Jianyu (Jack) ZhouJianyu (Jack) ZhouAdvisor: Reginald Golledge Advisor: Reginald Golledge Department of Geography Department of Geography

University of California Santa Barbara, CAUniversity of California Santa Barbara, CA

UCGIS Summery Assembly, June 28 - July 1, 2005UCGIS Summery Assembly, June 28 - July 1, 2005

A Three-Step General Map A Three-Step General Map Matching Method in the GIS Matching Method in the GIS

Environment: Environment: A Travel/Transportation Study Perspective A Travel/Transportation Study Perspective

  

OutlineOutline

1.1. IntroductionIntroduction2.2. Problem StatementProblem Statement3.3. A general three-step map matching A general three-step map matching

methodology that combines methodology that combines heterogeneous techniques: a) data heterogeneous techniques: a) data processing; b) curve-to-curve mapping; c) processing; b) curve-to-curve mapping; c) noise and off-road travel discernment. noise and off-road travel discernment.

4.4. Conclusion and future research Conclusion and future research

IntroductionIntroduction

• Map matching: the process of correlatingMap matching: the process of correlating two sets two sets of geographical positional informationof geographical positional information..

• Application area: travel behavior/transport study, Application area: travel behavior/transport study, car navigation, car tracking, spatial data car navigation, car tracking, spatial data conflation, etc.conflation, etc.

• Point-to-point matching, point-to-curve matching, Point-to-point matching, point-to-curve matching, curve-to-curve matchingcurve-to-curve matching

• On-line matching and Off-line matchingOn-line matching and Off-line matching

Map Matching in Travel Map Matching in Travel Study PerspectiveStudy Perspective

• In travel/transportation studies, map matching is In travel/transportation studies, map matching is used as a means to transfer the road network used as a means to transfer the road network attributes to the mapping travel route attributes to the mapping travel route in order to in order to derivederive certain travel behavior. certain travel behavior.

• Map matching in travel/transportation studies Map matching in travel/transportation studies aims aims at: 1) identifying the correct road links traversed at: 1) identifying the correct road links traversed by the traveler; 2) ensuring that the identified links by the traveler; 2) ensuring that the identified links form a meaningful form a meaningful travel route; and 3) expect to travel route; and 3) expect to help answer queries beyond the direct matching help answer queries beyond the direct matching result. result.

Problem StatementProblem Statement-matching factor selection-matching factor selection

• Proximity, Heading and others: Proximity, Heading and others: – ““GPS position relative to the road link” ;GPS position relative to the road link” ;– “ “average distance traveled on current link” and;average distance traveled on current link” and;– “ “large distance traveled on current road link” large distance traveled on current road link”

• Different select criteria could Different select criteria could also result inalso result in conflicting matching conclusions.conflicting matching conclusions.

• Combine the selecting factorsCombine the selecting factors– A weighting scheme A weighting scheme – Bayesian Belief Theory and Dempster-Shafter’s rule Bayesian Belief Theory and Dempster-Shafter’s rule

Problem StatementProblem Statement- - matching integritymatching integrity

• Selection criteria helps identify a series of the Selection criteria helps identify a series of the matched road segments from the pool of candidate matched road segments from the pool of candidate links. They might show up as a group of links. They might show up as a group of disconnected “disconnected “paths.”paths.”

• Curve-to-curve matching: Curve-to-curve matching: connectingconnecting the GPS the GPS points in sequence to form piece-wise linear curvespoints in sequence to form piece-wise linear curves

• Improvement on point-to-point point-to-curve Improvement on point-to-point point-to-curve matching: topology relations to guide the search matching: topology relations to guide the search for the next matching candidate and eliminate for the next matching candidate and eliminate unreachable links. unreachable links.

Defects with Existing Defects with Existing Map Matching MethodsMap Matching Methods

• WeightWeight-based map matching (Yin and -based map matching (Yin and Wolfson ,2004), Fuzzy-logic based map matching Wolfson ,2004), Fuzzy-logic based map matching (Syed and Cannon, 2004), General map matching (Syed and Cannon, 2004), General map matching (Quddus et al, 2003) (Quddus et al, 2003)

• Examinations of several map matching methods Examinations of several map matching methods revealed: revealed: – Ignore global information, matching to branch. Ignore global information, matching to branch. – Position of the street node and GPS sampling frequency Position of the street node and GPS sampling frequency

affects matching results.affects matching results.– Doesn’t allow repetitive visit of street links.Doesn’t allow repetitive visit of street links.

Example: Overshoot and GapExample: Overshoot and Gap

A Three-step General Map A Three-step General Map Matching Methodology (1)Matching Methodology (1)

Data Preprocessing Data Preprocessing - Cluster reduction: - Cluster reduction:

– Reduce the systematic noise in the data. Reduce the systematic noise in the data. Clusters Clusters phantom thephantom the slow moving speed and slow moving speed and random travel directions of the GPS carrier. random travel directions of the GPS carrier.

