NDLI: Density Peaks Clustering Approach for Discovering Demand Hot Spots in City-scale Taxi Fleet Dataset

Content Provider	IEEE Xplore Digital Library
Author	Dongchang Liu Shih-Fen Cheng Yiping Yang
Copyright Year	2015
Description	Author affiliation: Inst. of Autom., Beijing, China (Dongchang Liu; Yiping Yang) \|\| Sch. of Inf. Syst., Singapore Manage. Univ., Singapore, Singapore (Shih-Fen Cheng)
Abstract	In this paper, we introduce a variant of the density peaks clustering (DPC) approach for discovering demand hot spots from a low-frequency, low-quality taxi fleet operational dataset. From the literature, the DPC approach mainly uses density peaks as features to discover potential cluster centers, and this requires distances between all pairs of data points to be calculated. This implies that the DPC approach can only be applied to cases with relatively small numbers of data points. For the domain of urban taxi operations that we are interested in, we could have millions of demand points per day, and calculating all-pair distances between all demand points would be practically impossible, thus making DPC approach not applicable. To address this issue, we project all points to a density image and execute our variant of the DPC algorithm on the processed image. Experiment results show that our proposed DPC variant could get similar results as original DPC, yet with much shorter execution time and lower memory consumption. By running our DPC variant on a real-world dataset collected in Singapore, we show that there are indeed recurrent demand hot spots within the central business district that are not covered by the current taxi stand design. Our approach could be of use to both taxi fleet operator and traffic planners in guiding drivers and setting up taxi stands.
Starting Page	1831
Ending Page	1836
File Size	853221
Page Count	6
File Format	PDF
ISSN	21530017
e-ISBN	9781467365963
DOI	10.1109/ITSC.2015.297
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Public transportation Vehicles Algorithm design and analysis Memory management Lattices Business
Content Type	Text
Resource Type	Article

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