基于小波神经网络的城市交通出行方式交互预测
|
摘要
解决城市交通问题的关键在于优化调整交通出行结构以实现交通供需平衡,准确的进行交通需求分析是平衡供需矛盾的基础。本文通过分析城市经济结构发展与交通方式选择的相互关系,从横向和纵向两个层面提取交通方式选择的关键因素,构建描述城市交通出行方式选择行为的小波神经网络预测模型,利用关键因素之间的交互关系实现对出行方式的预测。在实际算例中,以北京市居民出行数据进行模型参数标定,验证了利用影响出行方式的关键因素及交互关系建立小波神经网络预测方法的可行性和精度。
The balance between traffic demand and supply derived from optimizing traffic modal split is crucial to tackle urban traffic problems.To strike such a balance,traffic demand analysis plays an important role.Aiming at predicting travel modal split in urban traffic system,the interaction between urban economic structure and travel characteristics was analyzed and the key factors in such interaction were extracted from the horizontal and longitudinal dimensions respectively.With the interaction and the influential factors,taken into account a prediction model based on wavelet neural networks was proposed.The new model was calibrated and validated by using empirical data from the travel survey of Beijing city.The results show that the wavelet neural networks can give a reliable prediction of traffic modal split.3tabs,4figs,10 refs.
The balance between traffic demand and supply derived from optimizing traffic modal split is crucial to tackle urban traffic problems.To strike such a balance,traffic demand analysis plays an important role.Aiming at predicting travel modal split in urban traffic system,the interaction between urban economic structure and travel characteristics was analyzed and the key factors in such interaction were extracted from the horizontal and longitudinal dimensions respectively.With the interaction and the influential factors,taken into account a prediction model based on wavelet neural networks was proposed.The new model was calibrated and validated by using empirical data from the travel survey of Beijing city.The results show that the wavelet neural networks can give a reliable prediction of traffic modal split.3tabs,4figs,10 refs.
引文
[1]Scheiner J.Interrelations between travel mode choice and trip distance:trends in Germany 1976-2002[J].Journal of Transport Geography,2010,18(1):75-84.
[2]Friedrichsmeier T,Matthies E,Klockner C A.Explaining stability in travel mode choice:an empirical comparison of two concepts of habit[J].Transportation Research Part F:Traffic Psychology and Behaviour,2013,16(1):1-13.
[3]贾洪飞,孙宝凤,王苏娟,等.基于计划行为理论的换乘决策机理解析[J].吉林大学学报:工学版,2012,42(6):1481-1486.JIA Hong-fei,SUN Bao-feng,WANG Su-juan,et al.Resolution of transfer decision-making mechanism based on the theory of planned behavior[J].Journal of Jilin University:Engineering and Technology Edition,2012,42(6):1481-1486.
[4]文婧,王星,连欣.北京市居民通勤特征研究:基于千余份问卷调查的分析[J].人文地理,2012,27(5):62-68.WEN Jing,WANG Xing,LIAN Xin.A study on commuting characteristics in Beijing:based on over1000questionnaires[J].Human Geography,2012,27(5):62-68.
[5]贾俊刚,杨东援.基于概率神经网络的交通方式划分模型[J].上海理工大学学报,1999,21(1):9-12.JIA Jun-gang,YANG Dong-yuan.Traffic mode split model based on probabilistic neural network[J].Journal of University of Shanghai for Science and Technology,1999,21(1):9-12.
[6]杜豫川,蒋盛川,朱迪,等.上海世博会出行方式选择意愿建模与场景分析[J].同济大学学报:自然科学版,2010,38(4):515-520.DU Yu-chuan,JIANG Sheng-chuan,ZHU Di,et al.Discrete choice model and scenario analysis on trip mode choice behavior for expo 2010 Shanghai[J].Journal of Tongji University:Natural Science,2010,38(4):515-520.
[7]武文,帅斌,殷焕焕,等.基于BL模型的居民小汽车出行行为研究[J].交通运输工程与信息学报,2012,10(3):71-75.WU Wen,SHUAI Bin,YIN Huan-huan,et al.Study of travel behavior by car based on BL model[J].Journal of Transportation Engineering and Information,2012,10(3):71-75.
[8]Ben-Akiva M,Lerman S.Theory and application to discrete choice analysis:travel demand[M].Cambridge:MIT Press,1985.
[9]郑治真.小波变换及其MATLAB工具的使用[M].北京:地震出版社,2001.ZHENG Zhi-zhen.Wavelets applications in matlab[M].Beijing:Seismological Press,2001.
[10]张德丰.MATALB神经网络应用设计[M].北京:机械工业出版社,2009ZHANG De-feng.Artificial neural network using matlab[M].Beijing:China Machine Press,2009.
[2]Friedrichsmeier T,Matthies E,Klockner C A.Explaining stability in travel mode choice:an empirical comparison of two concepts of habit[J].Transportation Research Part F:Traffic Psychology and Behaviour,2013,16(1):1-13.
[3]贾洪飞,孙宝凤,王苏娟,等.基于计划行为理论的换乘决策机理解析[J].吉林大学学报:工学版,2012,42(6):1481-1486.JIA Hong-fei,SUN Bao-feng,WANG Su-juan,et al.Resolution of transfer decision-making mechanism based on the theory of planned behavior[J].Journal of Jilin University:Engineering and Technology Edition,2012,42(6):1481-1486.
[4]文婧,王星,连欣.北京市居民通勤特征研究:基于千余份问卷调查的分析[J].人文地理,2012,27(5):62-68.WEN Jing,WANG Xing,LIAN Xin.A study on commuting characteristics in Beijing:based on over1000questionnaires[J].Human Geography,2012,27(5):62-68.
[5]贾俊刚,杨东援.基于概率神经网络的交通方式划分模型[J].上海理工大学学报,1999,21(1):9-12.JIA Jun-gang,YANG Dong-yuan.Traffic mode split model based on probabilistic neural network[J].Journal of University of Shanghai for Science and Technology,1999,21(1):9-12.
[6]杜豫川,蒋盛川,朱迪,等.上海世博会出行方式选择意愿建模与场景分析[J].同济大学学报:自然科学版,2010,38(4):515-520.DU Yu-chuan,JIANG Sheng-chuan,ZHU Di,et al.Discrete choice model and scenario analysis on trip mode choice behavior for expo 2010 Shanghai[J].Journal of Tongji University:Natural Science,2010,38(4):515-520.
[7]武文,帅斌,殷焕焕,等.基于BL模型的居民小汽车出行行为研究[J].交通运输工程与信息学报,2012,10(3):71-75.WU Wen,SHUAI Bin,YIN Huan-huan,et al.Study of travel behavior by car based on BL model[J].Journal of Transportation Engineering and Information,2012,10(3):71-75.
[8]Ben-Akiva M,Lerman S.Theory and application to discrete choice analysis:travel demand[M].Cambridge:MIT Press,1985.
[9]郑治真.小波变换及其MATLAB工具的使用[M].北京:地震出版社,2001.ZHENG Zhi-zhen.Wavelets applications in matlab[M].Beijing:Seismological Press,2001.
[10]张德丰.MATALB神经网络应用设计[M].北京:机械工业出版社,2009ZHANG De-feng.Artificial neural network using matlab[M].Beijing:China Machine Press,2009.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心