计算机科学 ›› 2019, Vol. 46 ›› Issue (7): 322-326.doi: 10.11896/j.issn.1002-137X.2019.07.049
孔繁钰1,周愉峰1,2,陈纲3
KONG Fan-yu1,ZHOU Yu-feng1,2,CHEN Gang3
摘要： 基于神经网络和大数据的交通流量预测方法层出不穷,但对交通流量预测的精度仍有待进一步提高。为了解决该问题,提出一种基于时空特征挖掘的交通流量预测方法。该方法使用改进的CNN来挖掘交通流量的空间特征,使用递归神经网络来挖掘交通流量的时间特征,能够充分利用交通流量的每周/每天的周期性和时空特征。此外,在该方法中还使用了一种基于相关性的模型,它可以根据过去的交通流量实现自动学习。实验结果表明,相比于其他几种较新的预测方法,所提方法具有较高的交通流量预测精度。
中图分类号:
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