Amer, HM, Al-Kashoash, HAA, Kemp, A et al. (2 more authors) (2018) Coalition Game for Emergency Vehicles Re-Routing in Smart Cities. In: 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM). SAM 2018, 08-11 Jul 2018, Sheffield, UK. IEEE , pp. 306-310. ISBN 978-1-5386-4752-3
Abstract
Traffic jam is considered as a difficult problem to deal with in many cities around the world due to the continuously increasing number of vehicles compared to the available infrastructure. Traffic congestion significantly influences drivers travel journey, fuel consumption and air pollution. However, the most important factor has affected the delay of emergency vehicles, such as ambulances and police cars, leading to increased road deaths and significant financial losses. To reduce this problem, we propose an advanced traffic control allows rapid emergency services response in smart cities. This can be achieved through a traffic management system capable of implementing path planning in road network monitoring and driving the emergency vehicle in the best possible way to reach the hazard zone. The performance of the proposed algorithm is compared with two other algorithms over Birmingham city centre test scenarios. Simulation results show that the proposed approach improves traffic efficiency of emergency vehicles by an overall average of 21.78%, 29.32%, 32.79% and 46.77% in terms of travel time, fuel consumption, CO 2 emission and average speed, respectively.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Traffic congestion control; Cooperative game theory; Particle swarm optimization; IoV applications; Vehicular Ad hoc Networks |
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: | 06 Feb 2019 11:07 |
Last Modified: | 06 Feb 2019 16:21 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SAM.2018.8448582 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142178 |