Adaptive Fastest Path Computation on a Road Network : A Traffic Mining Approach Hector Gonzalez Jiawei Han Xiaolei Li Margaret Myslinska John Paul Sondag

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

Center for E-Business Technology


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..



Term DefinitionTerm Definition

Road network

Speed pattern

Driving pattern

Edge forecast model F(edge_id, t)

returns

Query



Traffic DatabaseTraffic Database



Road Network PartitioningRoad Network Partitioning

Road networks are organized around a well-defined hierarchy of roads



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



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



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



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



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




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)




Example


The lowest common ancestor

In order to simplify,1.Ignoring edge frequency2.No pre-computed paths



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



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



Experimental EvaluationExperimental Evaluation Query Length and Upgraded Paths



Experimental EvaluationExperimental Evaluation

Area Pre-computation


Road Network Size


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



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


Documents

Adaptive Fastest Path Computation on a Road Network : A Traffic Mining Approach Hector Gonzalez Jiawei Han Xiaolei Li Margaret Myslinska John Paul Sondag