An Approach for Travel Pattern Analysis Using HDBSCAN and Apriori Algorithms

Year : 2023 | Volume : 01 | Issue : 02 | Page : 1-9
By

    P.V.S. Anil Kumar

  1. Anuradha Purohit

  1. Student, Department of Computer Engineering, Shri G.S. Institute of Technology and Science, Indore, Madhya Pradesh, India
  2. Professor, Department of Computer Engineering, Shri G.S. Institute of Technology and Science, Madhya Pradesh, India

Abstract

Most mega-city regions around the world are suffering from an ongoing increase in the number of commuting trips. Understanding commuting patterns is crucial for both public and authority planners. The understanding of travel patterns helps passengers to know about the places and the time where they could get vacant transport and also helps authority planners in laying out a new transport service. The traditional way of understanding travel patterns includes conducting surveys, which is time consuming process. Therefore, authority planners are exploring another way of acquiring knowledge about the travel patterns within less time and effort. Using the concept of clustering and sequential pattern mining helps in understanding the travel patterns by generating clusters and frequent patterns. In this paper, a new approach for trajectory pattern analysis using hierarchical density–based clustering algorithm and Apriori sequential pattern mining is proposed. The use of a hierarchical density–based clustering (HDBSCAN) algorithm instead of traditional density-based clustering (DBSCAN) algorithm gave better results in terms of quality of clusters. Performing clustering on an hourly basis helped in getting deeper insights of the travel patterns. We utilized the Porto taxi trajectory dataset from the UCI Machine Learning Repository for our experimentation.

Keywords: hierarchical density–based clustering (HDBSCAN), density-based clustering (DBSCAN), Apriori, pattern mining, global positioning system (GPS)

[This article belongs to International Journal of Algorithms Design and Analysis Review(ijadar)]

How to cite this article: P.V.S. Anil Kumar, Anuradha Purohit An Approach for Travel Pattern Analysis Using HDBSCAN and Apriori Algorithms ijadar 2023; 01:1-9
How to cite this URL: P.V.S. Anil Kumar, Anuradha Purohit An Approach for Travel Pattern Analysis Using HDBSCAN and Apriori Algorithms ijadar 2023 {cited 2023 Oct 25};01:1-9. Available from: https://journals.stmjournals.com/ijadar/article=2023/view=126896

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References

Zheng Y. Trajectory data mining: an overview. ACM Trans Intell Syst Technol. 2015; 6 (3): 1–41.
Lin M, Hsu WJ. Mining GPS data for mobility patterns: a survey. Pervasive Mobile Comput. 2014; 12: 1–6.
Zhong RY, Huang GQ, Lan S, Dai QY, Chen X, Zhang T. A big data approach for logistics trajectory discovery from RFID-enabled production data. Int J Prod Econ. 2015; 165: 260–272.
Zheng Y, Capra L, Wolfson O, Yang H. Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol. 2014; 5 (3): 1–55.
Dang S, Ahmad PH. Text mining: techniques and its application. Int J Eng Technol Innov. 2014; 1 (4): 22–25.
Campello RJ, Moulavi D, Sander J. Density-based clustering based on hierarchical density estimates. In: Pei J, Tseng VS, Cao L, Motoda H, Xu G, editors. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin, Germany: Springer; 2013. pp. 160–172.
Agrawal R, Mehta M, Shafer JC, Srikant R, Arning A, Bollinger T. The Quest data mining system. In: Simoudis E, Han J, Fayyad UM, editors. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press; 1996. pp. 244–249.
Wei YQ, Yang RH, Liu PY. An improved Apriori algorithm for association rules of mining. In: 2009 IEEE International Symposium on IT in Medicine & Education, Jinan, China, August 14–16, 2009. pp. 942–946.
Palma AT, Bogorny V, Kuijpers B, Alvares LO. A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM Symposium on Applied Computing, Fortaleza, Ceara Brazil, March 16–20, 2008. pp. 863–868.
Bermingham L, Lee I. Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Sci. 2014; 29: 379–389.
Bhaskar A, Chung E. Passenger segmentation using smart card data. IEEE Trans Intell Transport Syst. 2014; 16 (3): 1537–1548.
Shen Y, Zhao L, Fan J. Analysis and visualization for hot spot based route recommendation using short-dated taxi GPS traces. Information. 2015; 6 (2): 134–151.
Saptawati Spatio-temporal mining to identify potential traffic congestion based on transportation mode. In: 2017 International Conference on Data and Software Engineering (ICoDSE), Palembang, Indonesia, November 1–2, 2017. pp. 1–6.
Mao F, Ji M, Liu T. Mining spatiotemporal patterns of urban dwellers from taxi trajectory data. Front Earth Sci. 2016; 10: 205–221.
Qiu G, Song R, He S, Xu W, Jiang M. Clustering passenger trip data for the potential passenger investigation and line design of customized commuter bus. IEEE Trans Intell Transport Syst. 2018; 20 (9): 3351–3360.
Zhao L, Shi G, Yang J. An adaptive hierarchical clustering method for ship trajectory data based on DBSCAN algorithm. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, China, March 10–12, 2017. pp. 329–336.
Kieu LM, Bhaskar A, Chung E. A modified density-based scanning algorithm with noise for spatial travel pattern analysis from smart card AFC data. Transport Res Part C Emerg Technol. 2015; 58: 193–207.
Frank A. UCI Machine Learning Repository. [Online]. 2010. Available at http://archive.ics.uci.edu/ml
Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987; 20: 53–65.


Regular Issue Subscription Review Article
Volume 01
Issue 02
Received July 20, 2023
Accepted September 20, 2023
Published October 25, 2023