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。
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 twodimensional matrices which could represent the traffic states,and the features represented by the input timespace 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.
作者
唐克双
陈思曲
曹喻旻
张锋鑫
TANG Keshuang;CHEN Siqu;CAO Yumin;ZHANG Fengxin(Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China;Intelligent Transportation Department,Lianyungang Jari Electronics Co.,Ltd.,Lianyungang 222061,Jiangsu,China)
出处
《同济大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2021年第3期370-381,共12页
Journal of Tongji University:Natural Science
基金
国家自然科学基金(61673302)。
作者简介
第一作者: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;通信作者: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。