Amer, HM, Al-Kashoash, H, Hawes, M et al. (3 more authors) (2019) Centralized simulated annealing for alleviating vehicular congestion in smart cities. Technological Forecasting and Social Change, 142. pp. 235-248. ISSN 0040-1625
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier Inc. All rights reserved. This is an author produced version of a paper published in Technological Forecasting and Social Change. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Traffic congestion control; Simulated annealing; IoV applications; Multi-attribute decision making |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Jan 2019 11:34 |
Last Modified: | 11 Apr 2020 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.techfore.2018.09.013 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140425 |
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