Adaptive Traffic-Signal Control using Discrete Event Simulation Model

Aljaafreh, Ahmad; Al-Oudat, Naeem; Saleh, Ma�en · 2014 · Crossref

DOI: 10.5120/17737-8910

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Summary

This paper addresses the challenge of optimizing traffic signal control at single intersections to minimize vehicle waiting times and prevent congestion. The authors identify that while fixed-time controllers are simple, they fail to adapt to variable traffic conditions, whereas adaptive controllers can adjust cycle times and phase sequences based on real-time sensor data. The study aims to determine optimized timing parameters through simulation, specifically comparing a baseline fixed-time controller against three proposed adaptive algorithms. The research employs a discrete event simulation model developed using Matlab/Simulink/SimEvents. The simulation models a four-entry intersection where vehicle arrivals follow an exponential distribution. The authors evaluate three specific adaptive algorithms: (1) AW Adaptive, which adjusts green intervals based on current queue lengths; (2) AW Predictive, which calculates green intervals by predicting future queue lengths using current queues and arrival rates; and (3) AW VariableC, which dynamically adjusts both green intervals and the total cycle length based on service times and queue lengths. These are compared against a baseline fixed-time controller (AW Fixed) that uses a preset optimal cycle length determined for specific arrival rates. Simulation results from 51 runs reveal distinct performance characteristics under different traffic conditions. When arrival rates are equal across all entries, the baseline fixed-time controller performs optimally, as it utilizes the pre-calculated optimal cycle length without computational overhead. However, the AW VariableC algorithm outperforms all others in this scenario by minimizing idle time, though it requires more frequent calculations. Crucially, when there is high variance in arrival rates across entries, the adaptive algorithms significantly outperform the fixed-time baseline. The AW VariableC algorithm demonstrates the most robust performance, maintaining a nearly constant average waiting time regardless of the variance in arrival rates. This resilience is attributed to its ability to adjust the cycle length dynamically, thereby eliminating the "wasted" time inherent in fixed-cycle controllers during fluctuating traffic conditions. The study concludes that adaptive control, particularly algorithms that adjust both green intervals and cycle lengths like AW VariableC, offers superior performance in variable traffic environments compared to fixed-time controllers. While these adaptive methods incur higher computational overhead and require more input parameters, they provide a more efficient solution for managing traffic congestion under unpredictable conditions. The authors suggest future work should explore adapting the phase sequence and extending the model to distributed control systems managing multiple intersections.

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