adaptive fastest path computation on a road network : a traffic mining approach hector gonzalez...
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Adaptive Fastest Path Computation Adaptive Fastest Path Computation on a Road Network : A Traffic Mining on a Road Network : A Traffic Mining ApproachApproach
Hector Gonzalez
Jiawei Han
Xiaolei Li
Margaret Myslinska
John Paul Sondag
Department of Computer Science
University of Illinois at Urbana-Champaign
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Presented by Dongmin Shin
IDS Lab., SNU, Korea
2008.01.11.
Copyright 2006 by CEBT
IndexIndex
Overview
Contribution
Problem Definition
Traffic Database
Road Network Partitioning
Traffic Mining
Pre-computation and Upgrades
Fastest Path Computation
Experimental Evaluation
Conclusion
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OverviewOverview
Problem of Previous System MapQuest, MapPoint, Google Maps
a very simple model for road speeds– Constant speeds determined by their road class
Not considering a multitude of other factors that are very important– Driving patterns
ex) Nice and quick route, not a high crime area, weather, etc..
Instead of modeling all such factors explicitly, mining historic traffic data and learning from the past driving behavior
– Speed patterns ex) the time of departure, weather conditions, whether you are
qualified to drive on a car pool lane, etc..
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Term DefinitionTerm Definition
Road network
Speed pattern
Driving pattern
Edge forecast model F(edge_id, t)
returns
Query
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Traffic DatabaseTraffic Database
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Road Network PartitioningRoad Network Partitioning
Road networks are organized around a well-defined hierarchy of roads
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Traffic MiningTraffic Mining
Speed pattern mining
Multiple factors
– Weather, time of day, vehicle class and road construction, etc..
Ex) if area = a1 and weather = icy and time = rush hour then speed = ¼ X base speed
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Traffic MiningTraffic Mining
Driving pattern mining
Frequent pattern mining
1. Define a minimum support level
2. Go thorough the traffic database identifying frequent edges
3. Individual vehicle data
4. Longer frequent path segments can be mined
Problem of uniform minimum support level
– May filter many important local reads or may keep infrequently traveled high-level roads
– Using a minimum support relative to the traffic volume of each edge class in the area
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Pre-computation and UpgradesPre-computation and Upgrades
Area level pre-computation
May be different for different times and conditions
– Need to be annotated with the set of conditions
Two conditions to determine benefit
– How many fastest path queries will go through nodes of the pre-computed path
– How stable is the path
Apply limit to the area level
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Pre-computation and UpgradesPre-computation and Upgrades
Small road upgrades
Main assumption
– Drivers take the largest road available
An important exception
– If there is a small road that is faster than a large road, people will take it
Upgrade certain edges inside an area if under some driving conditions they have a significantly higher speed than the edges at the area borders under the same driving conditions
Only when absolutely necessary
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Fastest Path ComputationFastest Path Computation
Computed route has the following properties Supported by the historical driver behavior
Go through the largest possible roads
Account for all relevant factors affecting driving speed
Before running, following components have been computed Road network has already been partitioned.
Speed patterns have been mined.
Driving patterns have been mined.
Pre-computed a set of area-level fastest paths
Upgraded internal roads
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Fastest Path ComputationFastest Path Computation
Key concepts of the algorithm
1. For each path, keep
g(n) the current cost and h(n) the expected cost
2. At each step, pick the node with lowest g(n) + h(n) value that is frequent
3. Using the area hierarchy tree T
Ascending phase until reaching the lowest common ancestors
Descending phase otherwise
4. In ascending phase, only consider lower-leveled or equal-leveled neighbor
In descending phase, otherwise
5. Whenever inserting a new path, update g(n) and h(n)
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Fastest Path ComputationFastest Path Computation
Example
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The lowest common ancestor
In order to simplify,1.Ignoring edge frequency2.No pre-computed paths
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Fastest Path ComputationFastest Path Computation
Online path re-computation
The predictor function F is used to estimate driving conditions throughout the entire trip
– Initial estimate may be wrong
– Ex) weather, road closure, accident
In an online navigation system,
– Applying the algorithm with a starting node changed to the current position
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Experimental EvaluationExperimental Evaluation
Data Synthesis
Varying in areas, speed conditions, vehicles, weather factors
Three methods
– A* : correctness baseline. Searching for all nodes
– Hier : adaptive fastest path algorithm w/o pre-computation and upgrading
– Adapt : adaptive fastest path algorithm proposed in this paper
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Experimental EvaluationExperimental Evaluation Query Length and Upgraded Paths
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Experimental EvaluationExperimental Evaluation
Area Pre-computation
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Road Network Size
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ContributionContribution
Road hierarchy-based partitioning Natural hierarchy to partition the network into
semantically meaningful areas
Essential for driving pattern mining and adaptive fastest path pre-computation
Speed rule mining A set of concise rules
In conditions c for edge e then speed factor = f
Driving pattern mining Mining frequently traveled edges or edge-sequences
Frequent path-segment at the area level
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ContributionContribution
Adaptive pre-computation
Pre-computing a subset of fastest paths in order to speedup path computation
An area-level pre-computation strategy
Road upgrading
Support for some smaller roads should be upgraded
People usually drive through the largest possible roads available
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