An Integrated Model for Adaptive Intersection Signal Control

This research presents a local signal optimization system that integrates a neural network prediction model with a dynamic programming algorithm. With the reliable prediction function, the proposed system can perform real proactive rather than responsive control. Since the developed neural network model is capable of performing a reliable prediction 3-time-steps ahead under various surveillance environments, our proposed integrated control system offers the potential for use in practice. The preliminary results from extensive simulation tests have proved the promising properties of our proposed system.

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