Abstract
The basic principle of optimal traffic control is the appropriate real-time response to dynamic traffic flow changes. Signal plan efficiency depends on a large number of input parameters. An actuated signal system can adjust very well to traffic conditions, but cannot fully adjust to stochastic traffic volume oscillation. Due to the complexity of the problem analytical methods are not applicable for use in real time, therefore the purpose of this paper is to introduce heuristic method suitable for traffic light optimization in real time. With the evolution of artificial intelligence new possibilities for solving complex problems have been introduced. The goal of this paper is to demonstrate that the use of the Q learning algorithm for traffic lights optimization is suitable. The Q learning algorithm was verified on a road artery with three intersections. For estimation of the effectiveness and efficiency of the proposed algorithm comparison with an actuated signal plan was carried out. The results (average delay per vehicle and the number of vehicles that left road network) show that Q learning algorithm outperforms the actuated signal controllers. The proposed algorithm converges to the minimal delay per vehicle regardless of the stochastic nature of traffic. In this research the impact of the model parameters (learning rate, exploration rate, influence of communication between agents and reward type) on algorithm effectiveness were analysed as well.
Highlights
Due to the continuous growth of population and motorization and due to changes in travel behaviour we are facing increase of traffic volumes on the existing road system
Signal plans for pre-timed traffic light controllers were defined on the basis of historical traffic volume data
The proposed Q learning algorithm for the signal plan optimization was tested for the different combination of parameters mentioned above for two different traffic volumes, namely for oversaturated and saturated traffic flow
Summary
Due to the continuous growth of population and motorization and due to changes in travel behaviour we are facing increase of traffic volumes on the existing road system. Traffic light controllers are designed to coordinate the time between crossing traffic flows that use the same space at an intersection. Signal plans for pre-timed traffic light controllers were defined on the basis of historical traffic volume data. One could say the traffic flow is a living organism which is changing continuously In this context, the question arises as to whether a system based only on historical data is sufficiently effective. The question arises as to whether a system based only on historical data is sufficiently effective This suggests that traffic light controllers sensitive to traffic changes should be developed. A signal plan for this type of signal controllers continuously checks traffic flow and adjusts itself to the current traffic volume. Despite the flexibility of the system, considerable work for system calibration at major traffic volume change is required [1]
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