Autonomous Decentralized Control of Traffic Signals that can Adapt to Changes in Traffic

A major challenge for traffic signal control is adapting to unpredictable changes in traffic. To address this issue, we propose an autonomous decentralized control scheme for traffic signals that is based on physics. More specifically, “virtual impulses” given by red signals or preceding cars, which are defined in a similar manner as the impulses generally used in physics, are calculated at each traffic signal by using an optimal velocity model, and traffic signals are switched to reduce these virtual impulses. We performed simulations under various traffic conditions, and the results showed that the proposed control scheme works adaptively and resiliently in response to each set of circumstances. Thus, the virtual impulse can be a key physical quantity for designing adaptive traffic systems

Adaptive Traffic Control Model with Neural Network Analogy

We propose here an autonomous traffic signal control model based on analogy with neural networks. In this model, the duration of blue(green) and red traffic light display at each signal is autonomously adapted. We find a self--organizing collective behavior of such a model through simulation on a two--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 non--linear response of saturation shape as a function of proportion of adaptive signals in the model. 1 1 In the Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems (ICONIP'97, Novermber, Dunedin, New Zealand) pp.939942. 1 Introduction The problem of traffic control is one of the major issues of our society on motorways as well as computer networks. The problem h..