Awesome Reinforcement Learning for Traffic Signal Control

This is a collection of research papers for Traffic Signal Control with RL. And the repository will be continuously updated to track the frontier of RL-based traffic signal control.

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Contributors

There are contributors to this paper collection, we will continue to update it.

Cite

If you find LibSignal useful for your research or development, please cite our paper. Mei, H., Lei, X., Da, L. et al. Libsignal: an open library for traffic signal control. Mach Learn (2023). https://doi.org/10.1007/s10994-023-06412-y

@article{mei2023libsignal,
  title={Libsignal: an open library for traffic signal control},
  author={Mei, Hao and Lei, Xiaoliang and Da, Longchao and Shi, Bin and Wei, Hua},
  journal={Machine Learning},
  pages={1--37},
  year={2023},
  publisher={Springer}
}

Reinforcement Learning based Traffic Signal Control Paper Collection

In the paper collection, we collected RL-based traffic signal control papers published in the recent years (2016-2021) on 8 top conferences and journals, namely, NeurIPS, AAAI, AAMAS, KDD, CIKM, IEEE TITS, ITSC, TR-B and their workshop papers. In addition, the surveys since 2018 and representative papers mentioned in the surveys are also included. We will continue to update the collection.

• Traffic Signal Control Paper Collection
  • Surveys
  • NeurIPS
  • AAAI
  • AAMAS
  • KDD
  • CIKM
  • IEEE TITS
  • ITSC
  • TR-B
  • Workshops
  • Others
  • Other Related Topics
• Contributors
• Cite

Surveys and Simulators

1. Graph Neural Networks for Intelligent Transportation Systems: A Survey. S Rahmani, A Baghbani, et, al. IEEE Transactions on Intelligent Transportation Systems. 2023. link

2. Deep Reinforcement Learning Based Traffic Signal Control: A Comparative Analysis. C Wu, I Kim, et, al. 2023. link

3. Reinforcement learning in urban network traffic signal control: A systematic literature review. M Noaeen, A Naik, L Goodman, J Crebo, et, al.. 2022. link

4. The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality. R Chen, F Fang, N Sadeh. 2022. link

5. A review of reinforcement learning applications in adaptive traffic signal control. M Miletić, E Ivanjko, et, al. IET Intelligent Transportation System. 2022. link

6. Traffic Signal Control Methods: Current Status, Challenges, and Emerging Trends. I Tomar, S Indu, N Pandey. Data Analytics and Management. 2022. link

7. Reinforcement learning in urban network traffic signal control: A systematic literature review. M Noaeen, A Naik, L Goodman, J Crebo, T Abrar. Expert Systems with Applications. 2022. link

8. A Comparison of Deep Reinforcement Learning Models for Isolated Traffic Signal Control. F Mao, Z Li, L Li. IEEE Intelligent Transportation Systems Magazine. 2022. link

9. A Comparative Study of Algorithms for Intelligent Traffic Signal Control. H Chaudhuri, V Masti, V Veerendranath. Machine Learning and Autonomous Systems. 2022. link

10. Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. H Wei, G Zheng, V Gayah, Z Li. ACM SIGKDD Explorations Newsletter. 2021. link

11. A Survey on Deep Reinforcement Learning for Traffic Signal Control. W Miao, L Li, Z Wang. Chinese Control and Decision Conference. 2021. link

12. Comparative Evaluation of Road Traffic Simulators based on Modeler's Specifications: An Application to Intermodal Mobility Behaviors.. AO Diallo, G Lozenguez, A Doniec, R Mandiau. International Conference on Agents and Artificial Intelligence . 2021. link

13. Deep reinforcement learning in transportation research: A review. NP Farazi, B Zou, T Ahamed, L Barua. Transportation Research Interdisciplinary Perspectives. 2021. link

14. Reinforcement learning for traffic signal control: comparison with commercial systems. A Cabrejas-Egea, R Zhang, N Walton. Transportation research procedia. 2021. link

