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Performance comparison between queueing theoretical Optimality and Q-learning approach for intersection Traffic signal control



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This paper proposes the Performance comparison for optimal Traffic signal controls based on the following two frameworks: M/M/1 and D/D/1 Queueing models, and Q-learning approach. Firstly, using the M/M/1 and D/D/1 models, the optimal split derivation has been obtained to minimise the Mean waiting time of an intersection. Additionally, the Q-learning framework has been proposed in conjunction with the use of the macroscopic Cell transmission model (CTM) to update the Vehicle state dynamics upon Q-learning actions. The two approaches have been compared in terms of the Network throughput and the average vehicle delay per completed trip in nine scenarios. The simulation results from the microscopic AIMSUN traffic simulator show that the Q-learning approach can greatly improve the intersection throughput and can significantly reduce the average vehicle delay per completed trip with the respective M/M/1 and D/D/1 approaches. © 2012 IEEE.

Cell transmission model (11 items found) | Performance comparison (65 items found) | Traffic signal control (4 items found) | Q-learning approach (1 items found) | Network throughput (9 items found) | Mean waiting time (2 items found) | Queueing model (4 items found) | Vehicle state (1 items found) | Q-learning (5 items found) | CTM (2 items found) | Queueing theoryCell transmission model | Vehicle actuated signals | Artificial intelligence | Numerical analysis | Traffic simulators | Queueing theory | Optimization | Optimality |

ต้นฉบับข้อมูล : scopus