engleski

Variable Speed Limit Control Based on Deep Reinforcement Learning: A Possible Implementation

Today’s urban motorways cannot fulfill their pur- pose to simultaneously serve transit and local urban trafficwith a high Level of Service. In the case of urban motorwayinfrastructure, the traditional "build only" approach is notalways possible due to the lack of space. This study is focused onthe Variable Speed Limit Control (VSLC) as one of the trafficcontrol methods applicable for any type of motorway and Q-learning as one commonly used approach for designing learningbased VSLC algorithms. The drawback of this methodology isthe representation and exploration of the large state-action spaceas it is the case in its application for VSLC. This study introducesa Deep Q-Network to approximate the Q-function and presents anovel learning approach for the VSLC application with possibilityto track vehicles on the microscopic level. The proposed rewardfunction steers the learning towards the improvement of rewardand prevention of oscillation among consecutive speed limits.

Traffic Control ; Variable Speed Limit Control ; Intelligent Transportation Systems ; Learning Systems ; Deep Q-Learning Network ; Criteria Functions ; Performance Analysis

nije evidentirano