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

Motivation

• Traffic applications– Real time traffic congestion information– Route planning - traffic aware routing– Traffic delay prediction

• Traffic delays and congestiono Wasted fuelo Commuter frustration

• 4.2 billion hours in 2007 spent struck in traffic

Estimate current delay on each road segment

Vtrack Goal• Route planning• Hot spot detection

Road segment delay estimates

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

Challenges

Inaccuracy of position samples

Energyconsumption

GSM

GPS

Wi-Fi

50m 200m5m

VTrack– Wi-Fi– Infrequent GPS samples

Wi-Fi localization• War driving: Access point - GPS mapping• AP observations -> Centroid location

• Noise• Outliers• Outages

Delay estimation

• Map matching- Sequence of segments

• Find delay on road segments

Map matchingHidden Markov Model

S1S2

S3

p1 p2

p3

p4 S1

S2 S3

1/3

1/3

1/3

S1

S2

S3

S1

S2

S3

S1

S2

S3

S1

S2

S3

p1 p2 p3 p4

Viterbi

• Noise- Gaussian

• Outliers- Speed constraint

• Outages- Interpolation

Dealing with outages

Delay on segments

S1S2

S3

p1 p2

p3

p4

p1 p2 p3 p4

S1 S1 S3 S3

T (S1) = t(p2) – t(p1) + ½ (t(p3) – t (p2))

T (S3) = t(p4) – t(p3) + ½ (t(p3) – t (p2))

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

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

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