Title: Evaluation of digital twin resource allocation and multimodal information learning in internet of vehicles

Authors: Ke Wang; Zunhai Gao

Addresses: School of Business, Chongqing Vocational College of Transportation, 402247, Chongqing, China ' School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang 330108, Jiangxi, China

Abstract: The construction of modern intelligent transportation infrastructure has brought many inconveniences to transportation due to its large number of vehicles, high traffic density. This article applies digital twins to intelligent transportation devices in the internet of vehicles, and studies and analyses the composition of intelligent transportation IoT systems and the application of digital twins in intelligent transportation. First, the intelligent transportation equipment of different vehicles was tested, and then the digital twin intelligent transportation equipment was used to configure resources. The results showed that the average satisfaction score of the intelligent transportation equipment improved by digital twin technology was 7.73 points, while the average satisfaction score of traditional intelligent transportation equipment was 8.26 points, an increase of about 6.9%. Research has shown that intelligent transportation devices based on digital twin vehicle networking can allocate resources more reasonably, ensure the safety of road vehicles, and avoid traffic accidents.

Keywords: car networking; digital twins; intelligent transportation; internet of things; IoT; internet of vehicles; IoV.

DOI: 10.1504/IJIIDS.2025.147424

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.3/4, pp.498 - 515

Received: 13 Mar 2024
Accepted: 05 Jul 2024
Published online: 15 Jul 2025 *

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