Abstract: According to the shortcoming that traffic detector configuration is not considered in traffic flow prediction, this paper proposes an algorithm for short-term traffic flow prediction based on detector optimal selection. The paper takes the minimum?mean square?prediction error?as the objective function, selects suitable detectors with genetic algorithm, and predicts the?short term traffic flow with wavelet neural network. The test results with America I-84 highway measured data show that the proposed algorithm has higher accuracy compared with the traditional method, and it is an efficient method to the short-term traffic flow prediction.

Key words: traffic flow forecasting, sensor optimization, Genetic Algorithm（GA）, wavelet neural network

摘要： 针对交通流短期预测未考虑交通检测器配置的不足，提出了一种基于检测器优化选择的短时交通流预测算法。以预测的均方误差最小为目标函数，通过遗传算法优化选择合适的检测器，以小波神经网络作为预测算法进行短时交通流预测。美国I-84高速公路实测数据的测试结果表明该算法与传统预测方法相比具有更高的预测精度，是一种有效的短时交通流预测方法。

关键词: 交通流预测, 检测器优化, 遗传算法, 小波神经网络