From GPS Traces to a Routable Road Map

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Presentation transcript:

From GPS Traces to a Routable Road Map Lili Cao University of California Santa Barbara, California, USA John Krumm Microsoft Research Redmond, Washington, USA

Local Arrangements For negative comments, complaints For positive comments, compliments

Tickets 1 Drink 1 Drink 1 Drink 1 Drink ACM-GIS Banquet Reception Wednesday 4 November 2009 1 Drink Reception Wednesday 4 November 2009 Drink tickets for Wednesday (today) reception 1 Drink Banquet Thursday 5 November 2009 1 Drink Banquet Thursday 5 November 2009 ACM-GIS Banquet Thursday, November 5, 7:30 p.m. Banquet and drink tickets for Thursday (tomorrow) banquet

Lunches on Your Own Hyatt (you are here) Food (Bellevue Way)

Giveaway 5 copies Blue star on name badge Pick up at conference registration table MapPoint 2009 5 copies Red star on name badge Give me your mailing address MapPoint 2010

Basic Idea Create road map data from GPS traces Crowdsource GPS traces from everyday vehicles From this … … to this Create road map data from GPS traces

Basic Idea Create road map data from GPS traces Raw GPS Map Crowdsource GPS traces from everyday vehicles From this … … to this Create road map data from GPS traces

Road Data: Useful but Expensive Navteq Printed maps Digital maps Tele Atlas

Roads Change Road closures New roads Road changes, e.g. from two-way to one-way October 29, 2009

GPS Data 55 Microsoft Campus Shuttles On demand and scheduled routes ~100 hours of data from each vehicle RoyalTek RBT-2300 GPS Logger 1 Hz sampling rate Powered from cigarette lighter Uploaded to SQL Server database Raw Data Commercial Map

Goal – Routable Road Network Infer Road Network Data Connectivity and geometry Road type (e.g. highway, arterial) Number of lanes Lane restrictions Speeds Road names Ideal output

Why Is This Hard? GPS data is noisy Random data in parking lots Most well-known solution requires human editing openstreetmap.org

Overview Step 1: Clarify GPS traces Step 2: Generate map graph Clarified GPS traces Routable map graph Original GPS traces Step 1: Clarify GPS traces Step 2: Generate map graph

Clarifying GPS Traces Apply imaginary forces to bundle nearby GPS traces jumbled GPS traces clarified GPS traces

1: Pull Toward Other Traces Virtual potential well generated by blue segment (upside-down Gaussian) force = d/dx potential GPS point Avoid force from perpendicular traces Repellent force from opposite direction traces force’ = cos(θ)*force θ

2: Keep Point Near Home Virtual potential well generated by blue segment Parabolic potential corresponds to linear spring force GPS point

Sum Forces + + Sum potentials (forces) to get net effect on GPS point

Clarifying GPS Traces For each GPS point Add all potential wells Move point Iterate until converge Original Processed Twisting Problem Final Twisting Problem Happens when GPS point crosses over opposite traffic lane Heuristic: If cos(θ) < 0 AND point is on right side of trace, force = 0 Fixes twist problem Reverse heuristic in Anguilla, Antigua & Barbuda, Australia, Bahamas, Bangladesh, Barbados, Bermuda, Bhutan, Bophuthatswana, Botswana, British Virgin Islands, Brunei, Cayman Islands, Channel Islands, Ciskei, Cyprus, Dominica, Falkland Islands, Fiji, Grenada, Guyana, Hong Kong, India, Indonesia, Ireland, Jamaica, Japan, Kenya, Lesotho, Macau, Malawi, Malaysia, Malta, Mauritius, Montserrat, Mozambique, Namibia, Nepal, New Zealand, Pakistan, Papua New Guinea, St. Vincent & Grenadines, Seychelles, Sikkim, Singapore, Solomon Islands, Somalia, South Africa, Sri Lanka, St Kitts & Nevis, St. Helena, St. Lucia, Surinam, Swaziland, Tanzania, Thailand, Tonga, Trinidad & Tobago, Uganda, United Kingdom, US Virgin Islands, Venda, Zambia, Zimbabwe θ

Parameter Selection M,σ1 k Other trace potential Spring potential Ideal x y Actual jumbled clarified σ2: Error of GPS N: # of traces

GPS Clarification Results Overview Satellite Original GPS data Clarified GPS data

Making it Scale Naïve implementation: for each node, scan all other segments 20 minutes per iteration Θ(n2) complexity, suffers when map gets large Optimization: for each node, only search segments within small distance Use kD-tree to index nodes 15 seconds per iteration Θ(n logn) complexity, good scalability

Generating Map Graph Sequentially process the traces and incrementally build the graph Merge nodes to existing nodes if distances are small & directions match Create new nodes & edges otherwise

Results of Graph Generation

Demonstration

Summary Raw GPS Clarified GPS Routable Roads GPS clarification with forces from potential wells Principled setting of parameters Efficient implementation Merge traces into road network Route planner

Further Work Intersection Detector Lane Counting With Alireza Fathi, Georgia Tech Lane Counting With James Chen, U. Washington