Footprint-oriented generation of traffic trajectories using cellular - PowerPoint PPT Presentation

Institute for Engineering Geodesy Footprint-oriented generation of traffic trajectories using cellular phone data trajectories using cellular phone data Geneva, 5–9 th May, Geospatial World Forum 2014 Shenghua Chen Institute of Engineering Geodesy University of Stuttgart, Germany

Content Content Institute for Engineering Geodesy 1 Motivation & problems 2 Relationship between data structure 3 Generation of traffic trajectory and test 4 Map matching for validation 5 Conclusion 2 Footprint - oriented generation of traffic trajectories using cellular phone data

1 1 Motivation and problems Motivation and problems Institute for Engineering Geodesy Figure: Cellular probes used in traffic condition acquisition Figure: Cellular probes used in traffic condition acquisition Rich source: High penetration of cellular phone; Highly combined with market: LBS has grow quickly recently; Highly combined with market: LBS has grow quickly recently; Cost: Few additional cost to system; 3 Footprint - oriented generation of traffic trajectories using cellular phone data

1 1 Background for problems Background for problems Institute for Engineering Geodesy Handover operation don’t happens on the boundary completely; The cell sequence can reflect the trajectory roughly; The cell sequence can reflect the trajectory roughly; The cell sequence by time slot may do not reflect the trajectory comprehensively, especially when dealing with A data with long time intervals. 4 Footprint - oriented generation of traffic trajectories using cellular phone data

2 2 Relationship between data structure Relationship between data structure Institute for Engineering Geodesy Static data Static data Digital Road Map (DRM) Cellular Phone Network Best server plots Signal strength maps Antenna positions and orientation Antenna positions and orientation Dynamic data • Cellular Phone Network • A interface • • A-bis interface A-bis interface 5 Footprint - oriented generation of traffic trajectories using cellular phone data

2 2 Relationship between data structure Relationship between data structure Institute for Engineering Geodesy Footprints produced by spatial overlapping between road network and GSM cells between road network and GSM cells Figure: Simplified system architecture of a mobile phone network with the interface Geospatial overlapping between the cellular Geospatial overlapping between the cellular (Do - iT , 2008) (Do - iT , 2008) phone network and traffic road network is the principle behind this ideal of footprint - oriented generation of trajectory. 6 Footprint - oriented generation of traffic trajectories using cellular phone data

2 2 Data structure of traffic telematic database Data structure of traffic telematic database Institute for Engineering Geodesy Data structure in database for traffic information acquisition based on mobile phone data based on mobile phone data 7 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Footprint-oriented generation of traffic trajectories Footprint-oriented generation of traffic trajectories Institute for Engineering Geodesy Flow chart of footprint - oriented generation of trajectory 8 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Mobile phone data processing Mobile phone data processing Institute for Engineering Geodesy Abnormal handover Behavior: No common No common point in the boundary between between continuous records Abnormal handover line number = [51 52 53 62 63 64 65 69 70] 9 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Processing A interface data from GSM system Processing A interface data from GSM system Institute for Engineering Geodesy Abnormal handover Behavior: No common point in the boundary between continuous records Reason: overflow or jump; Overflow: sequential Overflow: sequential Line: 7, 8, 25, 26, 63, 65 Continue; Jump: not sequential, handle the line, and continue Jump: not sequential, handle the line, and continue Line: 33, 40, 41 To get the polygon which have common point with these cells. Consider the last abnormal HO in the A data 10 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Footprint-oriented generation of traffic trajectories Footprint-oriented generation of traffic trajectories Institute for Engineering Geodesy For problem two 11 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Result of footprint-oriented trajectories(1) Result of footprint-oriented trajectories(1) Institute for Engineering Geodesy Figure: visualization of original cells (data 2: long Figure: visualization of original cells (data 1: time interval) time interval) normal) normal) 12 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Result of footprint-oriented trajectories(2) Result of footprint-oriented trajectories(2) Institute for Engineering Geodesy Figure: Cell sequence from data 2 by ordinal analysis Cells in A data and the cell sequence by TSP algorithm 13 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Result of footprint-oriented trajectories(3) Result of footprint-oriented trajectories(3) Institute for Engineering Geodesy R = 0.7*diagonal of cell MBX Figure: Flow chart of extending the cells to connect cells along the cell sequence 14 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Result of footprint-oriented trajectories(5) Result of footprint-oriented trajectories(5) Institute for Engineering Geodesy Figure: Parameter: Parameter: total distance: 46.2km Figure: Visualization of footprint – oriented trajectory by data1 Time peroid : 1h Maximum time interval: 0.3h Mean time interval: 0.1h 15 Footprint - oriented generation of traffic trajectories using cellular phone data

4 4 Result of footprint-oriented trajectories(4) Result of footprint-oriented trajectories(4) Institute for Engineering Geodesy Figure: Visualization of footprint – oriented trajectory by data 2 Figure: Visualization of footprint – oriented trajectory by data 2 Minimal cells set which connect from the start to the end 16 Footprint - oriented generation of traffic trajectories using cellular phone data

3 3 Result of footprint-oriented trajectories(6) Result of footprint-oriented trajectories(6) Institute for Engineering Geodesy Figure: Footprint-oriented generation of traffic trajectory based on A data from wireless communication system Total distance: > 120km Total distance: > 120km Time period: > 6h (7:01:22 – 12:56:46) Time period: > 6h (7:01:22 – 12:56:46) Parameters: Area: Stuttgart – Karlsruhe Maximum time interval: 1.17h - Manheim Mean time interval: 0.17h 17 Footprint - oriented generation of traffic trajectories using cellular phone data

4 4 Map matching for validation Map matching for validation Institute for Engineering Geodesy Map matching – Purpose Purpose: GPS data can not decide whether the trajectory by footprint are correct. Map matching – flow chart Figure: Flow chart of map matching to validate the trajectory by mobile phone data Figure: Flow chart to restructure the Figure: Flow chart to restructure the Two times to narrow the search space: road network using quadtree grid Quadtree grid connected to line; Quadtree grid only in the area of GPS point (temporary); 18 Footprint - oriented generation of traffic trajectories using cellular phone data

4 4 Map matching for validation Map matching for validation Institute for Engineering Geodesy Map matching (1) Purpose of map matching Steps of map matching Figure 10: gps and footprint - oriented trajectory Parameters: Parameters: Size of quadtree grid: 300m * 300m Size of quadtree grid: 300m * 300m Mean of line length: 40.17m Mean GPS intervals distance: 17.6m (0.5s) 19 Footprint - oriented generation of traffic trajectories using cellular phone data

4 4 Map matching for validation Map matching for validation Institute for Engineering Geodesy Map matching result Figure: Map matching result with GPS, Figure: Some limitation for distance map matching and trajectory generated 20 Footprint - oriented generation of traffic trajectories using cellular phone data

4 4 Map matching for validation Map matching for validation Institute for Engineering Geodesy Distance map matching to topological map matching Figure: topological Map matching and illustration 21 Footprint - oriented generation of traffic trajectories using cellular phone data

4 4 Map matching for validation Map matching for validation Institute for Engineering Geodesy Validation of footprint-oriented trajectory by map matching result Formula: Formula: Correction rate FT: set of lines which combine the trajectory generated by footprint- FT: set of lines which combine the trajectory generated by footprint- oriented algorithm MM: set of lines which combine the trajectory generated by topological map matching algorithm Result: Type of A data Normal handover record handover records with long time intervals time intervals Correction rate 93.8% 97.1% 22 Footprint - oriented generation of traffic trajectories using cellular phone data