[1] Yang Shuo, Deng Wei, Cheng Long,. Application of support vector machine in trip chaining patternrecognition and analysis of explanatory variable effects [J]. Journal of Southeast University (English Edition), 2017, 33 (1): 106-114. [doi:10.3969/j.issn.1003-7985.2017.01.018]

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Application of support vector machine in trip chaining patternrecognition and analysis of explanatory variable effects()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

33

Issue:

2017 1

Page:

106-114

Research Field:

Traffic and Transportation Engineering

Publishing date:

2017-03-30

Info

Title:

Application of support vector machine in trip chaining patternrecognition and analysis of explanatory variable effects

Author(s):

Yang Shuo; Deng Wei; Cheng Long

School of Transportation, Southeast University, Nanjing 210096, China

Keywords:

trip chaining patterns;  support vector machine;  recognition performance;  sensitivity analysis

PACS:

U121

DOI:

10.3969/j.issn.1003-7985.2017.01.018

Abstract:

In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods: the support vector machine(SVM)model, the radial basis function neural network(RBFNN)model and the multinomial logit(MNL)model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performance were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a small sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns and exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.

References:

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Memo

Memo:

Biographies: Yang Shuo(1988—), male, graduate; Deng Wei(corresponding author), male, doctor, professor, dengwei@seu.edu.cn.
Foundation items: The Fundamental Research Funds for the Central Universities, the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX_0177).
Citation: Yang Shuo, Deng Wei, Cheng Long. Application of support vector machine in trip chaining pattern recognition and analysis of explanatory variable effects[J].Journal of Southeast University(English Edition),2017,33(1):106-114.DOI:10.3969/j.issn.1003-7985.2017.01.018.

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