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

摘要：

为了克服多时段控制模型数据输入源连续型数据离散化选取的随意性与经验性，提出一种基于传感网与人工智能理论相结合的交叉口多时段控制深度注意力递归网络输入源选取优化方法。利用Synchro与Sumo仿真评价功能模块对数据输入源进行标准化处理。以已标记好的控制方案中起始时间等关键属性作为模型输入，同时以最优数据输入源选取点为数据输出构建模型。通过对整个模型输入层、中间层、输出层和优化方法进行仿真实现，并以某城市实际交通流量数据为测试数据进行评价对比分析。结果表明，该创新模型与传统50%位、80%位取值法相比，信号配时方案更加精准高效，交叉口全天总延误时间有效降低。

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