关键词: 传感网, 交通信号控制, 多时段控制, 深度注意力机制, 数据输入

Abstract:

In order to overcome the arbitrariness and empirical nature of the continuous data discretization selection of time-of-day control model data input source, this paper proposes an optimization method for selecting the input source of an deep attention recursive network based on the combination of sensor network and artificial intelligence theory. Firstly, it uses the Synchron and Sumo simulation evaluation function module to standardize the typical samples of the input source. Secondly, the key attributes such as the starting time are used as the model input. At the same time, the optimal data input source is selected as the data output to build a model. Finally, it is realized by simulating the input layer, middle layer, output layer and optimization method of the entire model, and the actual traffic flow data of a certain city is used as the test data for evaluation and comparison analysis. The results show that, compared with other traditional methods that select 50% and 80% of the traffic volume as the fixed input source of the model, the innovative model in this paper is more accurate and efficient. The total delay time of the whole day is effectively reduced.

Key words: sensor networks, traffic signal control, time-of-day control, deep attention mechanism, data input