计算机科学 ›› 2018, Vol. 45 ›› Issue (6): 222-227.doi: 10.11896/j.issn.1002-137X.2018.06.040
沈夏炯1,2, 张俊涛2, 韩道军1,2
SHEN Xia-jiong1,2, ZHANG Jun-tao2, HAN Dao-jun1,2
摘要： 短时交通流预测是交通流建模的一个重要组成部分,在城市道路交通的管理和控制中起着重要的作用。然而,常见的时间序列模型(如ARIMA)、随机森林(RF)模型在交通流预测方面由于被构建模型产生的残差和输入变量所影响,其预测精度受到限制。针对该问题,提出了一种基于梯度提升回归树的短时交通预测模型来预测交通速度。首先,模型引入Huber损失函数作为模型残差的处理方法;其次,在输入变量中考虑预测断面受到毗邻空间因素和时间因素相关性的影响。模型在训练过程中通过不断调整弱学习器的权重来纠正模型的残差,从而提高模型预测的精度。利用某城市快速路的交通速度数据进行实验,并使用MSE和MAPE等指标将本文模型与ARIMA模型和随机森林模型进行对比,结果表明,文中所提模型的预测精度最好,从而验证了模型在短时交通流预测方面的有效性。
中图分类号:
| [1]YAO B,CHEN C,CAO Q,et al.Short-Term Traffic Speed Prediction for an Urban Corridor[J].Computer-Aided Civil and Infrastructure Engineering,2017,32(2):154-169. [2]WANG J,SHI Q X.Summary of short-term traffic flow prediction model[J].Its Communication,2005,1(1):10-13.(in Chi-nese) 王进,史其信.短时交通流预测模型综述[J].Its通讯,2005,1(1):10-13. [3]ZHANG Y,ZHANG Y,HAGHANI A.A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatilitymodel[J].Transportation Research Part C:Emerging Technologies,2014,43(1):65-78. [4]ZHANG J,LIU X M,HE Y L,et al.Application of ARIMA Model in Forecasting Traffic Accidents[J].Journal of Beijing University of Technology,2007,33(12):1295-1299.(in Chinese) 张杰,刘小明,贺玉龙,等.ARIMA模型在交通事故预测中的应用[J].北京工业大学学报,2007,33(12):1295-1299. [5]CHENG Z,CHEN X F.The model of short term traffic flow prediction based on the random forest[J].Microcomputer & Its Applications,2016,35(10):46-49.(in Chinese) 程政,陈贤富.基于随机森林模型的短时交通流预测方法[J].微型机与应用,2016,35(10):46-49. [6]ZHANG Y,HAGHANI A.A gradient boosting method to improve travel time prediction[J].Transportation Research Part C Emerging Technologies,2015,58(3):308-324. [7]ZHANG F,ZHU X,HU T,et al.Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations[J].ISPRS International Journal of Geo-Information,2016,5(11):201-204. [8]DING C,WANG D,MA X,et al.Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees[J].Sustainability,2016,8(11):1100-1115. [9]BREIMAN L.Arcing the Edge:Technical Report 486[R].Berkeley:University of California,CA,USA,1997. [10]Gradient boosting[EB/OL].https://en.wikipedia.org/wiki/Gra-dient_boosting. [11]FRIEDMAN J H.Greedy Function Approximation:A Gradient Boosting Machine[J].Annals of Statistics,2000,29(5):1189-1232. [12]FRIEDMAN J H.Stochastic gradient boosting[J].Computational Statistics & Data Analysis,2002,38(4):367-378. [13]SHEN D M,QIAO D X,XU K,et al.Gradient Boosting Regression Tree Algorithm and Application of E-commerce BrandRe-commendation[J].Computer Systems & Applications,2015,24(6):114-120.(in Chinese) 申端明,乔德新,许琨,等.梯度渐进回归树算法在电子商务品牌推荐中的应用[J].计算机系统应用,2015,24(6):114-120. [14]BREIMAN L.Random Forests[J].Machine Learning,2001,45(1):5-32. [15]周英,卓金武,卞月青.大数据挖掘:系统方法与实例分析[M].北京:机械工业出版社,2016:25-260 [16]Huber loss[EB/OL].https://en.wikipedia.org/wiki/Huber_loss. |
