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A Traffic Trajectory Recommendation Scheme Based on Edge-Cloud Computing Driver-Car-Road Preferences
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.

References

 
bin Zulkiflee, I., Haw, S.-C., Palanichamy, N., Yeoh, E.-T., Tong, Y.-C. (2024). Customer relationship management dashboard with descriptive analytics for effective recommendation. Journal of Logistics, Informatics and Service Science, 11(2), 301–323.
 
Cao, J., Tao, C., Zhang, Z., Gao, Z., Luo, X., Zheng, S., Zhu, Y. (2024). Accelerating point-voxel representation of 3D object detection for automatic driving. IEEE Transactions on Artificial Intelligence, 5(1), 254–266.
 
Cheng, Y., Hu, X., Chen, K., Yu, X., Luo, Y. (2023). Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach. Journal of Intelligent Transportation Systems, 27(3), 396–410.
 
Deng, J., Li, T., Yang, Z., Yuan, Q., Chen, X. (2025). Heterogeneity in route choice during peak hours: implications on travel demand management. Travel Behaviour and Society, 38, 100922.
 
Dudakli, N., Baykasoğlu, A. (2024). Performance evaluation of different dispatching rules and heuristics in a fully automated parking system. Informatica, 35(1), 99–129.
 
Guo, S., Liao, X., Meng, F., Zhao, Q., Tang, Y., Li, H., Zong, Q. (2024). FSASA: sequential recommendation based on fusing session-aware models and self-attention networks. Computer Science and Information Systems, 21(1), 1–20.
 
Jaurker, D., Pradhan, M.K. (2023). Finite element modelling for electrical discharge machining of Ti-6Al-4V alloy and multi-objective optimisation using response surface modelling. International Journal of Simulation and Process Modelling, 20(1), 21–30.
 
Jian, L., Jie, L., Hongji, F., Jiang, J. (2024). Dynamic energy-efficient path planning for electric vehicles using an enhanced ant colony algorithm. Technical Gazette/Tehnički Vjesnik, 31(2), 434–441.
 
Jiang, Y., Man, Z., Wang, Y., Yao, Z. (2024). Cooperative lane-changing for connected autonomous vehicles merging into dedicated lanes in mixed traffic flow. Expert Systems with Applications, 252, 124163.
 
Jin, X., Choi, Y.-S. (2024). Coupling evaluation and spatial analysis of regional economy and logistics development based on digital twin. Journal of Logistics, Informatics and Service Science, 11(2), 10–26.
 
Kalaivani Jayaraman, M.D., Malarvizhi, C.A.N. (2024). Survey validation for the impact of environmental management systems on green innovation. Journal of System and Management Sciences, 14(5), 212–230.
 
Kurnianto, D.D., Sfenrianto (2024). Classification and recommendations for insurance products using Naïve Bayes classification based on customer data. Journal of System and Management Sciences, 14(6), 300–315.
 
Lai, D., Qu, J., Sang, Y., Chen, X. (2024). Transfer-learning-based representation learning for trajectory similarity search. GeoInformatica, 28, 631–648.
 
Lei, D., Xu, M., Wang, S. (2025). A deep multimodal network for multi-task trajectory prediction. Information Fusion, 113, 102597.
 
Li, J., Yu, C., Shen, Z., Su, Z., Ma, W. (2023). A survey on urban traffic control under mixed traffic environment with connected automated vehicles. Transportation Research Part C: Emerging Technologies, 154, 104258.
 
Li, J., Zhang, H., Liu, H., Wang, S. (2024). Multi-objective planning of commuter carpooling under time-varying road network. Sustainability, 16(2), 647.
 
Lim, W., Lee, S., Sunwoo, M., Jo, K. (2021). Hybrid trajectory planning for autonomous driving in on-road dynamic scenarios. IEEE Transactions on Intelligent Transportation Systems, 22(1), 341–355.
 
Liu, M., Liu, G., Sun, L. (2023). Spatial-temporal dependence and similarity aware traffic flow forecasting. Information Sciences, 625, 81–96.
 
Liu, M., Yazdanpanah, V., Stein, S., Gerding, E. (2024). Sustainability-oriented route generation for ridesharing services. Computer Science and Information Systems, 21(1), 309–333.
 
Ning, X., Tian, H., Lin, Y., Yao, X., Hu, F., Yin, Y. (2024). Research on multi-objective optimization models for intersection crossing of connected autonomous vehicles with traffic signals. IEEE Access, 12, 36825–36840.
 
Nishida, R., Tanigaki, Y., Onishi, M., Hashimoto, K. (2024). Multi-objective deep reinforcement learning for crowd route guidance optimization. Transportation Research Record, 2678(5), 617–633.
 
Peng, C., Wu, Z., Huang, X., Wu, Y., Kang, J., Huang, Q., Xie, S. (2024). Joint energy and completion time difference minimization for UAV-enabled intelligent transportation systems: a constrained multi-objective optimization approach. IEEE Transactions on Intelligent Transportation Systems, 25(10), 14040–14053.
 
Qu, B., Yang, W., Cui, G., Wang, X. (2019). Profitable taxi travel route recommendation based on big taxi trajectory data. IEEE Transactions on Intelligent Transportation Systems, 21(2), 653–668.
 
Wang, Y., Li, X., Yao, H. (2019). Review of trajectory optimisation for connected automated vehicles. IET Intelligent Transport Systems, 13(4), 580–586.
 
Xiao, M., Chen, L., Feng, H., Peng, Z., Long, Q. (2024). Sustainable and robust route planning scheme for smart city public transport based on multi-objective optimization: digital twin model. Sustainable Energy Technologies and Assessments, 65, 103787.
 
Xing, Y., Hao, Y. (2022). Research on travel time prediction of expressway in peak period based on Greenberg model. International Journal of Simulation and Process Modelling, 19(1–2), 54–61.
 
Yang, X.T., Huang, K., Zhang, Z., Zhang, Z.A., Lin, F. (2021). Eco-driving system for connected automated vehicles: multi-objective trajectory optimization. IEEE Transactions on Intelligent Transportation Systems, 22(12), 7837–7849.
 
Yao, Z., Liu, M., Jiang, Y., Tang, Y., Ran, B. (2024). Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway. IET Intelligent Transport Systems, 18(3), 450–466.
 
Yu, X., Zhu, S., Ren, Y. (2023). Continuous trajectory similarity search with result diversification. Future Generation Computer Systems, 143, 392–400.
 
Zhang, J., Chang, C., He, Z., Zhong, W., Yao, D., Li, S., Li, L. (2023). CAVSim: a microscopic traffic simulator for evaluation of connected and automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 24(9), 10038–10054.
 
Zhao, C., Yu, H., Molnar, T.G. (2023). Safety-critical traffic control by connected automated vehicles. Transportation Research Part C: Emerging Technologies, 154, 104230.
 
Zhao, Y., Ma, C., Zhao, M., Xu, X., Du, B. (2025). An optimal multi-objective dynamic traffic guidance approach based on dynamic traffic assignment. Physica A: Statistical Mechanics and Its Applications, 657, 130257.

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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