[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(
)
支持向量机在出行链模式识别和影响因素分析中的应用
)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
- 支持向量机在出行链模式识别和影响因素分析中的应用
- 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.
- 为了提高交通需求预测精度,综合考虑居民出行行为在时间维度上的分布,采用支持向量机、径向基神经网络和多项logit三种方法,基于居民活动目的,建立了出行链模式识别模型,并利用敏感性分析方法研究了解释因素对出行链模式选择的影响和对模型性能的贡献程度.结果显示:支持向量机模型在总体准确度和分类准确度上均优于其他2种方法,体现了支持向量机在小样本下的识别性能优势;证明了支持向量机能够较准确地反映多分类因素对于出行链模式选择行为的影响程度;因素对于不同出行链模式识别精度的贡献度差异表明了细化出行链模式及探索各个模式特有影响因素的重要性.支持向量机技术在交通需求预测建模及影响因素分析方面均具有实践意义.
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.