The Max-Pressure Controller for Arbitrary Networks of Signalized Intersections

By Pravin Pratap Varaiya. 2013

296 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/abafcf4d5f1231c05d385dd36ed7fca420da293f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Hua Wei
2. Guanjie Zheng
3. Chacha Chen
4. Kai Xu
5. Nan Xu
6. Zhenhui Jessie Li
7. Nizar Bouguila
8. Saeed Rahmani

In Management Information Systems

1. Yuanhao Xiong
2. Hua Wei
3. Chacha Chen
4. Nizar Bouguila
5. Yanmin Zhu
6. Saeed Rahmani
7. Nan Xu
8. Asiye Baghbani

In Transportation

1. Yuanhao Xiong
2. Hua Wei
3. Chacha Chen
4. Nizar Bouguila
5. Yanmin Zhu
6. Saeed Rahmani
7. Nan Xu
8. Asiye Baghbani

Citations in the corpus

• Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2020, methodology): “Moreover, learning-based models show superior performance compared to MaxPressure because there is a large performance margin from PressLight in either travel time or throughput since it ignores the assessment of previous actions from the environment.”
• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “…RL model for signal coordination for the first time on a real-world large-scale network (including 196 intersections) and compared the performance of their model with fixed-time [158], Max Pressure [159], Authorized licensed use limited to the terms of the applicable license agreement with IEEE.”
• CoLight: Learning Network-level Cooperation for Traffic Signal Control (2019, methodology): “, uniform arrival rate [21, 27] and unlimited lane capacity [24]).”