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Reinforcement learning (RL) provides a flexible mechanism for adapting traffic signal control to heterogeneous traffic and sensing conditions, but directly learned signal actions do not automatically inherit the analytic...
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Max-pressure (MP) control adapts signal phases to prevailing traffic states, but its decision timescale is typically fixed a priori. This paper treats the decision timescale as an explicit dimension of MP control. We fir...
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Max-pressure (MP) traffic signal control selects phases using pressure scores computed from estimated traffic states, but these states are uncertain and their consequences depend on whether they alter phase ranking and h...