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Reseach Article

Passenger Travel behavior Model in Railway Network Simulation

by Kulkarni Gauri Ramakant, B. M. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 2
Year of Publication: 2017
Authors: Kulkarni Gauri Ramakant, B. M. Patil
10.5120/ijca2017913477

Kulkarni Gauri Ramakant, B. M. Patil . Passenger Travel behavior Model in Railway Network Simulation. International Journal of Computer Applications. 163, 2 ( Apr 2017), 36-39. DOI=10.5120/ijca2017913477

@article{ 10.5120/ijca2017913477,
author = { Kulkarni Gauri Ramakant, B. M. Patil },
title = { Passenger Travel behavior Model in Railway Network Simulation },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 2 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 36-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number2/27371-2017913477/ },
doi = { 10.5120/ijca2017913477 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:06.928988+05:30
%A Kulkarni Gauri Ramakant
%A B. M. Patil
%T Passenger Travel behavior Model in Railway Network Simulation
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 2
%P 36-39
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

People usually travel to the same destination and same purpose together with the other people in groups.Inferring the travel purpose of passenger groups help us to better understand passengers and bring meaningful changes for personalized travel service.First we construct cotravel network by extracting social relations between passengers from their historical travel records that are available in passenger information system.We generate series of sophisticated features for each passenger group and use the overlapping relation between passenger groups to capture relations.At last we collectively infer the labels of all the groups in iterative way.

References
  1. R. Nicole, J. Carlson, and P. J. Rosenberger, III, “Factors affecting group-oriented travel intention to major events,” J. Travel Tourism Marketing, vol. 29, no. 2, pp. 185–204, 2012.
  2. L. Wu, J. Zhang, and A. Fujiwara, “Representing tourists heterogeneous choices of destination and travel party with an integrated latent class and nested logit model,” Tourism Manage., vol. 32, no. 6, pp. 1407–1413, Dec. 2011.
  3. R. March and A. G. Woodside, “Testing theory of planned versus real-ized tourism behavior,” Ann. Tourism Res., vol. 32, no. 4, pp. 905–924, Oct. 2005.
  4. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, “Measurement and analysis of online social networks,” in Proc. 7th IMC, 2007, pp. 29–42.
  5. S. Wuchty and B. Uzzi, “Human communication dynamics in digital footsteps: A study of the agreement between self-reported ties and email networks,” PLoS One, vol. 6, no. 11, p. e26972, Nov. 2011.
  6. D. Jensen, J. Neville, and B. Gallagher, “Why collective infer-ence improves relational classification,” in Proc. 10th SIGKDD, 2004.
  7. R. Xiang and J. Neville, “Collective inference for network data with copula latent Markov networks,” in Proc. 6th WSDM, 2013, pp. 647–656.
  8. D. Jensen and J. Neville, “Linkage and autocorrelation cause fea-ture selection bias in relational learning,” in Proc. 19th ICML, 2002.
  9. Youfang Lin, Huaiyu Wan, Rui Jiang, Zhihao Wu,Xuguang Jia.”Inferring the travelling purpose of passenger groups for better understanding of passengers”.
  10. S. Pike and C. Ryan, “Destination positioning analysis through a compar-ison of cognitive, affective, conative perceptions,” J. Travel Res., vol. 42, no. 4, pp. 333–342, May 2004.
  11. . Tang et al., “ArnetMiner: Extraction and mining of academic social networks,” in Proc. 14th SIGKDD, 2008, pp. 990–998.
Index Terms

Computer Science
Information Sciences

Keywords

Collective inference cotravel networks iterative classification.