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1. Designing Traffic Management Strategies for a Heterogeneous Traffic Network

   https://doi.org/10.1016/j.ifacol.2022.11.263  

   Shahri, Pouria Karimi; HomChaudhuri, Baisravan; Ghaffari, Azad; Ghasemi, Amir H. (January 2022, IFAC-PapersOnLine)
2. TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks

   https://doi.org/10.1109/ICDM.2019.00193  

   Zhang, Yingxue; Li, Yanhua; Zhou, Xun; Kong, Xiangnan; Luo, Jun (November 2019, 2019 IEEE International Conference on Data Mining (ICDM))
3. MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions

   https://doi.org/10.1145/3627673.3679605  

   Yang, Qinchen; Xie, Zejun; Wei, Hua; Zhang, Desheng; Yang, Yu (October 2024, ACM)
4. Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic

   https://doi.org/10.1145/3491052  

   Bronzino, Francesco; Schmitt, Paul; Ayoubi, Sara; Kim, Hyojoon; Teixeira, Renata; Feamster, Nick (December 2021, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   Network management often relies on machine learning to make predictions about performance and security from network traffic. Often, the representation of the traffic is as important as the choice of the model. The features that the model relies on, and the representation of those features, ultimately determine model accuracy, as well as where and whether the model can be deployed in practice. Thus, the design and evaluation of these models ultimately requires understanding not only model accuracy but also the systems costs associated with deploying the model in an operational network. Towards this goal, this paper develops a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance (e.g., model accuracy) and the systems-level costs of different representations of network traffic. We highlight these two dimensions for two practical network management tasks, video streaming quality inference and malware detection, to demonstrate the importance of exploring different representations to find the appropriate operating point. We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept implementation that both monitors network traffic at 10~Gbps and transforms traffic in real time to produce a variety of feature representations for machine learning. Traffic Refinery both highlights this design space and makes it possible to explore different representations for learning, balancing systems costs related to feature extraction and model training against model accuracy. 

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5. Real-Time Traffic Pattern Collection and Analysis Model for Intelligent Traffic Intersection

   https://doi.org/10.1109/EDGE.2018.00028  

   Sreekumar, Unnikrishnan Kizhakkemadam; Devaraj, Revathy; Li, Qi; Liu, Kaikai (July 2018, 2018 IEEE International Conference on Edge Computing (EDGE))

   The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection. 

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