Centralized simulated annealing for alleviating vehicular congestion in smart cities

Hayder M. Amer, Hayder Al-Kashoash, Matthew Hawes, Moumena Chaqfeh, Andrew Kemp, Lyudmila Mihaylova

Research output: Contribution to journalArticlepeer-review


Vehicular traffic congestion is a serious problem arising in many cities around the world, due to the increasing number of vehicles utilizing roads of a limited capacity. Often the congestion has a considerable influence on the travel time, travel distance, fuel consumption and air pollution. This paper proposes a novel dynamic centralized simulated annealing based approach for finding optimal vehicle routes using a VIKOR type of cost function. Five attributes: the average travel speed of the traffic, vehicles density, roads width, road traffic signals and the roads' length are utilized by the proposed approach to find the optimal paths. The average travel speed and vehicles density values can be obtained from the sensors deployed in smart cities and communicated to vehicles and roadside communication units via vehicular ad hoc networks. The performance of the proposed algorithm is compared with four other algorithms, over two test scenarios: Birmingham and Turin city centres. These show the proposed method improves traffic efficiency in the presence of congestion by an overall average of 24.05%, 48.88% and 36.89% in terms of travel time, fuel consumption and CO2 emission, respectively, for a test scenario from Birmingham city in the UK. Additionally, similar performance patterns are achieved for the a test with data from Turin, Italy.

Original languageEnglish (US)
Pages (from-to)235-248
Number of pages14
JournalTechnological Forecasting and Social Change
StatePublished - May 2019


  • IoV applications
  • Multi-attribute decision making
  • Simulated annealing
  • Traffic congestion control

ASJC Scopus subject areas

  • Business and International Management
  • Applied Psychology
  • Management of Technology and Innovation


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