Optimizing systemic redundancy of traffic sensor networks while maintaining resilience: new evidence from using graph learning

Tang, J, Wei, S, Li, X and Li, D ORCID: https://orcid.org/0000-0003-0142-9290, 2023. Optimizing systemic redundancy of traffic sensor networks while maintaining resilience: new evidence from using graph learning. IEEE Systems Journal. ISSN 1932-8184

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Abstract

The optimization of systemic redundancy by minimizing the sensor quantity can improve the efficiency of sensor networks and save costs. However, from the perspective of risk management, this redundancy reduction can also bring a significant loss in the overall network resilience because the less the systemic redundancy is, the fewer backup components in the network when shocks hit and, therefore, the less overall resilience. In this article, we investigate this intractable dilemma and attempt to pinpoint the tradeoff point for a city-scale automatic number plate recognition (ANPR) system in Cambridge, U.K. By developing a two-stage graph deep learning model, we first optimize the layout of the ANPR system to reduce redundancy and find its efficiency profile. Next, we study what effects this redundancy reduction can bring to the overall resilience, as the overall observability drops with the reduction in the number of sensors and find an optimal balance. The results show that our approach can effectively optimize the system's redundancy by using only 47% of the original sensors to reconstruct the full picture with a mean absolute error of only 11.18 and a root mean square error of 19.49; most importantly, the overall system resilience is maintained at 70% in the meantime. This article provides an alternative perspective for dealing with the well-known “efficiency-resilience” dilemma and offers new evidence to enable better decision and policy making for city managers and planners in local authorities.

Item Type:	Journal article
Publication Title:	IEEE Systems Journal
Creators:	Tang, J., Wei, S., Li, X. and Li, D.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	30 March 2023
ISSN:	1932-8184
Identifiers:	Number Type 10.1109/jsyst.2023.3257886 DOI 1751760 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	21 Apr 2023 07:13
Last Modified:	21 Apr 2023 07:13
URI:	https://irep.ntu.ac.uk/id/eprint/48788

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