Issue

Vol 3, No 4, November 2018

DBscan Algorithm and Decision Tree to Automate Trip Purpose Detection

https://doi.org/10.22219/kinetik.v3i4.672
Lailatul Hidayah
Universitas Internasional Semen Indonesia
Catur Wulandari
Universitas Internasional Semen Indonesia

Corresponding Author(s) : Lailatul Hidayah

lailatul.hidayah@uisi.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol 3, No 4, November 2018

Abstract

One of transportation research topic is detecting trip purpose. Given a collection of GPS mobility records, researchers endeavored to infer useful information such as trip, travel mode, and trip purpose. Obtaining these attributes will help researcher in transportation modelling.  This work proposed an approach in defining a trip or a trip segmentation which is a part of trip purpose problem as well as inferring the trip purpose. By Utilizing Dbscan clustering algorithm, decision tree, and some useful features, we are able to detect the trips and their purposes as well as building the model to automate the trip derivation.

Keywords

Trip purpose dbscan algorithm decision tree transportation modelling travel behavior
Hidayah, L., & Wulandari, C. (2018). DBscan Algorithm and Decision Tree to Automate Trip Purpose Detection. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 3(4), 305-310. https://doi.org/10.22219/kinetik.v3i4.672
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. J. Wolf, “Using GPS Data Loggers To Replace Travel Diaries In the Collection of Travel Data by,” Analysis, no. July, p. 225, 2000.
  2. [P. Stopher, C. FitzGerald, and J. Zhang, “Search for a global positioning system device to measure person travel,” Transp. Res. Part C Emerg. Technol., vol. 16, no. 3, pp. 350–369, 2008.
  3. P. R. Stopher, “Use of an activity-based diary to collect household travel data,” Transportation (Amst)., vol. 19, no. 2, pp. 159–176, 1992.
  4. P. McGowen and M. McNally, “Evaluating the Potential To Predict Activity Types from GPS and GIS Data,” in Transportation Research Board 86th Annual Meeting, 2007.
  5. M. Zimowski, R. Tourangeau, R. Ghadialy, and S. Pedlow, “Nonresponse in household travel surveys,” Rep. to Fed. Highw. Adm., 1997.
  6. L. Wu, B. Yang, and P. Jing, “Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies,” 2016.
  7. P. Wang, Y. Fu, G. Liu, W. Hu, and C. Aggarwal, “Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes,” Proc. 23rd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. - KDD ’17, pp. 495–503, 2017.
  8. T. Griffin and P. O. B. Denton, “A Decision Tree Classification Model to Automate Trip Purpose Derivation,” Proc. CAINE, pp. 44–49, 2005.
  9. J. Wolf, R. Guensler, and W. Bachman, “Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data,” in Transportation Research Record: Journal of the Transportation Research Board, 2001, vol. 1768, pp. 125–134.
  10. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” in Kdd, 1996, vol. 96, no. 34, pp. 226–231.
  11. L. Breiman, “Classification and regression tree,” pp. 1–33, 2001.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 402 Download : 325

Downloads

Download data is not yet available.