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面向动态交通分配的交通需求深度学习预测方法

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为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法.根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中交通需求的周期性、随机性和非线性等特征,为减少数据噪声的干扰,引入局部加权回归周期趋势分解方法将交通需求数据分解,将其中的趋势分量和余项分量作为深度学习预测方法的输入量,周期分量采用周期估计进行预测;选用具有随机寻优能力强、寻优效率高等特点的布谷鸟寻优算法优化预测方法的隐藏层单元数量、学习速率和训练迭代次数等核心参数.应用西安市长安区的卡口车牌数据验证该方法.结果表明:本文模型的预测结果在高峰及平峰各连续4个时段内相比于自回归滑动平均模型、长短期记忆神经网络模型、支持向量回归模型,平均绝对误差降低了10.55％～19.80％,均方根误差降低了11.20％～17.99％,决定系数提升了8.62％～12.48％;相比遗传算法、粒子群算法优化的模型,平均绝对误差降低了7.36％～13.81％,均方根误差降低了4.23％～10.67％,决定系数提升了3.50％～7.01％,且本文模型运行时间最短.说明与对比模型相比,本文所建立的预测方法在面向动态交通分配的交通需求预测中具有更高的预测精度.
Traffic Demand Prediction Method Based on Deep Learning for Dynamic Traffic Assignment
This paper proposes a deep learning traffic demand prediction method to meet the requirements of high accuracy and time sensitivity in dynamic traffic assignment.The time interval of traffic demand data is determined based on the requirements of dynamic traffic assignment.A prediction method using long short-term memory neural network is established for better performance in complex traffic demand.Combining the periodicity,randomness and nonlinearity of traffic demand in dynamic traffic assignment,this study uses a time series decomposition method to decompose the traffic demand data and to reduce the interference of data noise.The trend component and residual component are used as the input of the deep learning prediction method.Meanwhile,the periodic component is predicted using the cycles.The key parameters of the prediction method,such as the number of hidden layer units,learning rate and training iterations,are optimized by using the cuckoo search algorithm,which is characterized by strong random optimization ability and high optimization efficiency.The proposed method is verified using the checkpoint data in Chang'an District of Xi'an,China.In each of the four consecutive periods of peak and off peak,the results of proposed method are compared with the auto regressive moving average model,the long short-term memory model,and the support vector regression model.The results indicate a reduction of the average absolute error of 10.55％to 19.80％,a reduction of the root mean square error of 11.20％to 17.99％,and the coefficient of determination increased by 8.62％to12.48％.Compared with the models optimized by genetic algorithm and particle swarm optimization,the proposed model reduced the average absolute error by 7.36％to 13.81％and reduced the root mean square error by 4.23％to 10.67％.The coefficient of determination increased by 3.50％to 7.01％.The proposed model has the shortest running time.Compared with the traditional methods,the proposed prediction method has higher prediction accuracy in the traffic demand prediction for dynamic traffic assignment.

intelligent transportationtraffic demand predictionCuckoo search algorithmlong short-term memorydynamic traffic assignmentseasonal and trend decomposition using loess

李岩、王泰州、徐金华、陈姜会、汪帆

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长安大学,运输工程学院,西安 710064

中交第一公路勘察设计研究院有限公司,西安 710075

智能交通 交通需求预测 布谷鸟寻优算法 长短期记忆神经网络 动态交通分配 局部加权回归周期趋势分解

国家自然科学基金陕西省自然科学基础研究计划项目

514080492020JM-237

2024

10.16097/j.cnki.1009-6744.2024.01.011

交通运输系统工程与信息
中国系统工程学会

交通运输系统工程与信息

CSTPCD北大核心
影响因子：0.664
ISSN：1009-6744
年,卷(期)：2024.24(1)
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