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ENERGY-AWARE NAVIGATION IN LARGE-SCALE EVACUATION USING G-NETWORKS

Published online by Cambridge University Press:  18 May 2016

Huibo Bi  and
Omer H. Abdelrahman
Huibo Bi
Affiliation:
Intelligent Systems and Networks Group, Dept. of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK E-mail: [email protected], [email protected]
Omer H. Abdelrahman
Affiliation:
Intelligent Systems and Networks Group, Dept. of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK E-mail: [email protected], [email protected]
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Abstract

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Previous studies on emergency management of large-scale urban networks have commonly concentrated on system development to off-load intensive computations to remote cloud servers or improving communication quality during a disaster and ignored the effect of energy consumption of vehicles, which can play a vital role in large-scale evacuation owing to the disruptions in energy supply. Hence, in this paper we propose a cloud-enabled navigation system to direct vehicles to safe areas in the aftermath of a disaster in an energy and time efficient fashion. A G-network model is employed to mimic the behaviors and interactions between individual vehicles and the navigation system, and analyze the effect of re-routing decisions toward the vehicles. A gradient descent optimization algorithm is used to gradually reduce the evacuation time and fuel consumption of vehicles by optimizing the probabilistic choices of linked road segments at each intersection. The re-routing decisions arrive at the intersections periodically and will expire after a short period. When a vehicle reaches an intersection, if the latest re-routing decision has not expired, the vehicle will follow this advice, otherwise, the vehicle will stick to the shortest path to its destination. The experimental results indicate that the proposed algorithm can reduce the evacuation time and the overall fuel utilization especially when the number of evacuated vehicles is large.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 32 , Issue 3 , July 2018 , pp. 340 - 352
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2016

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