1. Jianyu (Jack) Zhou Advisor: Reginald Golledge Department of Geography University of California Santa Barbara, CA University of California Santa Barbara, CA UCGIS Summery Assembly, June 28 - July 1, 2005 A Three-Step General Map Matching Method in the GIS Environment: A Travel/Transportation Study Perspective A Three-Step General Map Matching Method in the GIS Environment: A Travel/Transportation Study Perspective

2. Outline Outline 1.Introduction 2.Problem Statement 3.A general three-step map matching methodology that combines heterogeneous techniques: a) data processing; b) curve- to-curve mapping; c) noise and off-road travel discernment. 4.Conclusion and future research

3. Introduction Map matching: the process of correlating two sets of geographical positional information.Map matching: the process of correlating two sets of geographical positional information. Application area: travel behavior/transport study, car navigation, car tracking, spatial data conflation, etc.Application area: travel behavior/transport study, car navigation, car tracking, spatial data conflation, etc. Point-to-point matching, point-to-curve matching, curve-to-curve matchingPoint-to-point matching, point-to-curve matching, curve-to-curve matching On-line matching and Off-line matchingOn-line matching and Off-line matching

4. Map Matching in Travel Study Perspective In travel/transportation studies, map matching is used as a means to transfer the road network attributes to the mapping travel route in order to derive certain travel behavior.In travel/transportation studies, map matching is used as a means to transfer the road network attributes to the mapping travel route in order to derive certain travel behavior. Map matching in travel/transportation studies aims at: 1) identifying the correct road links traversed by the traveler; 2) ensuring that the identified links form a meaningful travel route; and 3) expect to help answer queries beyond the direct matching result.Map matching in travel/transportation studies aims at: 1) identifying the correct road links traversed by the traveler; 2) ensuring that the identified links form a meaningful travel route; and 3) expect to help answer queries beyond the direct matching result.

5. Problem Statement -matching factor selection Proximity, Heading and others:Proximity, Heading and others: –“GPS position relative to the road link” ; – “average distance traveled on current link” and; – “large distance traveled on current road link” Different select criteria could also result in conflicting matching conclusions.Different select criteria could also result in conflicting matching conclusions. Combine the selecting factorsCombine the selecting factors –A weighting scheme –Bayesian Belief Theory and Dempster-Shafter’s rule

6. Problem Statement - matching integrity Selection criteria helps identify a series of the matched road segments from the pool of candidate links. They might show up as a group of disconnected “paths.”Selection criteria helps identify a series of the matched road segments from the pool of candidate links. They might show up as a group of disconnected “paths.” Curve-to-curve matching: connecting the GPS points in sequence to form piece-wise linear curvesCurve-to-curve matching: connecting the GPS points in sequence to form piece-wise linear curves Improvement on point-to-point point-to-curve matching: topology relations to guide the search for the next matching candidate and eliminate unreachable links.Improvement on point-to-point point-to-curve matching: topology relations to guide the search for the next matching candidate and eliminate unreachable links.

7. Defects with Existing Map Matching Methods Weight-based map matching (Yin and Wolfson,2004), Fuzzy-logic based map matching (Syed and Cannon, 2004), General map matching (Quddus et al, 2003)Weight-based map matching (Yin and Wolfson,2004), Fuzzy-logic based map matching (Syed and Cannon, 2004), General map matching (Quddus et al, 2003) Examinations of several map matching methods revealed:Examinations of several map matching methods revealed: –Ignore global information, matching to branch. –Position of the street node and GPS sampling frequency affects matching results. –Doesn’t allow repetitive visit of street links.

8. Example: Overshoot and Gap

9. A Three-step General Map Matching Methodology (1) Data Preprocessing - Cluster reduction: –Reduce the systematic noise in the data. Clusters phantom the slow moving speed and random travel directions of the GPS carrier. –DBSCAN (Ester et al., 1996) clustering algorithm for cluster searching since it doesn’t need assumption on the number and shape of the clusters in the input data.

10. Cluster of GPS points Recovered via DBSCAN Algorithm

11. A Three-step General Map Matching Methodology (1) Data preprocessing - Density leverage: –Dynamically adjust the GPS data sampling frequency against the model resolution of the base street map. –Generating pseudo GPS points when GPS sampling interval is greater than the length of a traversed street link

12. Density Leverage

13. A Three-step General Map Matching Methodology (2) Matching procedure -Curve-to-curve Matching:Matching procedure -Curve-to-curve Matching: –GPS recorded travel trace is treated as a translated and rotated version of the matching route. Dual selection criteria: accumulated 2-norm distance (A2ND) and rotational variation metric (RVM).Dual selection criteria: accumulated 2-norm distance (A2ND) and rotational variation metric (RVM). –Develop a pool of the best candidates simultaneously and incrementally. –A2ND and RVM both serve to constrain the match search in the street network space. Two ranked solution pools are derived in terms of A2ND and RVM separately.

14. A Three-step General Map Matching Methodology (2) Topological completeness: determine potential turning action around a street intersection:Topological completeness: determine potential turning action around a street intersection: The projection of current GPS point falls on or out of the end point of the current link,The projection of current GPS point falls on or out of the end point of the current link, The projection of the current GPS point comes near to the end point of the current link, but the point’s position is getting away from the current link,The projection of the current GPS point comes near to the end point of the current link, but the point’s position is getting away from the current link, The candidate set of next traversed link: the topologically connected links to the intersection node. Filtered with Prohibited maneuver and turn restriction info.The candidate set of next traversed link: the topologically connected links to the intersection node. Filtered with Prohibited maneuver and turn restriction info.

15. A Three-step General Map Matching Methodology (2) Use the rank aggregation method to combine the ranking solution list in A2ND and RVM to obtain a combined ordering:Use the rank aggregation method to combine the ranking solution list in A2ND and RVM to obtain a combined ordering: –Kemeny ordering minimizes the sum of the “bubble sort” distances and thus generates the best compromise ranking. It is a NP-hard problem. –Borda’s method: Each candidate in the list is assigned a score of the number of candidates ranked blow it. Its total score across the different ranking list is finally sorted in a descending order. –Footrule optimal aggregation:Given n lists of same set of elements, generate the median permutation of the candidates in the lists.

16. Sample Match Results

17. A Three-step General Map Matching Methodology (1) (3) Off-Road Travel/Noise Discernment Dempter-Shafter theory (Shafer, 1976) Yes No Perhaps 1 1 1 20m30m Yes No Perhaps 1 1 1 90  Heading AssignmentProximity Assignment

18. Sample Match Results

19. Conclusion The method is unique inThe method is unique in –1) data preprocessing with point cluster reduction and density leverage, –2) offering the candidate solution within a pool of “the best” –3) balancing of matching results from multiple matching factors with rank aggregation –4) intelligently utilizing the basic network constraint attributes with “expert rules” to increase the matching accuracy –5) and Dempster belief test to discern the noise and off- road travel