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基于改进回声状态网络的盖亚大数据短时交通状态预测研究
A Short-Term Traffic Forecasting Research Based on Didi Chuxing GAIA Open Dataset Using Echo State Network Optimized by PSO Algorithm

DOI: 10.12677/HJDM.2019.94019, PP. 153-158

Keywords: 回声状态网络，交通预测，粒子群算法
Echo State Network, Traffic Forecasting, PSO Algorithm

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Abstract:

短期交通预测是现代智能交通系统重要的组成部分，本文设计了改进的回声状态网络基于盖亚开放数据来对短时交通状态进行预测。对于数据集中的无效数据，采用随机森林算法进行数据预处理，剔除其中的无效数据。由于盖亚数据集中的实时交通数据量巨大，采用粒子群算法对回声状态网络参数进行优化。最后采用2016年10月~11月成都市二环局部区域轨迹数据作为样本集对所设计的方法进行了验证，结果表明了该方法可以有效的对短时交通状态进行预测。
Short-term traffic forecasting is an important part of contemporary intelligent transportation systems. In this paper, based on echo state network, a procedure is proposed to provide a forecast of traffic state. For the invalid data in the Didi Chuxing GAIA Open Dataset, the random forest algo-rithm is used to preprocess the data and eliminate the invalid data. Because of the huge amount of real-time traffic data in GAIA data set, particle swarm optimization algorithm is employed to op-timize the parameters of echo state network. Finally, the method is validated by using the local area trajectory data of the second ring road in Chengdu from October to November 2016 as the sample set. The result illustrates the effectiveness of this method.

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