Multi-agent reinforcement learning framework based on information fusion biometric ticketing data in different public transport modes

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255109" target="_blank" >RIV/61989100:27240/24:10255109 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1566253524002495" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1566253524002495</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.inffus.2024.102471" target="_blank" >10.1016/j.inffus.2024.102471</a>

Result language

angličtina

Original language name

Multi-agent reinforcement learning framework based on information fusion biometric ticketing data in different public transport modes

Original language description

In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology-based mobile ticket application-based system. We formulate the problem as a multi-agent reinforcement learning framework for biometric ticketing in multi-transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger&apos;s mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi-agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems. (C) 2024 The Author(s)

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Information Fusion

ISSN

1566-2535

e-ISSN

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Volume of the periodical

110

Issue of the periodical within the volume

Neuveden

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

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UT code for WoS article

001264184600001

EID of the result in the Scopus database

2-s2.0-85193721347