arXiv:2605.29693v1 Announce Type: new Abstract: Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution. Traditional traffic signal control systems often fail to adapt to dynamic traffic conditions. Adaptive traffic signal control can improve urban traffic without changing road infrastructure. Deep Reinforcement Learning (DRL) has shown strong performance for this task, but existing delay and queue-based rewards often produce short-sighted or unstable policies. This paper proposes a Momentum-Based Reward Function (MBRF) th
The increasing sophistication of AI and reinforcement learning models allows for more advanced solutions to complex urban problems like traffic management.
Improving urban traffic flow through AI-driven signal control can significantly reduce commute times, fuel consumption, and environmental pollution.
This research introduces a novel reward function that promises more stable and effective DRL policies for adaptive traffic signal control, moving beyond previous short-sighted solutions.
- · Smart city initiatives
- · Urban populations
- · Environmental agencies
- · AI developers in DRL
- · Traditional traffic control systems
- · Commuters in highly congested cities (if not adopted)
More efficient urban mobility reduces economic friction and improves quality of life.
Reduced fuel consumption and emissions contribute to environmental sustainability goals and potentially lower healthcare costs related to air pollution.
Widespread adoption could free up urban space currently dedicated to parking and road infrastructure, enabling new forms of urban development or green areas.
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