15. Decision-making at unsignalized intersection for autonomous vehicles: Left-turn maneuver with deep reinforcement learning. T Liu, X Mu, B Huang, X Tang, F Zhao, X Wang. arXiv preprint arXiv .... 2020. link

16. Deep reinforcement learning and transportation research: A comprehensive review. NP Farazi, T Ahamed, L Barua, B Zou. arXiv preprint arXiv:2010.06187. 2020. link

17. State-of-art review of traffic signal control methods: challenges and opportunities. SSSM Qadri, MA Gökçe, E Öner. European transport research review. 2020. link

18. Deep reinforcement learning for traffic signal control: A review. F Rasheed, KLA Yau, RM Noor, C Wu, YC Low. IEEE Access. 2020. link

19. A review on swarm intelligence and evolutionary algorithms for solving the traffic signal control problem. PW Shaikh, M El-Abd, M Khanafer. IEEE transactions on .... 2020. link

20. QarSUMO: A Parallel, Congestion-optimized Traffic Simulator. H Chen, K Yang, SG Rizzo, G Vantini, P Taylor. International Conference on Advances in Geographic Information Systems. 2020. link

21. Assessment of reward functions for reinforcement learning traffic signal control under real-world limitations. AC Egea, S Howell, M Knutins. International Conference on Systems, Man, and Cybernetics. 2020. link

22. A review on swarm intelligence and evolutionary algorithms for solving the traffic signal control problem. PW Shaikh, M El-Abd, M Khanafer. TITS. 2020. link

23. Deep reinforcement learning for intelligent transportation systems: A survey. A Haydari, Y Yilmaz. TITS. 2020. link

24. A survey on traffic signal control methods. H Wei, G Zheng, V Gayah, Z Li. arXiv preprint. 2019. link

25. Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. H Zhang, S Feng, C Liu, Y Ding, Y Zhu, Z Zhou. TheWebConf. 2019. link

26. A survey on reinforcement learning models and algorithms for traffic signal control. KLA Yau, J Qadir, HL Khoo, MH Ling. ACM Computing Surveys. 2017. link

27. Comprehensive analysis of reinforcement learning methods and parameters for adaptive traffic signal control. S El-Tantawy, B Abdulhai. TRB. 2011. link

NeurIPS

1. Attendlight: Universal attention-based reinforcement learning model for traffic signal control. A Oroojlooy, M Nazari. NeurIPS. 2020. link

2. Learning individually inferred communication for multi-agent cooperation. Z Ding, T Huang, Z Lu. NeurIPS. 2020. link

KDD

1. Mitigating Action Hysteresis in Traffic Signal Control with Traffic Predictive Reinforcement Learning. X Han, X Zhao, L Zhang, W Wang. KDD. 2023. link
2. Optimizing Traffic Control with Model-Based Learning: A Pessimistic Approach to Data-Efficient Policy Inference. X Han, X Zhao, L Zhang, W Wang. KDD. 2023. link
3. CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation. C Liang, Z Huang, Y Liu, et, al. 2023. link
4. TransformerLight: A Novel Sequence Modeling Based Traffic Signaling Mechanism via Gated Transformer. Q Wu, M Li, J Shen, et, al. KDD. 2023. link
5. Modeling Network-level Traffic Flow Transitions on Sparse Data. X Lei, H Mei, B Shi, H Wei. KDD. 2022. link
6. Presslight: Learning max pressure control to coordinate traffic signals in arterial network. H Wei, C Chen, G Zheng, K Wu, V Gayah. KDD. 2019. link
7. Time critic policy gradient methods for traffic signal control in complex and congested scenarios. SG Rizzo, G Vantini, S Chawla. KDD. 2019. link
8. Intellilight: A reinforcement learning approach for intelligent traffic light control. H Wei, G Zheng, H Yao, Z Li. KDD. 2018. link

AAAI

1. Safelight: A reinforcement learning method toward collision-free traffic signal control. Liang E, Su Z, Fang C, et al. AAAI. 2022. link

