city roads. This paper deals with advanced methods for optimization, the genetic algorithms, for using in urban traffic management. Implementation of genetic algorithm and also implementation of classical static solution were provided. We try to prove the advantages of modern optimization methods, which could bring more fluent traffic to the cities and solve the current challenges as i.e. high emissions, big delay, higher probability of accidents. The paper provides comparison measurements of static and dynamic solution in discrete time, discussion of the possible implementation in praxis and evaluation of the advantages and disadvantages for both methods.can serve as building block in future IoT / Smart home implementations.

English abstract

The Smart City as a concept of future cities anticipates the smart and efficient traffic management. Current situation of traffic management did not offer a sufficient solution and it is not wise to use the current technology to improve the traffic situation on the city roads. This paper deals with advanced methods for optimization, the genetic algorithms, for using in urban traffic management. Implementation of genetic algorithm and also implementation of classical static solution were provided. We try to prove the advantages of modern optimization methods, which could bring more fluent traffic to the cities and solve the current challenges as i.e. high emissions, big delay, higher probability of accidents. The paper provides comparison measurements of static and dynamic solution in discrete time, discussion of the possible implementation in praxis and evaluation of the advantages and disadvantages for both methods.

Keywords

Traffic Management, Genetic Algorithms, Optimalization methods, Smart cities

Released

22.04.2016

Location

St. Petersburg

ISBN

978-952-68397-3-8

Book

Proceedings of the 18th FRUCT & ISPIT Conference, 18-22 April 2016, Technopark of ITMO University, Saint-Petersburg, Russia. FRUCT Oy, Finland.

Pages from

1

Pages to

7

Pages count

6

BibTex


@inproceedings{BUT124124,
  author="Radek {Fujdiak} and Pavel {Mašek} and Petr {Mlýnek} and Jiří {Mišurec} and Ammar {Muthanna}",
  title="Advanced Optimization Method for Improving the Urban Traffic Management",
  annote="The Smart City as a concept of future cities anticipates the smart and efficient traffic management. Current
situation of traffic management did not offer a sufficient solution and it is not wise to use the current technology to improve the traffic situation on the city roads. This paper deals with advanced methods for optimization, the genetic algorithms, for using in urban traffic management. Implementation of genetic
algorithm and also implementation of classical static solution were provided. We try to prove the advantages of modern optimization methods, which could bring more fluent traffic to the cities and solve the current challenges as i.e. high emissions, big delay, higher probability of accidents. The paper provides comparison
measurements of static and dynamic solution in discrete time, discussion of the possible implementation in praxis and evaluation of the advantages and disadvantages for both methods.",
  booktitle="Proceedings of the 18th FRUCT & ISPIT Conference, 18-22 April 2016, Technopark of ITMO University, Saint-Petersburg, Russia. FRUCT Oy, Finland.",
  chapter="124124",
  howpublished="online",
  year="2016",
  month="april",
  pages="1--7",
  type="conference paper"
}