Constructing Popular Routes from Uncertain Trajectories Authors of Paper: Ling-Yin Wei (National Chiao Tung University, Hsinchu) Yu Zheng (Microsoft Research Asia) Wen-Chih Peng (National Chiao Tung University, Hsinchu) Paper reviewed by: Aniruddha

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Constructing Popular Routes from Uncertain
TrajectoriesAuthors of PaperLing-Yin
Wei(National Chiao Tung University, Hsinchu)Yu
Zheng(Microsoft Research Asia)Wen-Chih
Peng(National Chiao Tung University,
Hsinchu)Paper reviewed byAniruddha
Desai(University of Washington, Tacoma)
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Applications

• Scope Infer popular routes from a set of
  uncertain trajectories
• Trip Planning (Travel / Tourism)
• Traffic Management (Transportation)
• Animal Movement studies

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Spatial Trajectories

• What is a trajectory?
• Sequence of points Location (Latt, Long)
  Time-stamp
• What are the moving objects?
• Humans, Vehicles, Animals etc.
• How are the trajectories collected?
• Ubiquitous location acquisition technologies /
  devices using GPS

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Uncertainty and Inference

• Trajectories generated at low or irregular
  frequencies.
• Routes between consecutive points on trajectories
  are uncertain.
• To infer a popular route we need to find
  similarity between two uncertain trajectories
  this is hard to measure.

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RICK

• Route Inference framework based onCollective
  Knowledge
• Approach aggregate uncertain trajectories in a
  mutually reinforcing way uncertain uncertain
  gt certain

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Datasets

• Real datasets used for conducting extensive
  experiments
• Check-in dataset from Foursquare 6,600
  trajectories from Manhattan (3 check-ins min)
• 15,000 taxi trajectories in Beijing.

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How does it work?

• Rick Overview user specified query consists of
  a location sequence a time span RICK infers
  the top-k popular routes that pass through these
  locations within given time span

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Region Construction

• Historical uncertain trajectories used to
  construct a routable graph in a gridded space
  based on spatio-temporal characteristics
• Grid cell size (l) represents granularity of
  inferences
• Data points (or grid cells) spatially close
  if
• x - x lt 1 and y - y lt 1

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Region Construction (contd)

• Data points st-correlated (spatio-temporally
  correlated) if they are spatially close (Rule 1
  or Rule 2) and they mutually satisfy a temporal
  constraint q
• Connection support C is of a cell pair is a
  threshold for connectivity in the graph.
• Neighbor If the connection support of a cell
  pair is gt C then they are neighbors.

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Region Construction (contd)

• Region Based on the connection support (above a
  specified threshold value C) between individual
  cell pairs regions are constructed.
• Cell pairs are merged into regions using an
  efficient recursive algorithm Time complexity
  O(cnm2)
• Where c minimum loop iterationsn size
  (cardinality) of the set of cells in the grid
  spacem size (cardinality) of the dataset

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Edge Inference

• After the regions are constructed we infer edges.
• Two types of Edges
• Edges within each region
• Edges among regions

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Edge Inference (contd)

• Each vertex represents a cell and each edge
  indicates a transition relationship and has two
  attributes
• Transition support
• Travel time
• Virtual bidirected edges between cells (vertices)
  are generated if cells are neighbors in a region.
• Shortest path inference approach is used. The
  direction, transition supports and travel time
  information for edge on shortest path is stored.
• Redundant edges and edges whose transition
  support is 0 are eliminated

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Route Inference

• Two phases
• Route generation
• Route refinement
• Route generation
• Top-k coarse routes are discovered with the
  routable graph

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Route Inference (contd)

• If query location can not be mapped to a graph
  vertex we use MINDIST (nearest neighbor
  algorithm) to find the cells close to the query
  location.
• Local Routes the top-k local routes between any
  two consecutive cells are searched in the cell
  sequence by an A-like algorithm.
• Route score is computed based on the range of
  time interval between the two query locations.
• Based on top-k local routes top-k global routes
  are searched by a branch-and-bound search
  approach

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Route Inference (contd)

• Two-Layer Routing Algorithm
• Before searching for local routes region
  sequences are generated to reduce the search
  space by using a lower bound of the transition
  times between the regions with respect to two
  given cells.
• Thus, multiple region sequences are possible

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Route Inference (contd)

• Route Refinement
• Use historical data points (of trajectories that
  traverse the cells on the rough route) that
  locate in the cells on the route generated.
• Adopt linear regression for set of points of each
  cell to derive a line segment.
• Concatenate line segments in the order of the
  inferred route

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Performance Evaluation

• Inferred routes are compared against ground-truth
  from raw-trajectories.
• Two metrics used
• NDTW normalized dynamic time warping distance
• MD - maximum distance between inferred route and
  the raw-trajectory of the ground truth.
• Compared RICK with existing approach MPR (Most
  Popular Route) as a baseline
• Time Efficiency is tested (avg. query time 0.5
  secs).
• RICK outperforms the baseline by generating
  routes 300-700m closer to the ground-truth (than
  the those of the baseline).

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Visualization of Results

• Visualization of the query Central Park - gt The
  Museum of Modern Art - gt Times Square - gt Empire
  State Building - gt SoHo, for top-1 (most
  popular) route inferred by RICK

Note The route does not just connect the query
locations, but passes through other attractions
along the inferred most popular route.
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Strengths

• Thorough / Credible
• The authors have conducted extensive experiments
  on real data. Their results show that the route
  inference framework is effective, efficient and
  measurably accurate.
• Organized / Easy to understand
• The content of the paper is very well organized
  and can be easily understood even by a naïve
  reader.
• Illustrations (where provided) are very
  effective in describing spatial concepts.

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Weaknesses

• Connection Support Not explained sufficiently,
  diagrams would have been helpful explain key
  concept
• Route generated using A-like algorithm Not
  explained the role of A-like algorithm
  adequately in the context of inferred route
  generated.
• NDTW Normalized dynamic time warping distance
  is not explained adequately diagrams would have
  helped explain this key performance metric better.

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• Thank you!
• QA