2. OAM: An Option-Action Reinforcement Learning Framework for Universal Multi-Intersection Control. Liang E, Su Z, Fang C, et al. AAAI. 2022. link

3. Hierarchically and cooperatively learning traffic signal control. B Xu, Y Wang, Z Wang, H Jia, Z Lu. AAAI. 2021. link

4. Metalight: Value-based meta-reinforcement learning for traffic signal control. X Zang, H Yao, G Zheng, N Xu, K Xu, Z Li. AAAI. 2020. link

5. Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control. C Chen, H Wei, N Xu, G Zheng, M Yang. AAAI. 2020. link

AAMAS

1. Feudal multi-agent deep reinforcement learning for traffic signal control. J Ma, F Wu. AAMAS. 2020. link

2. Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation. H Meess, J Gerner, D Hein, S Schmidtner. AAMAS. 2022. link

CIKM

1. Cooperative max-pressure enhanced traffic signal control. Li L, Li R, Peng Y, et al. CIKM. 2022. link

2. Meta-Reinforcement Learning for Multiple Traffic Signals Control. Lou Y, Wu J, Ran Y. CIKM. 2022. link

3. DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control. L Wu, M Wang, D Wu, J Wu. CIKM. 2021. link

4. Generalight: Improving environment generalization of traffic signal control via meta reinforcement learning. H Zhang, C Liu, W Zhang, G Zheng, Y Yu. CIKM. 2020. link

5. Colight: Learning network-level cooperation for traffic signal control. H Wei, N Xu, H Zhang, G Zheng, X Zang. CIKM. 2019. link

6. Learning phase competition for traffic signal control. G Zheng, Y Xiong, X Zang, J Feng, H Wei. CIKM. 2019. link

7. Learning traffic signal control from demonstrations. Y Xiong, G Zheng, K Xu, Z Li. CIKM. 2019. link

IEEE TITS

1. CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles Using Deep Reinforcement Learning. Guo J, Cheng L, Wang S. TITS. 2023. link

2. DeepGAL: Intelligent Vehicle Control for Traffic Congestion Alleviation at Intersections. Cao M, Li V O K, Shuai Q. TITS. 2023. link

3. Real-Time Cooperative Vehicle Coordination at Unsignalized Road Intersections. Luo J, Zhang T, Hao R, et al. TITS. 2023. link

4. A gain with no pain: Exploring intelligent traffic signal control for emergency vehicles. Cao M, Li V O K, Shuai Q. TITS. 2023. link

5. Network-wide traffic signal control using bilinear system modeling and adaptive optimization. Wang H, Zhu M, Hong W, et al. TITS. 2022. link

6. Mastering arterial traffic signal control with multi-agent attention-based soft actor-critic model. Mao F, Li Z, Lin Y, et al. TITS. 2022. link

7. Distributed Signal Control of Arterial Corridors Using Multi-Agent Deep Reinforcement Learning. Zhang W, Yan C, Li X, et al. TITS. 2022. link

8. Network-Level Traffic Signal Cooperation: A Higher-Order Conflict Graph Approach. Li W, Wang B, Khattak Z H, et al. TITS. 2022. link

9. PV-TSC: Learning to control traffic signals for pedestrian and vehicle traffic in 6G era. Xu K, Huang J, Kong L, et al. TITS. 2022. link

10. A Gain With No Pain: Exploring Intelligent Traffic Signal Control for Emergency Vehicles. M Cao, VOK Li, Q Shuai. TITS. 2022. link

11. PV-TSC: Learning to Control Traffic Signals for Pedestrian and Vehicle Traffic in 6G Era. K Xu, J Huang, L Kong, J Yu. TITS. 2022. link

12. Data augmented deep behavioral cloning for urban traffic control operations under a parallel learning framework. X Li, P Ye, J Jin, F Zhu, FY Wang. TITS. 2021. link

13. IG-RL: Inductive graph reinforcement learning for massive-scale traffic signal control. FX Devailly, D Larocque. TITS. 2021. link

