[Paper Review] Autonomous Traffic Signal Control Model with Neural Network Analogy

Toru Ohira|ArXiv.org|Apr 18, 1997

Traffic control and management10 references3 citations

TL;DR

This paper proposes an autonomous traffic signal control model inspired by neural network dynamics, where signal cycle times self-adapt based on local traffic conditions. Through simulation on a 1D lattice road, the model reduces congestion significantly and exhibits a threshold effect, with system-wide effectiveness emerging when a critical proportion of signals are adaptive.

ABSTRACT

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.

Motivation & Objective

• To develop a decentralized, self-organizing traffic signal control system inspired by neural network principles.
• To investigate how autonomous adaptation of signal cycle times affects traffic flow and congestion on a road network.
• To determine the critical threshold of adaptive signals required for system-wide performance improvement.
• To explore emergent collective behavior in a network of interacting, adaptive traffic signals.
• To evaluate the effectiveness of local, reactive signal control in reducing urban traffic congestion.

Proposed method

• The model uses a one-dimensional lattice to represent a road network with traffic signals at discrete intersections.
• Each signal independently adjusts its cycle time based on local traffic density, mimicking neural network learning dynamics.
• Adaptation is governed by a local rule that modifies cycle length in response to queue length or vehicle accumulation.
• The system operates without centralized coordination, relying on local feedback and interaction between adjacent signals.
• Simulations are run over multiple time steps to observe macroscopic traffic behavior and congestion patterns.
• Parameter tuning is applied to optimize the adaptation mechanism for maximum congestion reduction.

Experimental results

Research questions

• RQ1 How does decentralized, autonomous signal control based on neural network analogy affect overall traffic flow?
• RQ2 What is the minimum proportion of adaptive signals required to significantly reduce congestion?
• RQ3 Does self-organizing behavior emerge from local interactions between signals?
• RQ4 How does the system performance change as a function of the number of adaptive signals?
• RQ5 Can local, reactive signal adjustments lead to global improvements in traffic efficiency?

Key findings

• Traffic congestion is greatly diffused when signals have autonomous adaptability with properly tuned parameters.
• A threshold transition in system performance is observed as the proportion of adaptive signals increases.
• Emergent system-wide effectiveness arises from interactions between adaptive signal units.
• The model exhibits self-organizing collective behavior without centralized control.
• Optimal performance is achieved when a critical fraction of signals are adaptive, indicating a phase-like transition.
• The system reduces localized bottlenecks through dynamic, responsive cycle time adjustments.