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Deep Reinforcement Learning for Enhanced Traffic
Il y a 5 mois
**Deep Reinforcement Learning for Enhanced Traffic Signal Control with Integrated Drone Detection**:
- Réf **ABG-123719**
- Sujet de Thèse- 07/05/2024- Contrat doctoral- Université Gustave Eiffel / IFSTTAR - Site de Marne-la-Vallée- Lieu de travail- champs sur marne - Ile-de-France - France- Intitulé du sujet- Deep Reinforcement Learning for Enhanced Traffic Signal Control with Integrated Drone Detection- Champs scientifiques- Informatique
- Mathématiques
- Science de la donnée (stockage, sécurité, mesure, analyse)
- Mots clés- Machine learning, AI, Operation research, Data-driven, computer science, Transport, informatique, mathématiques appliquées,**Description du sujet**:
Main host Laboratory: COSYS-GRETTIA
Main location: Paris area, France
Doctoral affiliation: UNIVERSITE GUSTAVE EIFFEL
PhD school: MATHEMATIQUES ET SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (MSTIC)
**Background**
**Objectives**
- Develop a DRL algorithm for intelligent traffic signal control, integrating drone detection to improve accuracy.
- Investigate the influence of drone-assisted detection on DRL's efficiency in traffic signal control.
- Create more realistic and comprehensive traffic management scenarios utilizing drone data.
**Methodology**
To accomplish the research objectives, the proposed plan entails the following primary steps: (i) A thorough examination of existing DRL techniques, TSC systems, and drone technology in traffic management; (ii) Designing a DRL-based algorithm, incorporating real-time data from drones, and implementing it using Python and relevant libraries; (iii) Evaluate and validate the newly developed model in terms of accuracy, computational efficiency, and applicability in various traffic scenarios.
- The research will be conducted in the following steps:
- Perform a literature review on existing traffic signal control model, RL techniques, and optimization methods in traffic dynamics modeling.
- Develop a Deep RL model for synthetic networks and extend it to realistic scenarios using drone data sets.
- Investigate multi-agent communication methods for RL-based algorithms for online traffic control.
- Analyze the impact of the network (intersections) topology on the performance of RL controllers.
- Develop a multi-agent RL framework for traffic signal control for multimodal networks, including the incorporation of agents and data collectors (in this case, drones) in the machine learning process.
- Identify and implement the necessary preprocessing steps for extending the framework for multi-modal networks.
- Evaluate the performance of the proposed model in various case studies, comparing its accuracy and computational efficiency with existing classic methods, such as Longest queue first and Max pressure, and RL-based approaches for scalable signal control.
**References**
[1] Wang, T., Cao, J. and Hussain, A., 2021. Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning. Transportation research part C: emerging technologies, 125, p.103046.
[2] Geroliminis, N., Haddad, J., & Ramezani, M. (2012). Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: A model predictive approach. IEEE Transactions on Intelligent Transportation Systems, 14(1), 348-359.
[3] Korecki, M. and Helbing, D., 2021. Analytically guided machine learning for green IT and fluent traffic. Preprint submitted to Transportation Research Part C: Emerging Technologies on, 18.
[4] Abul-Magd, A.Y., 2007. Modeling highway-traffic headway distributions using superstatistics. Physical Review E, 76(5), p.057101.
[5] Li, Z., Yu, H., Zhang, G., Dong, S. and Xu, C.Z., 2021. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning. Transportation Research Part C: Emerging Technologies, 125, p.103059.
[6] Ducrocq, R. and Farhi, N., 2021. Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection. arXiv preprint arXiv:2109.14337.
[7] Alemzadeh S, Moslemi R, Sharma R, Mesbahi M. Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond. arXiv preprint arXiv:2007.10960. 2020 Jul 21.
[8] Long, M., Zou, X., Zhou, Y. and Chung, E., 2022. Deep reinforcement learning for transit signal priority in a connected environment. Transportation Research Part C: Emerging Technologies, 142, p.103814.
**Prise de fonction**:
- 01/09/2024**Nature du financement**:
- Contrat doctoral**Précisions sur le financement**:
**Présentation établissement et labo d'accueil**:
- Université Gustave Eiffel / IFSTTAR - Site de Marne-la-ValléeThe Gustave Eiffel University is a national multi-campus university. It was created on 1 January 2020 as a result of the merger of the University of Paris-Est Marne la vallée, Ifsttar, and several schools and engineering schools: EIVP, ENSG, ESIEE, and Ecole d'architecture Paris Est. Gusta