14. Traffic signal control with reinforcement learning based on region-aware cooperative strategy. M Wang, L Wu, J Li, L He. TITS. 2021. link

15. Fuzzy inference enabled deep reinforcement learning-based traffic light control for intelligent transportation system. N Kumar, SS Rahman, N Dhakad. TITS. 2020. link

16. Optimizing signal timing control for large urban traffic networks using an adaptive linear quadratic regulator control strategy. H Wang, M Zhu, W Hong, C Wang. TITS. 2020. link

17. Multi-agent deep reinforcement learning for large-scale traffic signal control. T Chu, J Wang, L Codecà , Z Li. TITS. 2019. link

18. Meta-heuristics for bi-objective urban traffic light scheduling problems. K Gao, Y Zhang, Y Zhang, R Su. TITS. 2018. link

19. Reinforcement learning with function approximation for traffic signal control. LA Prashanth, S Bhatnagar. TITS. 2010. link

ITSC

1. QueueLearner: A Knowledge-Combined Reinforcement Learning to Understand Queuing Evolution in Isolated Traffic Signal Control. Han T, Oguchi T, Lyu S. ITSC. 2022. link

2. A Hierarchical Spatio-Temporal Cooperative Reinforcement Learning Approach for Traffic Signal Control. Li M, Hu Z, Huang H, et al. ITSC. 2022. link

3. A Spatial-Temporal Deep Reinforcement Learning Model for Large-Scale Centralized Traffic Signal Control. Yi C, Wu J, Ren Y, et al. ITSC. 2022. link

4. A Deep Reinforcement Learning Framework with Memory Network to Coordinate Traffic Signal Control. Kong A Y, Lu B X, Yang C Z, et al. ITSC. 2022. link

5. Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking. Müller A, Sabatelli M. ITSC. 2022. link

6. Meta-Reinforcement Learning for Centralized Multiple Intersections Traffic Signal Control. Ren Y, Wu J, Yi C, et al. ITSC. 2022. link

7. A deep reinforcement learning approach for fair traffic signal control. M Raeis, A Leon-Garcia. ITSC. 2021. link

8. An ontology-based intelligent traffic signal control model. S Ghanadbashi, F Golpayegani. ITSC. 2021. link

9. Designing Reinforcement Learning Agents for Traffic Signal Control with the Right Goals: a Time-Loss based Approach. M D'Almeida, A Paes, D Mossé. ITSC. 2021. link

10. A deep on-policy learning agent for traffic signal control of multiple intersections. CC Yen, D Ghosal, M Zhang. ITSC. 2020. link

11. A reinforcement learning approach for intelligent traffic signal control at urban intersections. M Guo, P Wang, CY Chan. ITSC. 2019. link

12. Reinforcement learning with explainability for traffic signal control. SG Rizzo, G Vantini, S Chawla. ITSC. 2019. link

13. Traffic Signal Control for Isolated Intersection Based on Coordination Game and Pareto Efficiency. Y Zhao, Y Liang, J Hu, Z Zhang. ITSC. 2019. link

14. Training reinforcement learning agent for traffic signal control under different traffic conditions. J Zeng, J Hu, Y Zhang. ITSC. 2019. link

15. Deep learning vs. discrete reinforcement learning for adaptive traffic signal control. SMA Shabestary, B Abdulhai. ITSC. 2018. link

16. Traffic signal control based on reinforcement learning with graph convolutional neural nets. T Nishi, K Otaki, K Hayakawa. ITSC. 2018. link

TR-C

1. Traffic behavior recognition from traffic videos under occlusion condition: a Kalman filter approach. Jiao J, Wang H. TR-C. 2022. link

2. Intelligent vehicle pedestrian light (IVPL): A deep reinforcement learning approach for traffic signal control. Yazdani M, Sarvi M, Bagloee S A, et al. TR-C. 2022. link

3. Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning. T Wang, J Cao, A Hussain. TR-C. 2021. link

