# Traffic-Aware Optimization of Task Offloading and Content
[PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS] (Z.H.) [PERSON]  
1 School of Computer Science and Technology, Donghua University, Shanghai 201620, China; [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (J.Q. ); [EMAIL_ADDRESS]. networks continue to evolve, they hold the promise of not only improving the efficiency of transportation systems but also reducing congestion, emissions, and accidents, ultimately enhancing the quality of life in urban areas. For time-sensitive tasks, meeting deadlines is critical. Vehicular units often have limited computing and storage capacity, leading to latency or incomplete task execution when processed locally. Mobile Edge Computing (MEC) addresses this by integrating resources and intelligence at the network's edge. MEC brings cloud-like capabilities closer to data sources, allowing vehicles to offload tasks to edge servers. This enhances the driving experience and supports complex computations. AI and ML models on edge servers enable applications like predictive maintenance and intelligent traffic management. MEC reduces data transmission latency, ensuring real-time safety alerts and traffic information. It creates a responsive vehicular network, adapting to urban traffic dynamics. As vehicles become smarter and more connected, MEC with AI-driven edge computing promises safer and efficient urban mobility. In this rapidly evolving landscape, traffic stream forecasting plays a pivotal role in enhancing the efficiency of task offloading and content caching. It offers invaluable insights into the distribution of task volumes across different regions, aiding in more effective resource allocation [2]. As vehicular terminals experience a continuous surge in computational demands, the development of efficient computational offloading strategies becomes increasingly crucial. This is precisely the focus of our paper, where we delve into the evolution of task-level offloading strategies [3]. By integrating cutting-edge trends in AI- and machine-learning-driven edge/fog computing technologies, we propose an innovative optimization approach known as TOCC. Based on traffic flow prediction, task offloading and content caching are jointly optimized in this method, which can effectively meet the growing demand of intelligent applications.