基于组合模型的短时交通流量预测算法

芮兰兰; 李钦铭

doi:10.11999/JEIT150846

基于组合模型的短时交通流量预测算法

doi: 10.11999/JEIT150846

芮兰兰¹,
李钦铭¹

基金项目:

国家自然科学基金创新研究群体科学基金(61121061)，国家自然科学基金(61302078, 61372108)，北京高等学校青年英才计划项目(YETP0476)

计量
- 文章访问数: 1767
- HTML全文浏览量: 181
- PDF下载量: 981
- 被引次数: 0
出版历程
- 收稿日期: 2015-07-14
- 修回日期: 2016-01-08
- 刊出日期: 2016-05-19

Short-term Traffic Flow Prediction Algorithm Based on Combined Model

RUI Lanlan¹,
LI Qinming¹

Funds:

Funds for Creative Research Groups of China (61121061), The National Natural Science Foundation of China (61302078, 61372108), Beijing Higher Education Young Elite Teacher Project (YETP0476)

摘要

摘要: 交通流量预测是实现智能交通技术的核心问题，及时准确地预测道路交通流量是实现动态交通管理的前提，短时交通流量的预测是交通流量预测的重要组成部分。该文针对十字路口的短时交通流量预测问题设计了基于交通流量序列分割和极限学习机(Extreme Learning Machine, ELM)组合模型的交通流量预测算法(Traffic Flow Prediction Based on Combined Model, TFPBCM)。该算法首先采用K-means对交通流量数据在时间上进行序列分割，然后采用ELM对各个序列进行建模和预测。仿真实验证明，与单一的BP(Back Propagation)神经网络和ELM相比，该组合模型算法建模时间为BP的1/10, ELM建模时间的4倍，均方误差为BP的1/50, ELM的1/20，该组合模型算法决定系数R2更接近于1，模型可信度更高。
- 短时交通流量 /
- K均值算法 /
- 极限学习机 /
- 组合模型算法
Abstract: Traffic flow prediction is a key problem of realizing intelligent transportation technology. Forecasting traffic flow in time and accurately is the precondition to realize the dynamic traffic management. Short -term traffic flow prediction is an important part of traffic flow prediction. In this paper, the Traffic Flow Prediction Based on Combined Model (TFPBCM) based on traffic flow sequence partition and Extreme Learning Machine (ELM) is designed for the short time traffic flow forecasting. The algorithm divides the traffic flow into different patterns along a time dimension by K-means, and then models and forecasts for each pattern by ELM. The proposed algorithm is compared with Back Propagation (BP) and ELM. The combined model algorithm on modeling time is 1/10 of BP, but is 4 times ELM. Its MSE is 1/50 of BP and 1/20 of ELM. The combined model algorithms coefficient of detemination (R2 ) is close to 1, so the credibility of the model is higher than others.
- Short-term traffic flow /
- K-means algorithm /
- Extreme Learning Machine (ELM) /
- Traffic Flow Prediction Based on Combined Model (TFPBCM)

HTML全文

参考文献(31)

兰云. 短时交通流量预测研究[D]. [硕士论文], 西北工业大学, 2002.

LAN Yun. The study on short time traffic flow prediction[D]. [Master dissertation], Northwestern Polytechnical University, 2002.

范腾腾. 城市道路交通流量短时预测研究[D]. [硕士论文], 北京交通大学, 2012.

FAN Tengteng. The research of urban road traffic flow short-term prediction[D]. [Master dissertation], Beijing Jiaotong University, 2012.

覃明贵. 城市道路交通数据挖掘研究与应用[D]. [博士论文], 复旦大学, 2010.

QIN Minggui. Research and application on the urban traffic data mining[D]. [Ph.D. dissertation], Fudan University, 2010.

曹虹. 基于BP神经网络的交通流量预测[D]. [硕士论文], 长安大学, 2012.

CAO Hong. Traffic flow prediction based on BP neural network[D]. [Master dissertation], Changan University, 2012.

曹炜. 基于BP神经网络的城市交通流量预测与路口分析[D]. [硕士论文], 昆明理工大学, 2006.

CAO Wei. The city traffic flow prediction and cross analysis based on BP neural network[D]. [Master dissertation], Kunming University of Science and Technology, 2006.

尚宁, 覃明贵, 王亚琴, 等. 基于BP神经网络的路口短时交通流量预测方法[J]. 计算机应用与软件, 2006, 23(2): 32-33, 57.

SHANG Ning, QIN Minggui, WANG Yaqin, et al. A BP neural network method for short-term traffic flow forecasting on crossroads[J]. Computer Applications and Software, 2006, 23(2): 32-33, 57.

翟敏. 极限学习机的自适应网络结构选择方法研究[D]. [硕士论文], 西北大学, 2014.

ZHAI Min. Research on adaptive network structure selection method for extreme learning machine[D]. [Master dissertation], Northwest University, 2014.

甘露. 极限学习机的研究与应用[D]. [硕士论文], 西安电子科技大学, 2014.

GAN Lu. Research and application of extreme learning machine[D]. [Master dissertation], Xidian University, 2014.

王智慧. BP神经网络和ELM算法研究[D]. [硕士论文], 中国计量学院, 2012.

WANG Zhi-hui. Research on BP neural networks and ELM Algorithms[D]. [Master dissertation], China Jiliang University, 2012.

张一. 基于卡尔曼滤波的短时交通流量预测模型研究[D]. [硕士论文], 沈阳工业大学, 2014.

ZHANG Yi. Research of short-term traffic volume prediction based on kalman filtering[D]. [Master dissertation], Shenyang University of Technology, 2014.

罗向龙. 交通流量经验模态分解与神经网络短时预测方法[J]. 计算机工程与应用, 2010, 46(26): 212-214. doi: 10.3778/j.issn. 1002-8331.2010.26.066.

LUO Xianglong. Short-term traffic flow prediction method based on EMD and artificial neural network[J]. Computer Engineering and Applications, 2010, 46(26): 212-214. doi: 10.3778/j.issn.1002-8331.2010.26.066.

沈永增. 基于混沌粒子群优化小波神经网络的短时交通流预测[J]. 计算机应用与软件, 2014, 31(6): 84-87. doi: 10.3969 /j.issn.1000-386x.2014.06.024.

SHEN Yongzeng. Short-time traffic flow forecast based on WNN optimised by CPSO[J]. Computer Applications and Software, 2014, 31(6): 84-87. doi: 10.3969/j.issn.1000-386x. 2014.06.024.

朱征宇. 一种结合SVM与卡尔曼滤波的短时交通流预测模型[J]. 计算机科学, 2013, 40(10): 248-251.

ZHU Zhengyu. Short-term traffic flow forecasting model combining SVM and Kalman filtering[J]. Computer Science, 2013, 40(10): 248-251.

TCHRAKIAN T T, BASU B, and OMAHONY M. Real-time traffic flow forecasting using spectral analysis[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 519-526.

CHEN Syuanyi and CHOU Weiyao. Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach[C]. 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, 2012: 1821-1826.

CHAN K Y, DILLON T S, SINGH J, et al. Neural- network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg- Marquardt algorithm[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 644-654.

XU Yanyan, KONG Qingjie, and LIU Yuncai. Short-term traffic volume prediction using classification and regression trees[C]. IEEE Intelligent Vehicles Symposium(IV), Gold Coast, 2013: 493-498.

WILLIAMS B M. Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process[D]. [Ph.D. dissertation], University of Virginia, 1999: 243-246.

施引文献

资源附件(0)

访问统计