4. DRL-TP3: A learning and control framework for signalized intersections with mixed connected automated traffic. Y Guo, J Ma. TR-C. 2021. link

5. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning. Z Li, H Yu, G Zhang, S Dong, CZ Xu. TR-C. 2021. link

6. An equitable traffic signal control scheme at isolated signalized intersections using Connected Vehicle technology. XJ Liang, SI Guler, VV Gayah. TR-C. 2020. link

7. Reinforcement learning with average cost for adaptive control of traffic lights at intersections. LA Prashanth, S Bhatnagar. ITSC. 2011. link

Workshops

1. An efficient deep reinforcement learning model for urban traffic control. Y Lin, X Dai, L Li, FY Wang. ICDM Workshop. 2018. link

2. Deep reinforcement learning for traffic light optimization. M CoÅŸkun, A Baggag, S Chawla. ICDM Workshop. 2018. link

3. A reinforcement learning approach with Fourier basis linear function approximation for traffic signal control. T Ziemke, LN Alegre, ALC Bazzan. ECAI Workshop. 2020. link

4. Coordinated deep reinforcement learners for traffic light control. E Van der Pol, FA Oliehoek. NeurIPS Workshop. 2016. link

5. Towards Explainable Deep Reinforcement Learning for Traffic Signal Control. LV Schreiber, GO Ramos, ALC Bazzan. ICML Workshop. 2021. link

Others

1. Multi-agent reinforcement learning for traffic signal control: A cooperative approach. M Kolat, B Kővári, T Bécsi. Sustainability, 2023. link

2. Distributed agent-based deep reinforcement learning for large scale traffic signal control. Q Wu, J Wu, J Shen, et, al. Knowledge-Based Systems. 2022. link

3. Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network. H Joo, Y Lim. Applied Sciences. 2022. link

4. Model Predictive Traffic Control by Bi-Level Optimization. K Stoilova, T Stoilov. Applied Sciences. 2022. link

5. A Concurrent Switching Model for Traffic Congestion Control. H Rastgoftar, X Liu, JB Jeannin. arXiv preprint. 2022. link

6. CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning. J Guo, L Cheng, S Wang. arXiv preprint. 2022. link

7. Offline Reinforcement Learning for Road Traffic Control. M Kunjir, S Chawla. arXiv preprint. 2022. link

8. Proximal Policy Optimization Learning based Control of Congested Freeway Traffic. S Mo, J Qi, A Pan. arXiv preprint. 2022. link

9. Cooperative Traffic Signal Control Through A Counterfactual Multi-Agent Deep Actor Critic Approach. X Song, B Zhou, D Ma. Available at SSRN 40219. 2022. link

10. Dynamic Traffic Light Control with Reinforcement Learning Based on Gnn Prediction. C Zhao, G Wang. Available at SSRN 40405. 2022. link

11. Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control. F Rasheed, KLA Yau, RM Noor. Computers, Materials and Continua. 2022. link

12. Exploring reward efficacy in traffic management using deep reinforcement learning in intelligent transportation system. A Paul, S Mitra. ETRI Journal. 2022. link

13. Learning from Oracle Demonstrations–A new approach to develop Autonomous Intersection Management control algorithms based on Multi-Agent Deep .... A Guillen-Perez, MD Cano. IEEE Access. 2022. link

14. Intelligent Traffic Light Control by Exploring Strategies in an Optimised Space of Deep Q-Learning. J Liu, S Qin, Y Luo, Y Wang. IEEE Transactions on Vehicular Technology. 2022. link

15. Micro Junction Agent: A Scalable Multi-agent Reinforcement Learning Method for Traffic Control. BK Choi, JSB Choe, J Kim. International Conference on Agents and Artificial Intelligence. 2022. link

16. Dynamic Weight-based Multi-Objective Reward Architecture for Adaptive Traffic Signal Control System. ARM Jamil, N Nower. International Journal of Intelligent Transportation Systems Research. 2022. link

