1. Adaptive Fastest Path Computation on a Road Network : A Traffic Mining Approach Hector Gonzalez Jiawei Han Xiaolei Li Margaret Myslinska John Paul Sondag Department of Computer Science University of Illinois at Urbana-Champaign ---------------- Presented by Dongmin Shin IDS Lab., SNU, Korea 2008.01.11.

2. Copyright  2006 by CEBT Index  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

3. Copyright  2006 by CEBT Overview  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.. Center for E-Business Technology

4. Copyright  2006 by CEBT Term Definition  Road network  Speed pattern  Driving pattern  Edge forecast model F(edge_id, t) returns  Query Center for E-Business Technology

5. Copyright  2006 by CEBT Traffic Database Center for E-Business Technology

6. Copyright  2006 by CEBT Road Network Partitioning  Road networks are organized around a well-defined hierarchy of roads Center for E-Business Technology

7. Copyright  2006 by CEBT Traffic 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 Center for E-Business Technology

8. Copyright  2006 by CEBT Traffic 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 Center for E-Business Technology

9. Copyright  2006 by CEBT Pre-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 Center for E-Business Technology

10. Copyright  2006 by CEBT Pre-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 Center for E-Business Technology

11. Copyright  2006 by CEBT Fastest 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 Center for E-Business Technology

12. Copyright  2006 by CEBT Fastest 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) Center for E-Business Technology

13. Copyright  2006 by CEBT Fastest Path Computation  Example Center for E-Business Technology The lowest common ancestor In order to simplify, 1.Ignoring edge frequency 2.No pre-computed paths

14. Copyright  2006 by CEBT Fastest 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 Center for E-Business Technology

15. Copyright  2006 by CEBT Experimental 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 Center for E-Business Technology

16. Copyright  2006 by CEBT Experimental Evaluation  Query Length and Upgraded Paths Center for E-Business Technology

17. Copyright  2006 by CEBT Experimental Evaluation  Area Pre-computation Center for E-Business Technology  Road Network Size

18. Copyright  2006 by CEBT Contribution  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 Center for E-Business Technology

19. Copyright  2006 by CEBT Contribution  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 Center for E-Business Technology