[1]王栋. 基于BP神经网络的公路客运量预测方法[J].计算机技术与发展,2017,27(02):187-190.
　WANG Dong. Prediction Method of Highway Passenger Transportation Volume Based on BP Neural Network[J].,2017,27(02):187-190.
点击复制

 基于BP神经网络的公路客运量预测方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年02期
页码:
187-190
栏目:
应用开发研究
出版日期:
2017-02-10

文章信息/Info

Title:
 Prediction Method of Highway Passenger Transportation Volume Based on BP Neural Network
文章编号:
1673-629X（2017)02-0187-04
作者:
 王栋
 西安航空学院 车辆工程学院
Author(s):
 WANG Dong
关键词:
 ; 灰色关联分析; BP神经网络; 公路客运量; 预测
Keywords:
 ; grey relational analysis; BP neural network; highway passenger transportation; forecasting
分类号:
U491.1
文献标志码:
A
摘要:
 公路客运量是交通科学管理的基础性数据资料,能够反映出公路运输产出成果,对提高公路交通管理层次及建立畅通、高效的公路交通系统,具有重要意义.为提高公路客运量的预测精度,选择与公路客运量相关的主要社会指标(包括公路客运量、汽车保有量、国民总收入、人均GDP、人口总量、城镇居民人均可支配收入、社会消费品零售总额和城市化率),运用灰色关联分析法进行计算分析,最终确定公路客运量影响因子为汽车保有量、人均GDP、人口总量和城市化率.将所确定的因子作为公路客运量的预测指标,建立基于BP神经网络的公路客运量预测模型,并对模型进行了应用测试.结果表明:BP神经网络模型具有较高的精度,最小相对误差为1.1％,平均相对误差为2.78％.
Abstract:
 Highway passenger transportation volume is basic data of traffic scientific management and can reflect the results of highway transportation,which is of great significance to improve the road traffic management level and establish a smooth and efficient highway traffic system.In order to improve the forecasting accuracy of highway passenger transportation,the gray correlation method is used to compute and analyze.The main predictors are car ownership,per capita GDP,total population number and urbanization.The prediction model of highway passenger transportation is establish based on BP neural network,and then verified with tests.The results show that highway passenger transportation can be predicted accurately by the model based on BP neural network.The minimum relative error is 1.1％ and the average relative error is 2.78％.

相似文献/References:

[1]李玲娟 孔凡龙.基于灰色理论的层次化网络安全态势评估方法[J].计算机技术与发展,2010,(08):163.
　LI Ling-juan,KONG Fan-long.A Hierarchical Network Security Situation Evaluation Method Based on Grey Theory[J].,2010,(02):163.
[2]何志明 马苗.基于灰色关联分析和人工蜂群算法的图像匹配方法[J].计算机技术与发展,2010,(10):78.
　HE Zhi-ming,MA Miao.Fast Image Matching Approach Based on Grey Relational Analysis and Artificial Bee Colony Algorithm[J].,2010,(02):78.
[3]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(02):1.
[4]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(02):5.
[5]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(02):13.
[6]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(02):21.
[7]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(02):25.
[8]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(02):29.
[9]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(02):34.
[10]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(02):38.
[11]王智刚,马苗. 基于灰色关联分析的改进GAC模型轮廓检测方法[J].计算机技术与发展,2015,25(01):70.
　WANG Zhi-gang,MA Miao. An Improved Contour Detection Method of GAC Model Based on Grey Relational Analysis[J].,2015,25(02):70.

更新日期/Last Update: 2017-05-11