Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

Research article (Expert Systems with Applications, 2024) · cited 29× · AI/ML
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Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

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Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning. Retrieved May 24, 2026, from https://4ort.xyz/entity/dynamic-traffic-signal-control-for-heterogeneous-traffic-conditions-using-max-pressure-and-reinforcement-learning
MLA “Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/dynamic-traffic-signal-control-for-heterogeneous-traffic-conditions-using-max-pressure-and-reinforcement-learning.
BibTeX @misc{4ortxyz_dynamic-traffic-signal-control-for-heterogeneous-traffic-conditions-using-max-pressure-and-reinforcement-learning_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning}}, year = {2026}, url = {https://4ort.xyz/entity/dynamic-traffic-signal-control-for-heterogeneous-traffic-conditions-using-max-pressure-and-reinforcement-learning}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning — https://4ort.xyz/entity/dynamic-traffic-signal-control-for-heterogeneous-traffic-conditions-using-max-pressure-and-reinforcement-learning (retrieved 2026-05-24)

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