19,463 research outputs found
A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data
The increased availability of large-scale trajectory data around the world
provides rich information for the study of urban dynamics. For example, New
York City Taxi Limousine Commission regularly releases source-destination
information about trips in the taxis they regulate. Taxi data provide
information about traffic patterns, and thus enable the study of urban flow --
what will traffic between two locations look like at a certain date and time in
the future? Existing big data methods try to outdo each other in terms of
complexity and algorithmic sophistication. In the spirit of "big data beats
algorithms", we present a very simple baseline which outperforms
state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs
permit large scale experimentation). Such a travel time estimation baseline has
several important uses, such as navigation (fast travel time estimates can
serve as approximate heuristics for A search variants for path finding) and
trip planning (which uses operating hours for popular destinations along with
travel time estimates to create an itinerary).Comment: 12 page
Lost in Time: Temporal Analytics for Long-Term Video Surveillance
Video surveillance is a well researched area of study with substantial work
done in the aspects of object detection, tracking and behavior analysis. With
the abundance of video data captured over a long period of time, we can
understand patterns in human behavior and scene dynamics through data-driven
temporal analytics. In this work, we propose two schemes to perform descriptive
and predictive analytics on long-term video surveillance data. We generate
heatmap and footmap visualizations to describe spatially pooled trajectory
patterns with respect to time and location. We also present two approaches for
anomaly prediction at the day-level granularity: a trajectory-based statistical
approach, and a time-series based approach. Experimentation with one year data
from a single camera demonstrates the ability to uncover interesting insights
about the scene and to predict anomalies reasonably well.Comment: To Appear in Springer LNE
PRESS: A Novel Framework of Trajectory Compression in Road Networks
Location data becomes more and more important. In this paper, we focus on the
trajectory data, and propose a new framework, namely PRESS (Paralleled
Road-Network-Based Trajectory Compression), to effectively compress trajectory
data under road network constraints. Different from existing work, PRESS
proposes a novel representation for trajectories to separate the spatial
representation of a trajectory from the temporal representation, and proposes a
Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal
Compression (BTC) algorithm to compress the spatial and temporal information of
trajectories respectively. PRESS also supports common spatial-temporal queries
without fully decompressing the data. Through an extensive experimental study
on real trajectory dataset, PRESS significantly outperforms existing approaches
in terms of saving storage cost of trajectory data with bounded errors.Comment: 27 pages, 17 figure
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