– DBSCAN (Ester et DBSCAN (Ester et al., 1996) clusteringal., 1996) clustering algorithm for cluster searching since it doesn’t algorithm for cluster searching since it doesn’t need assumption on the number and shape of need assumption on the number and shape of the clusters in the input data.the clusters in the input data.

Cluster of GPS points Recovered via Cluster of GPS points Recovered via DBSCAN AlgorithmDBSCAN Algorithm

A Three-step General Map A Three-step General Map Matching Methodology (1)Matching Methodology (1)

Data preprocessing Data preprocessing - Density leverage:- Density leverage:

– Dynamically adjust the GPS data Dynamically adjust the GPS data sampling frequency against the model sampling frequency against the model resolution resolution of the baseof the base street map. street map.

– Generating pseudo GPS points when Generating pseudo GPS points when GPS sampling interval is greater than the GPS sampling interval is greater than the length of a traversed street length of a traversed street link link

Density LeverageDensity Leverage

A Three-step General Map A Three-step General Map Matching Methodology (2)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 GPS recorded travel trace is treated as a translated and

rotated version of the matching route.rotated version of the matching route.• Dual selection criteria: accumulated 2-norm distance (A2ND) Dual selection criteria: accumulated 2-norm distance (A2ND)

and rotational variation metric (RVM). and rotational variation metric (RVM).

– Develop a pool of the best candidates simultaneously Develop a pool of the best candidates simultaneously and incrementally.and incrementally.

– A2ND and RVM both serve to constrain the match A2ND and RVM both serve to constrain the match search in the street network space. Two ranked solution search in the street network space. Two ranked solution pools arepools are derived in terms of derived in terms of A2ND and RVM A2ND and RVM separately.separately.

A Three-step General Map A Three-step General Map Matching Methodology (2)Matching Methodology (2)

• Topological completeness: determine potential turning Topological completeness: determine potential turning action aroundaction around a street intersection: a street intersection:

• The projection of current GPS point falls on or out of The projection of current GPS point falls on or out of the end point of the current link, the end point of the current link,

• The projection of the current GPS point comes near to The projection of the current GPS point comes near to the end point of the current link, but the point’s the end point of the current link, but the point’s positionposition is getting away from the current link, is getting away from the current link,

• The candidate set of next traversed link: the The candidate set of next traversed link: the topologically connected links to the intersection node. topologically connected links to the intersection node. Filtered with Prohibited maneuver and turn restriction Filtered with Prohibited maneuver and turn restriction info. info.

A Three-step General Map A Three-step General Map Matching Methodology (2)Matching Methodology (2)

• Use theUse the rank aggregation method to combine the ranking rank aggregation method to combine the ranking solution list in A2ND and RVM to obtain a combined solution list in A2ND and RVM to obtain a combined ordering:ordering:– Kemeny ordering minimizes the sum of the “bubble sort” Kemeny ordering minimizes the sum of the “bubble sort”

distances and thus generates the best compromise ranking. It is distances and thus generates the best compromise ranking. It is a NP-hard problem.a NP-hard problem.

– Borda’s method: Each candidate in the list is assigned a score Borda’s method: Each candidate in the list is assigned a score of the number of candidates ranked blow it. Its total score of the number of candidates ranked blow it. Its total score across the different ranking list is finally sorted in a across the different ranking list is finally sorted in a descending order.descending order.

– Footrule optimal aggregation:Given n lists of same set of Footrule optimal aggregation:Given n lists of same set of elements, generate the median permutation of the candidates in elements, generate the median permutation of the candidates in the lists. the lists.

Sample Match Results Sample Match Results

A Three-step General Map A Three-step General Map Matching Methodology (1)Matching Methodology (1)

(3)(3)• Off-Road Travel/Noise Discernment Dempter-Shafter theory (Shafer, 1976)

Yes

No

Perhaps

1

1

1

20m 30m

Yes

No

Perhaps

1

1

1

90

Heading Assignment Proximity Assignment

Sample Match ResultsSample Match Results

ConclusionConclusion

• The method is unique inThe method is unique in – 1) data preprocessing with point cluster reduction and 1) data preprocessing with point cluster reduction and

density leverage, density leverage, – 2) offering the candidate solu2) offering the candidate solution within a tion within a pool of “the best” pool of “the best” – 3) balancing of matching results from multiple matching 3) balancing of matching results from multiple matching

factors with rank aggregation factors with rank aggregation – 4) intelligently4) intelligently utilizing the basic network constraint utilizing the basic network constraint

attributes with “expert rules” to increase the matching attributes with “expert rules” to increase the matching accuracyaccuracy

– 5) and Dempster belief test to discern the noise and off-road 5) and Dempster belief test to discern the noise and off-road traveltravel