NDLI: Inferring human mobility patterns from taxicab location traces

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Inferring human mobility patterns from taxicab location traces

Content Provider	ACM Digital Library
Author	Ranganathan, Anand Srivatsa, Mudhakar Ganti, Raghu Han, Jiawei
Abstract	Taxicabs equipped with real-time location sensing devices are increasingly becoming popular. Such location traces are a rich source of information and can be used for congestion pricing, taxicab placement, and improved city planning. An important problem to enable these application is to identify human mobility patterns from the taxicab traces, which translates to being able to identify pickup and dropoff points for a particular trip. In this paper, we show that while past approaches are effective in detecting hotspots using location traces, they are largely ineffective in identifying trips (pairs of pickup and dropoff points). We propose the use of a graph theory concept - stretch factor in a novel manner to identify trip(s) made by a taxicab and show that a Hidden Markov Model based algorithm can identify trips (using real datasets from taxicab deployments in Shanghai and partially simulated datasets from Stockholm) with precision and recall of 90-94%, a significant improvement over past approaches that result in a precision and recall of about 50-60%.
Starting Page	459
Ending Page	468
Page Count	10
File Format	PDF
ISBN	9781450317702
DOI	10.1145/2493432.2493466
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-09-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hidden markov models Human mobility patterns Trajectory analysis Taxi cab occupancy
Content Type	Text
Resource Type	Article

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