Knowledge Discovery from Transportation Network Data

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Knowledge Discovery from Transportation Network
Data

• Paper Review
• Jiang, W., Vaidya, J., Balaporia, Z.,
  Clifton, C., and Banich, B. Knowledge Discovery
  from Transportation Network Data. In ICDE, 2005

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Outline

• Background.
• Experiments.
• Structurally Similar Routes
• Temporally Repeated Routes
• Experiment results.
• Conventional techniques.
• New challenges.

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A natural application area for Data Mining

• Transportation and logistics are an important
  sector of the economy.
• --Transportation consumes 60 of oil
  worldwide
• Data mining has lead to significant gains in
  other areas
• Computer use is widespread in transportation and
  logistics.
• --Inventory management, parcel tracking,
  and even on-truck location sensors

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Existing Applications

• Data Mining
• Mining with transactional characteristics of
  freight and events.
• -- i.e. classification on
  safety/accident records might find that trucks
  are prone to accidents at 700 AM on east - west
  roads.
• -- NO geometry of the network.
• Network Structure
• Optimization
• -- Finds solution (Minimize cost)

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Transportation Networks

• Graph problems
• Graph mining
• i.e. Finding the frequent sub-graphs
• Algorithms
• WARMR
• AGM
• SUBDUE
• FSG

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Dataset

• Six months of origin-destination (OD) data from a
  large third-party logistic company. 98,292
  transactions.
• Represented as a directed graph by mapping
  locations to vertices.
• Each transaction can then be represented as the
  edge of an OD pair.
• The edges are labeled with the other attributes
  of the transaction pickup date, delivery date,
  distance, hours, weight, and mode. (binning
  strategy)

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Mining Interests

• Structurally Similar Routes
• --Identify structurally similar patterns that
  occur in many locations.
• Methods SUBDUE
• FSG
• Temporally Repeated Routes
• --Find patterns of routes repeated in time,
  rather than space.
• Method FSG

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Structurally Similar Routes

• We assign all vertices the same label.
• Three variants for edge labels weight, distance,
  and time.
• -- OD_TD TOTAL-DISTANCE
• -- OD_GW GROSS-WEIGHT
• -- OD_TH MOVE-TRANSIT-HOURS

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Experiments with SUBDUE (MDL principle)

• SUBDUE A substructure discovery system
• Results
• Took about 3.25 hours to handle a graph of 100
  vertices and 561 edges to find the best 3
  patterns of beam size 4.
• Would need 6 months on the complete graph.
• Results were trivial.

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• Significant traffic from node 2 to node 4 via
  node 3, but not much return traffic (deadheading)

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Experiments with FSG

• FSG mines patterns across a set of graph
  transactions.
• Divides the single graph into multiple distinct
  sub-graphs, and treats each sub-graph as a
  separate transaction.

• Breadth first partitioning
• Depth first partitioning
• Both may result in patterns being broken across
  partitions

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• Results
• Partition sizes 400, 800, 1200 and 1600.
• Depth-first partitioning 200 frequent patterns
  were found with the minimum support 120.
• Breadth-first partitioning 667 frequent patterns
  were found with the minimum support 240.
• Had runtime and memory problems with lower
  supports on the breadth-first partitions.
• FSG is not an appropriate tool to use for mining
  recurrence patterns in a large single graph

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Temporally Repeated Routes

• FSG
• Exploits the temporal nature of the
  transportation graph
• Partition each graph into a set of graph
  transactions based on date

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• Results
• Unable to run FSG on the entire data set due to
  insufficient memory / swap space.
• Most were small patterns. (The following is the
  biggest one)

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Patterns Discovered by Using ConventionalMining
Algorithms

• Mapped the dataset into a standard
  transactional representation.
• Used traditional data mining approaches.
• Used Weka for association rule mining, instance
  (tuple) classification and cluster analysis on
  the transportation data.

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Evaluations of Conventional Algorithms

• Traditional data mining techniques have produced
  interesting and meaningful results to summarize
  our data.
• Further experimentation is required to explore
  the potential and limitations of these techniques
  on temporal transportation network data.
• Lose some insights from the structural
  characteristics of the data.

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Challenges forData Mining Research

• Handling the temporal aspects of graphs (dynamic
  graphs).
• Incorporating the notion of events into a graph.
• Expanding graph mining techniques beyond data
  similar to molecular structures.
• Determining what makes a graph pattern
  interesting.