International Conference on Transportation and Development 2020
Inferring Travel Modes from Trajectory Data Based on Hidden Markov Model
Publication: International Conference on Transportation and Development 2020
ABSTRACT
Identifying travel modes from massive information is critical for travel demand modeling and transportation planning. Along with widespread application of the highly accurate position devices, the need of cost-effective approaches is more urgent than ever before. In the field of movement ecology, it has been proved by experiments that Hidden Markov Model (HMM) is much more efficient than traditional statistical classification method, such as Bayesian methods. Accordingly, in this paper, the Hidden Markov Model is adopted to predict travel modes based on raw GPS trajectories, where the modes are labeled as walk, bike, bus, and driving. Besides, this study also endeavors to examine the inherent statistical characteristics of trajectories in different modes, and identify the relationship between residents’ choice of travel mode and the traffic flow structure along the route. The data analysis results are believed to gain a deeper insight into the impact of distance and route on residents’ choice of travel mode, and it is expected to provide ideas and references for the transportation planning of cities with completed transportation infrastructure construction.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
[1] Eluru, N., Chakour, V., El-Geneidy, A. M., 2012. Travel mode choice and transit route choice behavior in Montreal: insights from McGill University members commute patterns. Public Transp. 4, 129–149.
[2] Bilmes JA. What HMMs can do. IEICE TRANS- ACTIONS on Information and Systems, 2006, 89(3):1–24.
[3] Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y., 2008a. Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing. ACM, pp. 312–321.
[4] Endo, Y., Toda, H., Nishida, K., Kawanobe, A., 2016. Deep feature extraction from trajectories for transportation mode estimation. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp. 54–66.
[5] Xiao, Z., Wang, Y., Fu, K., Wu, F., 2017. Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS Int. J. Geo-Inf. 6, 57.
[6] Wu, L., Yang, B., Jing, P., 2016. Travel mode detection based on GPS raw data collected by smartphones: a systematic review of the existing methodologies. Information 7, 67.
[7] Zheng, Y., Liu, L., Wang, L., Xie, X., 2008b. Learning transportation mode from raw gps data for geographic applications on the web. In: Proceedings of the 17th International Conference on World Wide Web. ACM, pp. 247–256.
[8] Franke, A., Caelli, T. & Hudson, R. (2004) Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models. Ecological Modelling, 173, 259–270.
[9] Holzmann, H., Munk, A., Suster, M. & Zucchini, W. (2006) Hidden Markov models for circular and linear-circular time series. Environmental and Ecological Statistics, 13, 325–347.
[10] van de Kerk, M., Onorato, D., Criffield, M., Bolker, B., Augustine, B., McKinley, S.&Oli, M. (2015)Hidden semi-Markov models reveal multiphasic movement of the endangered Florida panther. Journal of Animal Ecology, 84, 576–585.
[11] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791–800.
[12] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312–321.
[13] Yu Zheng, Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp. 32-40.
[14] Zucchini, W., MacDonald, I. L., and Langrock, R. (2016). Hidden Markov models for time series: an introduction using R, Second Edition. Chapman and Hall/CRC.
[15] Th´eo Michelot, Roland Langrock & Toby Patterson (2019). moveHMM: An R package for the analysis of animal movement data. https://cran.r-project.org/web/packages/moveHMM/vignettes/moveHMM-guide.pdf.
DATA REFERENCE
Zheng, Y., Fu, H., Xie, X., Ma, W.-Y., Li, Q., August, 2012. Geolife GPS Trajectory Dataset. Microsoft Repository. Version 1.3.
Information & Authors
Information
Published In
International Conference on Transportation and Development 2020
Pages: 95 - 103
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8316-9
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Published in print: Aug 31, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.