基于Inception卷积神经网络的城市快速路行程速度短时预测
作者:
作者单位:

1.同济大学 道路与交通工程教育部重点实验室,上海 201804;2.连云港杰瑞电子有限公司 智能交通事业部,江苏 连云港 222061

作者简介:

唐克双(1980—),男,教授,工学博士,主要研究方向为智能交通系统、信号控制、驾驶行为。 E-mail: tang@tongji.edu.cn

通讯作者:

张锋鑫(1982—),男,高级工程师,工程硕士,主要研究方向为智能交通系统、软件系统架构、大数据。 E-mail:zhangfengxin3@163.com

中图分类号:

U491.14

基金项目:

国家自然科学基金(61673302)


Short-Term Travel Speed Prediction for Urban Expressways Based on Convolutional Neural Network with Inception Module
Author:
Affiliation:

1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2.Intelligent Transportation Department, Lianyungang Jari Electronics Co., Ltd., Lianyungang 222061, Jiangsu, China

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    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。

    Abstract:

    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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唐克双,陈思曲,曹喻旻,张锋鑫.基于Inception卷积神经网络的城市快速路行程速度短时预测[J].同济大学学报(自然科学版),2021,49(3):370~381

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  • 收稿日期:2020-06-18
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  • 在线发布日期: 2021-04-06
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