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Anuradha Purohit, PVS Anil Kumar
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- Professor, Student, Department of Computer Engineering, Shri G.S. Institute of Technology and Science, M.E. Computer Engineering, Shri G.S. Institute of Technology and Science, Madhya Pradesh, India
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Abstract
nMost 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 the travel patterns include 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.
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Keywords: HDBSCAN, DBSCAN, Apriori, Pattern Mining, Global Positioning System (GPS)
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References
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International Journal of Algorithms Design and Analysis Review
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| Volume | 01 |
| Issue | 02 |
| Received | July 20, 2023 |
| Accepted | September 20, 2023 |
| Published | October 25, 2023 |
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