Traffic signal control is an effective way to regulate traffic flow to avoid conflict and reduce congestions. This research investigates a real-time traffic signal control system that integrates a traffic flow prediction model and an adaptive control scheme based on dynamic programming with rolling horizon. The proposed approach estimates the parameter of the arriving traffic flow at the intersection, predicts the state transition probabilities, and then formulates the traffic signal control problem as a decision-making problem of a stochastic system. Two different traffic arrival patterns are considered, including the normal distribution and the Poission distribution.


Electrical and Computer Engineering

Publisher statement

Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.



URL: https://digitalcommons.calpoly.edu/eeng_fac/110