NDLI: Knowledge-based trajectory completion from sparse GPS samples

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Knowledge-based trajectory completion from sparse GPS samples

Content Provider	ACM Digital Library
Author	Li, Yangyan Li, Yang Gunopulos, Dimitrios Guibas, Leonidas
Abstract	Traffic trajectories collected from GPS-enabled mobile devices or vehicles are widely used in urban planning, traffic management, and location based services. Their performance often relies on having dense trajectories. However, due to the power and bandwidth limitation on these devices, collecting dense trajectory is too costly on a large scale. We show that by exploiting structural regularity in large trajectory data, the complete geometry of trajectories can be inferred from sparse GPS samples without information about the underlying road network - a process called trajectory completion. In this paper, we present a knowledge-based approach for completing traffic trajectories. Our method extracts a network of road junctions and estimates traffic flows across junctions. GPS samples within each flow cluster are then used to achieve fine-level completion of individual trajectories. Finally, we demonstrate that our method is effective for trajectory completion on both synthesized and real traffic trajectories. On average 72.7% of real trajectories with sampling rate of 60 seconds/sample are completed without map information. Comparing to map matching, over 89% of points on completed trajectories are within 15 meters from the map matched path.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2996924
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trajectory skeleton Trajectory completion Junction network
Content Type	Text
Resource Type	Article

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