17. Centralized and Decentralized Signal Control with Short-Term Origin-Destination Demand for Network Traffic. C Zhang, TZ Qiu, A Kim. Journal of Advanced Transportation. 2022. link

18. IPDALight: Intensity-and phase duration-aware traffic signal control based on Reinforcement Learning. W Zhao, Y Ye, J Ding, T Wang, T Wei. Journal of Systems Architecture. 2022. link

19. Distributed agent-based deep reinforcement learning for large scale traffic signal control. Q Wu, J Wu, J Shen, B Du, A Telikani. Knowledge-Based Systems. 2022. link

20. A fog-based Traffic Light Management Strategy (TLMS) based on fuzzy inference engine. SA Gamel, AI Saleh, HA Ali. Neural Computing and Applications. 2022. link

21. Information upwards, recommendation downwards: reinforcement learning with hierarchy for traffic signal control. TO Antes, ALC Bazzan, AR Tavares. Procedia Computer Science. 2022. link

22. Biased pressure: cyclic reinforcement learning model for intelligent traffic signal control. B Ibrokhimov, YJ Kim, S Kang. Sensors. 2022. link

23. SecureTLC: Secure and Two-level Traffic Signal Control for Intelligent Transportation System. CC Yen. UC Davis Electronic Theses and Dissertations. 2022. link

24. Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles, Qu, Ao, Yihong Tang, and Wei Ma. arXiv preprint arXiv:2111.02845. 2021. link

25. Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning. J Gu, M Lee, C Jun, Y Han, Y Kim, J Kim. Applied Sciences. 2021. link

26. A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles. H Su, YD Zhong, D Biswadip, A Chakraborty. arXiv preprint. 2021. link

27. A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers. GS Varela, PP Santos, A Sardinha, FS Melo. arXiv preprint. 2021. link

28. Back to Basics: Deep Reinforcement Learning in Traffic Signal Control. S Kanis, L Samson, D Bloembergen. arXiv preprint. 2021. link

29. DQN Control Solution for KDD Cup 2021 City Brain Challenge. Y Chen, K Chen, K Chen, L Wang. arXiv preprint. 2021. link

30. EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles. H Su, YD Zhong, B Dey, A Chakraborty. arXiv preprint. 2021. link

31. Expression is enough: Improving traffic signal control with advanced traffic state representation. L Zhang, Q Wu, J Shen, L Lü, J Wu, B Du. arXiv preprint. 2021. link

32. Variationally and intrinsically motivated reinforcement learning for decentralized traffic signal control. L Zhu, P Peng, Z Lu, X Wang, Y Tian. arXiv preprint. 2021. link

33. A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems. VV Myasnikov, AA Agafonov, AS Yumaganov. Computer Optics. 2021. link

34. Traffic Signal Control: a Double Q-learning Approach. A Agafonov, V Myasnikov. Conference on Computer Science and Intelligence Systems. 2021. link

35. Multi-agent Deep Reinforcement Learning with Spatio-Temporal Feature Fusion for Traffic Signal Control. X Du, J Wang, S Chen, Z Liu. ECML-PKDD. 2021. link

36. Traffic Signal Control System Based on Intelligent Transportation System and Reinforcement Learning. J Hurtado-Gómez, JD Romo, R Salazar-Cabrera. Electronics. 2021. link

37. Learning scalable multi-agent coordination by spatial differentiation for traffic signal control. J Liu, H Zhang, Z Fu, Y Wang. Engineering Applications of Artificial Intelligence. 2021. link

38. Intelligent Traffic Signal Control System using Deep Q-network. H Joo, Y Lim. Eurasia Conference on IOT, Communication and Engineering. 2021. link

39. Hierarchical traffic signal optimization using reinforcement learning and traffic prediction with long-short term memory. M Abdoos, ALC Bazzan. Expert systems with applications. 2021. link

40. Transfer Learning Method in Reinforcement Learning-based Traffic Signal Control. Z Mao, J Li, N Zheng, K Tei. Global Conference on Consumer Electronics. 2021. link

