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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. Potential of carpool for network traffic management

   https://doi.org/10.1016/j.ijtst.2021.04.006  

   Nie, Yu; Li, Ruijie (June 2022, International Journal of Transportation Science and Technology)
3. Mixed-Traffic Intersection Management Utilizing Connected and Autonomous Vehicles as Traffic Regulators

   https://doi.org/10.1145/3566097.3567849  

   Chen, Pin-Chun; Liu, Xiangguo; Lin, Chung-Wei; Huang, Chao; Zhu, Qi (January 2023, ACM)
4. Safety in Traffic Management Systems: A Comprehensive Survey

   https://doi.org/10.3390/designs7040100  

   Du, Wenlu; Dash, Ankan; Li, Jing; Wei, Hua; Wang, Guiling (August 2023, Designs)

   Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions. 

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5. Multi-Agent-Based Traffic Prediction and Traffic Classification for Autonomic Network Management Systems for Future Networks

   https://doi.org/10.3390/fi14080230  

   Arzo, Sisay Tadesse; Akhavan, Zeinab; Esmaeili, Mona; Devetsikiotis, Michael; Granelli, Fabrizio (August 2022, Future Internet)

   Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best. 

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