|Table of Contents|

[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]
Copy

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

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 ShuoDeng WeiCheng 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:

[1] Xianyu J C. An exploration of the interdependencies between trip chaining behavior and travel mode choice [J]. Procedia-Social and Behavioural Sciences, 2013, 96: 1967-1975. DOI:10.1016/j.sbspro.2013.08.222.
[2] Li Z B, Wang W, Yang C, et al. Exploring the causal relationship between bicycle choice and trip chain pattern [J]. Transport Policy, 2013. 29: 170-177. DOI:10.1016/j.tranpol.2013.06.001.
[3] Lee Y, Hickman M, Washington S. Household type and structure, time-use pattern, and trip-chaining behaviour [J]. Transportation Research Part A: Policy and Practice, 2007, 41(10): 1004-1020. DOI:10.1016/j.tra.2007.06.007.
[4] Wang R. The stops made by commuters: Evidence from the 2009 US National Household Travel Survey [J]. Journal of Tranport Geography, 2015, 47: 109-118. DOI:10.1016/j.jtrangeo.2014.11.005.
[5] Ma J, Mitchell G, Heppenstall A. Daily travel behavior in Beijing, China: An analysis of workers trip chains, and the role of socio-demographics and urban form [J]. Habitat International, 2014, 43: 263-273. DOI:10.1016/j.habitatint.2014.04.008.
[6] Lee Y, Washington S, Frank L D. Examination of relationships between urban form, household activities, and time allocation in the Atlanta metropolitan region [J]. Transportation Research Part A: Policy and Practice, 2009, 43(4): 360-373. DOI:10.1016/j.tra.2008.11.013.
[7] Frank L, Bradley M, Kavage S, et al. Urban form, travel time, and cost relationships with tour complexity and mode choice [J]. Transportation, 2008, 35(1): 37-54. DOI:10.1007/s11116-007-9136-6.
[8] Maat K, Wee B V, Stead D. Land use and travel behavior: Expected effects from the perspective of utility theory and activity-based theories [J]. Environment and Planning B: Planning and Design, 2005, 32(1): 33-46. DOI:10.1068/b31106.
[9] Shiftan Y. The use of activity-based modelling to analyse the effect of land-use policies on travel bahavior [J]. The Annals of Regional Science, 2008, 42(1): 79-97. DOI:10.1007/s00168-007-0139-1.
[10] Li Z B, Liu P, Wang W, et al. Using support vector machine models for crash injury severity analysis [J]. Accident Analysis and Prevention, 2012, 45: 478-486. DOI:10.1016/j.aap.2011.08.016.
[11] Xianyu J C. Travel mode choice analysis using support vector machine [C]//11th International Conference of Chinese Transportation Professionals (ICCTP).Nanjing, China, 2011: 14-17.
[12] Chang C C, Lin C J. LIBSVM: A library for support vector machines [EB/OL].(2013-03-04)[2015-01-27] https://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf.
[13] Powers D M W. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation [J]. Journal of Machine Learning Technologies, 2011, 2(1): 37-63.
[14] Cortez P, Embrechts M J. Using sensitivity analysis and visualization techniques to open black box data mining models [J]. Information Sciences, 2013, 225: 1-17. DOI:10.1016/j.ins.2012.10.039.

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.
Last Update: 2017-03-20