41. Probabilistic Graph Neural Networks for Traffic Signal Control. T Zhong, Z Xu, F Zhou. ICASSP. 2021. link

42. Urban Traffic Light Control Considering Capacity Difference Between Public Bus and Private Vehicles. Z Liu, N Wu, K Gao. IEEE Access. 2021. link

43. Self-Optimizing Traffic Light Control Using Hybrid Accelerated Extremum Seeking. F Galarza-Jimenez, JI Poveda. IEEE Conference on Decision and Control. 2021. link

44. Knowledge embedding-assisted multi-exemplar learning particle swarm optimization for traffic signal timing optimization. ZJ Deng, LY Luo, ZH Zhan. IEEE Congress on Evolutionary Computation. 2021. link

45. TSLib: A Unified Traffic Signal Control Framework Using Deep Reinforcement Learning and Benchmarking. TV Tran, TN Doan, M Sartipi. IEEE International Conference on Big Data (Big Data). 2021. link

46. Reinforcement Learning-driven Attack on Road Traffic Signal Controllers. NS Arabi, T Halabi, M Zulkernine. IEEE International Conference on Cyber Security and Resilience. 2021. link

47. A Meta Multi-agent Reinforcement Learning Algorithm for Multi-intersection Traffic Signal Control. S Yang, B Yang. IEEE Intl Conf on Dependable, Autonomic and Secure Computing. 2021. link

48. Adversarial Attacks and Defense in Deep Reinforcement Learning (DRL)-Based Traffic Signal Controllers. A Haydari, M Zhang, CN Chuah. IEEE Open Journal of Intelligent Transportation Systems . 2021. link

49. Improving Traffic Signal Control With Joint-Action Reinforcement Learning. JVB Labres, ALC Bazzan. IEEE Symposium Series on Computational Intelligence. 2021. link

50. GAN and Multi-Agent DRL based Decentralized Traffic Light Signal Control. Z Wang, H Zhu, M He, Y Zhou, X Luo. IEEE Transactions on Vehicular Technology. 2021. link

51. Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks. FSP Borges, AP Fonseca, RC Garcia. Infrastructures. 2021. link

52. Minimize Traffic Congestion with Emergency Facilitation using Deep Reinforcement Learning. D Kodagoda, D Perera, G Seneviratne. International Conference on Advances in ICT for Emerging Regions. 2021. link

53. Joint Control of Lane Allocation and Traffic Light for Changeable-Lane Intersection Based on Reinforcement Learning. ESA Gyarteng, R Shi, Y Long. International Conference on Algorithms, Computing and Artificial Intelligence. 2021. link

54. Combining SysML with Petri Nets for the Design of an Urban Traffic Signal Control. LS Souza, MS Soares. International Conference on Computational Science and Its Applications. 2021. link

55. Intelligent Traffic Signal Control with Deep Reinforcement Learning at Single Intersection. Y Li, J He, Y Gao. International Conference on Computing and Artificial Intelligence. 2021. link

56. Agent-Based Traffic Signal Control Using a Reinforcement Learning Approach. A Agafonov, A Yumaganov. International Conference on Information Technology and Nanotechnology. 2021. link

57. Adaptive Traffic Control With TinyML. AN Roshan, B Gokulapriyan. International Conference on Wireless Communications, Signal Processing and Networking. 2021. link

58. A Spatial-Temporal Graph Attention Network for Multi-intersection Traffic Light Control. L He, L Wu, M Wang, J Li, D Wu. International Joint Conference on Neural Networks. 2021. link

59. Evaluating action durations for adaptive traffic signal control based on deep Q-learning. SA Celtek, A Durdu, MEM Ali. International Journal of Intelligent Transportation Systems Research . 2021. link

60. Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning.. P Mandhare, J Yadav, V Kharat. International Journal of Next-Generation Computing. 2021. link

