1. VTrack: Energy-Aware Traffic Delay Estimation Using Mobile Phones Lenin Ravindranath, Arvind Thiagarajan, Katrina LaCurts, Sivan Toledo, Jacob Eriksson, Sam Madden, Hari Balakrishnan Massachusetts Institute of Technology

2. Motivation Traffic applications – Real time traffic congestion information – Route planning - traffic aware routing – Traffic delay prediction Traffic delays and congestion o Wasted fuel o Commuter frustration 4.2 billion hours in 2007 spent struck in traffic Estimate current delay on each road segment

3. Vtrack Goal Route planning Hot spot detection Road segment delay estimates

4. Approaches Flow monitoring sensors – High deployment cost GPS equipped probe vehicles – Cover large areas – Deployment cost End user smart phones – Large penetration and massive amount of data – Sensors: GPS, Wi-Fi, GSM – On roads and time useful for other users

5. Challenges Inaccuracy of position samples Energy consumption GSM GPS Wi-Fi 50m200m 5m VTrack – Wi-Fi – Infrequent GPS samples

6. Wi-Fi localization War driving: Access point - GPS mapping AP observations -> Centroid location Noise Outliers Outages

7. Delay estimation Map matching - Sequence of segments Find delay on road segments

8. Map matching Hidden Markov Model S1 S2 S3 p1p2 p3 p4 S1 S2 S3 1/3 S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 p1p2p3p4 Viterbi Noise - Gaussian Outliers - Speed constraint Outages - Interpolation

9. Dealing with outages

10. Delay on segments S1 S2 S3 p1p2 p3 p4 p1p2p3p4 S1 S3 T (S1) = t(p2) – t(p1) + ½ (t(p3) – t (p2)) T (S3) = t(p4) – t(p3) + ½ (t(p3) – t (p2))

11. VTrack Applications Route Planning – Shortest time path between a source and a destination Hotspot detection – Finding road segments that are highly congested Evaluation Analyzed over 800 hours of drive data 25 cars with both GPS and Wi-Fi

12. Key Results HMM based map matching is robust to noise – Trajectories with median error less than 10% Delay estimates from Wi-Fi are accurate enough for route planning – Though individual segment delay estimates have 25% median error – Over 90% of shortest paths have travel times within 15% of true shortest path Accurately detect over 80% hotspots with less than 5% false positives

13. Further work Sampling GPS infrequently – Improves the accuracy of Wi-Fi based estimates – Analyzed energy consumption Adaptive sampling – Dynamically selects best sensor – Based on road networks, accuracy, energy Segment delay prediction