This thesis presents a study on an adaptive traffic signal controller for real-time operation.
An approximate dynamic programming (ADP) algorithm is developed for controlling traffic
signals at isolated intersection and in distributed traffic networks. This approach is derived
from the premise that classic dynamic programming is computationally difficult to solve, and
approximation is the second-best option for establishing sequential decision-making for
complex process. The proposed ADP algorithm substantially reduces computational burden
by using a linear approximation function to replace the exact value function of dynamic
programming solution. Machine-learning techniques are used to improve the approximation
progressively. Not knowing the ideal response for the approximation to learn from, we use the
paradigm of unsupervised learning, and reinforcement learning in particular. Temporal-difference
learning and perturbation learning are investigated as appropriate candidates in the
family of unsupervised learning. We find in computer simulation that the proposed method
achieves substantial reduction in vehicle delays in comparison with optimised fixed-time
plans, and is competitive against other adaptive methods in computational efficiency and
effectiveness in managing varying traffic. Our results show that substantial benefits can be
gained by increasing the frequency at which the signal plans are revised. The proposed ADP
algorithm is in compliance with a range of discrete systems of resolution from 0.5 to 5
seconds per temporal step. This study demonstrates the readiness of the proposed approach
for real-time operations at isolated intersections and the potentials for distributed network
control
