Abstract
The increased vehicle usage significantly aggravate the urban air pollution, which have great impact on the public health. Therefore, it is necessary to make proper traffic control policies and reduce traffic emissions. However, it is difficult to establish control strategies based on modeling methods, and carry out online control based on historical traffic information for the complex time-varying characteristics of emissions. In this paper, we present a deep reinforcement learning emission control strategy, which automatically learns the optimal traffic flow and speed limits to reduce traffic emission on the target road segment based on the temporal traffic information. The proposed approach is evaluated on real world vehicle emission data in Hefei. And the results demonstrate the effectiveness of the proposed approach against baseline methods.
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