| [1] | 孟月波, 穆思蓉, 刘光辉, 徐胜军, 韩九强. 基于向量注意力机制GoogLeNet-GMP的行人重识别方法 Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism 计算机科学, 2022, 49(7): 142-147. https://doi.org/10.11896/jsjkx.210600198 |
| [2] | 高荣华, 白强, 王荣, 吴华瑞, 孙想. 改进注意力机制的多叉树网络多作物早期病害识别方法 Multi-tree Network Multi-crop Early Disease Recognition Method Based on Improved Attention Mechanism 计算机科学, 2022, 49(6A): 363-369. https://doi.org/10.11896/jsjkx.210500044 |
| [3] | 黄颖琦, 陈红梅. 基于代价敏感卷积神经网络的非平衡问题混合方法 Cost-sensitive Convolutional Neural Network Based Hybrid Method for Imbalanced Data Classification 计算机科学, 2021, 48(9): 77-85. https://doi.org/10.11896/jsjkx.200900013 |
| [4] | 张晓宇, 王彬, 安卫超, 阎婷, 相洁. 基于融合损失函数的3D U-Net++脑胶质瘤分割网络 Glioma Segmentation Network Based on 3D U-Net＋+ with Fusion Loss Function 计算机科学, 2021, 48(9): 187-193. https://doi.org/10.11896/jsjkx.200800099 |
| [5] | 冯姣, 陆昶谕. 基于残差注意力网络的跨媒体检索方法 Cross Media Retrieval Method Based on Residual Attention Network 计算机科学, 2021, 48(6A): 122-126. https://doi.org/10.11896/jsjkx.201100026 |
| [6] | 段菲, 王慧敏, 张超. 面向数据表示的Cauchy非负矩阵分解 Cauchy Non-negative Matrix Factorization for Data Representation 计算机科学, 2021, 48(6): 96-102. https://doi.org/10.11896/jsjkx.200700195 |
| [7] | 石先让, 宋廷伦, 唐得志, 戴振泳. 一种新颖的单目视觉深度学习算法:H_SFPN Novel Deep Learning Algorithm for Monocular Vision:H_SFPN 计算机科学, 2021, 48(4): 130-137. https://doi.org/10.11896/jsjkx.200400090 |
| [8] | 曲浩, 崔超然, 王萧萧, 苏雅茜, 韩晓晖, 尹义龙. 基于非均衡数据层次学习的案件案由预测方法 Hierarchical Learning on Unbalanced Data for Predicting Cause of Action 计算机科学, 2021, 48(12): 337-342. https://doi.org/10.11896/jsjkx.201100212 |
| [9] | 穆逢君, 邱静, 陈路锋, 黄瑞, 周林, 于功敬. 面向人机协同的物体姿态估计帧间稳定性优化方法 Optimization Method for Inter-frame Stability of Object Pose Estimation for Human-Machine Collaboration 计算机科学, 2021, 48(11): 226-233. https://doi.org/10.11896/jsjkx.201200095 |
| [10] | 孟丽莎, 任坤, 范春奇, 黄泷. 基于密集卷积生成对抗网络的图像修复 Dense Convolution Generative Adversarial Networks Based Image Inpainting 计算机科学, 2020, 47(8): 202-207. https://doi.org/10.11896/jsjkx.190700017 |
| [11] | 景雨, 祁瑞华, 刘建鑫, 刘朝霞. 基于改进多尺度深度卷积网络的手势识别算法 Gesture Recognition Algorithm Based on Improved Multiscale Deep Convolutional Neural Network 计算机科学, 2020, 47(6): 180-183. https://doi.org/10.11896/jsjkx.200200030 |
| [12] | 王立华,杜明辉,梁亚玲. 基于角度特征的分类网络 Classification Net Based on Angular Feature 计算机科学, 2020, 47(2): 83-87. https://doi.org/10.11896/jsjkx.190500077 |
| [13] | 王丽星, 曹付元. 基于Huber损失的非负矩阵分解算法 Huber Loss Based Nonnegative Matrix Factorization Algorithm 计算机科学, 2020, 47(11): 80-87. https://doi.org/10.11896/jsjkx.190900144 |
| [14] | 朱维军, 王鑫, 钟英辉, 樊永文, 陈永华. 一种基于梯度提升回归树的系外行星宜居性预测方法 Habitability Prediction of Exoplanets Based on GBRT Algorithm 计算机科学, 2019, 46(6A): 71-73. |
| [15] | 鲁淑霞, 蔡莲香, 张罗幻. 基于零阶减小方差方法的鲁棒支持向量机 Robust SVM Based on Zeroth Order Variance Reduction 计算机科学, 2019, 46(11): 193-201. https://doi.org/10.11896/jsjkx.181001840 |
|
||
首页