1. CrowdAtlas: Self-Updating Maps for Cloud and Personal Use Mike Lin

2. Authors Yin Wang I earned my B.S. and M.S. degrees at the Shanghai Jiao Tong University in 2000 and 2003, respectively, both in control theory. During 2003-2008, I worked with Stéphane Lafortune at the University of Michigan for my Ph.D degree in EECS. HP Labs was my first job after graduation. Since May 2013, I am affliated with Facebook.Stéphane Lafortune

3. Authors I am a computer science researcher in the Data Mining and Machine Learning group at Hewlett-Packard Laboratories. I work on techniques for automated classification, e.g. technology that learns to categorize documents into a topic hierarchy based on a small number of training examples given by humans, or to recognize computer systems that are likely to fail based on their past failures. Repeatedly I find that applying such technologies to real-world business problems often leads to fixable robustness issues & opportunities for substantial performance improvement. Hence, HP Labs is an excellent place for technology research as well as business impact.Hewlett-Packard Laboratories George Forman

4. Introduction i.Aggregates exceptional traces from users ii.Not conform to the open street map iii.Automatically update the map iv.Computer-generated roads inaccurate maps: A British insurance survey found that car accidents caused by or related to digital maps.

5. Introduction

7. Contribution i.An automatic map update system ii.Map inference with navigation iii.Contributing 61 km of roads for the beijing map on OSM.

8. CrowdAtlas Service 8 days of data from 70 taxis in Beijing, with a sampling interval of 10 seconds.

9. CrowdAtlas Service Extracting unmatched segments (red) after map matching seconds.

10. CrowdAtlas Service With one week of data and a threshold of four sub-traces, there are three clusters in the area

11. CrowdAtlas Service With one week of data and a threshold of four sub-traces, there are three clusters in the area with aerial image

12. MAP MATCHING 1.Within the error radius 2.Candidate sets ex: {x00, x10} 3.Likely drive path with observing sequence

13. Extracting Unmatched Segments Type I mismatch: Out of the error radius When a sample’s error radius of 50m does not intersect any road. Type II mismatch : Accidental long trajectories will be eliminated The maximum travel speed to 180 km/h; therefore, any consecutive samples matched to locations beyond 50t meters from each other are considered a mismatch, where t is the sampling interval.

14. New Road Inference i. Trace clustering by Hausdorff distance: The distance between two trajectories ii. Centerline fitting: exceeds threshold generates a polyline to minimize its mean square error to the samples. iii. Connection: connect with intersections iv. Iteration: Re-match and re-cluster

15. New Road Inference

16. i. Road attributes: Give directions of roads ii. Standalone mode: User-selected type of roads(drive,cycling,walking)

17. Challenges and Limitations

18. IMPLEMENTATION

19. PERFORMANCE

22. CONTRIBUTION

23. CENTERLINE OFFSET

24. COMMENT DATA COLLECTION RELIABLITY CHECK