  We propose here an autonomous traffic signal control model based on analogy
with neural networks. In this model, the length of cycle time period of traffic
lights at each signal is autonomously adapted. We find a self-organizing
collective behavior of such a model through simulation on a one-dimensional
lattice model road: traffic congestion is greatly diffused when traffic signals
have such autonomous adaptability with suitably tuned parameters. We also find
that effectiveness of the system emerges through interactions between units and
shows a threshold transition as a function of proportion of adaptive signals in
the model.
