基于Inception卷积神经网络的城市快速路行程速度短时预测
Short-Term Travel Speed Prediction for Urban Expressways Based on Convolutional Neural Network with Inception Module
投稿时间:2020-06-18  
DOI:10.11908/j.issn.0253-374x.20242     稿件编号:    中图分类号:U491.14
 
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中文摘要
      为了高效捕捉城市快速路复杂的交通拥堵特征,提升短时行程速度预测的准确性,以卷积神经网络为基础,结合Inception模块,构建行程速度短时预测模型。将行程速度信息按照时空关联关系组织为二维数据矩阵,以图像为特征学习对象,自动提取交通数据高维特征并学习多粒度复杂交通拥堵模式,通过系统的网络设计与测试训练得到模型最优结构参数和优化参数,结合回归分析方法与梯度幅度相似性偏差指标,综合评价模型性能。实证结果表明,模型提取行程速度数据时序特征和时空演化特征能力较强,预测准确性较高,可进一步应用于其他交通参数的短时预测。
英文摘要
      In order to effectively learn the mixed traffic congestion patterns from the urban expressways and improve the accuracy of short-term travelling speed prediction, based on the convolution neural network, and incorporated with the Inception Module, a short-term travelling speed prediction model was established. The travelling speed information was arranged into two-dimensional matrices which could represent the traffic states, and the features represented by the input time-space travel speed images were learnt. The optimum model was obtained as the result of a systematic neural network design and training process, with the ability to automatically recognize multi-scale mixed traffic congestion patterns and extract high-dimensional features of the traffic data. Besides the regression analysis method as well as the gradient magnitude similarity deviation indicator was introduced to conduct a comprehensive evaluation. The case study shows that the proposed model outperforms other models in learning the temporal/spatiotemporal features from traffic data with a high prediction accuracy, which can be further applied to making short-term prediction for other traffic parameters.
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