61. An Innovative Hybrid Biologically Inspired Method for Traffic Optimization Problem. S Srivastava, T Stephan, SK Sahana. International Journal on Artificial Intelligence Tools. 2021. link

62. An effective hybrid-heuristic algorithm for urban traffic light scheduling. CW Tsai, TC Teng, JT Liao, MC Chiang. Neural Computing and Applications . 2021. link

63. IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control. S Yang, B Yang, Z Kang, L Deng. Neural networks. 2021. link

64. Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control. LN Alegre, ALC Bazzan, BC da Silva. PeerJ Computer Science. 2021. link

65. Traffic signal optimization on a square lattice with quantum annealing. D Inoue, A Okada, T Matsumori, K Aihara, H Yoshida. Scientific reports. 2021. link

66. Traffic signal optimization on a square lattice with quantum annealing. D Inoue, A Okada, T Matsumori, K Aihara, H Yoshida. Scientific reports. 2021. link

67. Traffic signal control using hybrid action space deep reinforcement learning. S Bouktif, A Cheniki, A Ouni. Sensors. 2021. link

68. Network Traffic Signal Control with Short-Term Origin-Destination Demand in a Connected Vehicle Environment via Mobile Edge Computing. C Zhang. University of Alberta Libraries. 2021. link

69. Agent-Based Urban Traffic Management for Connected and Automated Vehicles. S Liu. University of California Riverside Dissertations. 2021. link

70. Impact of Deep RL-based Traffic Signal Control on Air Quality. A Haydari, M Zhang, CN Chuah. Vehicular Technology Conference. 2021. link

71. Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data. M Gregurić, M Vujić, C Alexopoulos, M Miletić. Applied Sciences. 2020. link

72. A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction. X Hu, C Zhao, G Wang. arXiv preprint. 2020. link

73. Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning. KA Diaz, D Dailisan, U Sharaf, C Santos, Q Gan. arXiv preprint. 2020. link

74. Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond. S Alemzadeh, R Moslemi, R Sharma. arXiv preprint. 2020. link

75. Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations. A Cabrejas-Egea, C Connaughton. arXiv preprint. 2020. link

76. Pdlight: a deep reinforcement learning traffic light control algorithm with pressure and dynamic light duration. C Zhao, X Hu, G Wang. arXiv preprint. 2020. link

77. Deep reinforcement learning for traffic signal control under disturbances: A case study on Sunway city, Malaysia. F Rasheed, KLA Yau, YC Low. Future Generation Computer Systems. 2020. link

78. Learning Multi-Agent Communication with Policy Fingerprints for Adaptive Traffic Signal Control. Y Zhao, G Xu, Y Duy, M Fangz. IEEE 16th International Conference on Automation Science and Engineering. 2020. link

79. A Novel Approach to Coordinating Green Wave System with Adaptation Evolutionary Strategy. Y Zheng, R Guo, D Ma, Z Zhao, X Li. IEEE Access. 2020. link

80. Cooperative Control for Multi-Intersection Traffic Signal Based on Deep Reinforcement Learning and Imitation Learning. Y Huo, Q Tao, J Hu. IEEE Access. 2020. link

81. Optimizing transportation dynamics at a city-scale using a reinforcement learning framework. L Khaidem, M Luca, F Yang, A Anand, B Lepri. IEEE Access. 2020. link

82. PlanLight: Learning to Optimize Traffic Signal Control With Planning and Iterative Policy Improvement. H Zhang, M Kafouros, Y Yu. IEEE Access. 2020. link

83. Reducing congestion in an intelligent traffic system with collaborative and adaptive signaling on the edge. A Jaleel, MA Hassan, T Mahmood, MU Ghani. IEEE Access. 2020. link

84. A Cooperative Multiagent System for Traffic Signal Control Using Game Theory and Reinforcement Learning. M Abdoos. IEEE Intelligent Transportation Systems Magazine. 2020. link

85. Deep reinforcement learning based traffic signal optimization for multiple intersections in ITS. A Paul, S Mitra. IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). 2020. link

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