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A Traffic Trajectory Recommendation Scheme Based on Edge-Cloud Computing Driver-Car-Road Preferences
Volume 36, Issue 1 (2025), pp. 223–240
Pub. online: 19 February 2025      Type: Research Article      Open accessOpen Access
Received
1 June 2024
Accepted
1 January 2025
Published
19 February 2025

Abstract

Most existing traffic trajectory recommendation methods don’t consider the driver-car-road preferences, resulting in the poor ability to meet the driver-car-road requirements. To address this issue, we propose a traffic Trajectory Recommendation scheme based on Edge-cloud computing Driver-car-road Preferences (named as TREDP). TREDP reduces the computational, storage, and energy burden on the edge through edge-cloud collaborative computing. TREDP enhances the recommended accuracy by considering driver-car-road requirements and the relationship among driver-car-road in different traffic trajectories. Meanwhile, TREDP increases the computational efficiency through edge-cloud computing. Thus, it improves the driver experience of intelligent traffic trajectory recommendation systems.

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Biographies

Yu Jing
yujingellemma@gmail.com

J. Yu received the MSc degree from Huazhong University of Science and Technology in 2012 and the PhD degree from Wonkwang University in 2021. She is currently an associate professor in School of Management, Jiujiang University, China. Her research interests include tensor algebra, privacy-preserving, personalized recommendation, information management, etc.

Huang Jinzhou
huangjinzhou@hbuas.edu.cn

J. Huang received the PhD degree in computer system architecture from Huazhong University of Science and Technology (HUST), in 2015. He is currently an associate professor in the School of Computer Engineering, Hubei University of Arts and Science, China. His research interests include online social networking, intelligent transportation, internet of things, computer networking and information security.

Chi Lianhua
L.chi@latrobe.edu.au

L. Chi received the dual PhD degrees in computer science from the University of Technology Sydney, Australia, and the Huazhong University of Science and Technology, Wuhan, China, in 2015. She was a Post-Doctoral Research Scientist in IBM Research Melbourne. Dr. Chi was a recipient of the Best Paper Award in PAKDD in 2013. Currently, she is a lecturer with the Department of Computer Science and Information Technology at La Trobe University since 2018. Her current research interests include data mining, machine learning and big data hashing.

Wei Wei
weiwei@xaut.edu.cn

W. Wei received his MS and PhD degrees from Xi’an Jiaotong University in 2005 and 2010, respectively. He is currently a professor with School of Computer Science and Engineering, Xi’an University of Technology. His research interests include wireless networks, internet of things, image processing, mobile computing, distributed computing, pervasive computing, and tensor computing.

Cui Zongmin
cuizm01@gmail.com

Z. Cui received the BE degree from Southeast University in 2002. He received the ME degree and PhD degree from Huazhong University of Science and Technology in 2006 and 2014 respectively. He is currently a professor at Jiujiang University. His research interests include tensor computing, privacy-preserving, and data analysis.


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Copyright
© 2025 Vilnius University
Open access article under the CC BY license.

Keywords
traffic trajectory trajectory recommendation driver-car-road preference personalized recommendation edge-cloud computing

Funding
This work was supported in part by the National Nature Science Foundation of China (No. 62362042); in part by the Hubei Natural Science Foundation Innovation and Development Joint Fund Project (Nos. 2022CFD101 and 2022CFD103); in part by the Xiangyang High-tech Key Science and Technology Plan Project (No. 2022ABH006848); in part by the Hubei Superior and Distinctive Discipline Group of “New Energy Vehicle and Smart Transportation”; in part by the Jiangxi Provincial Natural Science Foundation of China (No. 20224BAB202012); in part by the Jiangxi Provincial Social Science Foundation of China (No. 23GL52D); in part by the Scientific and Technological Research Project of Jiangxi Provincial Education Department of China (No. GJJ2401835); and in part by the Open Fund for Chongqing Key Laboratory of Computational Intelligence (No. 2020FF02).

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