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 ID: 741170
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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 TechnologySlide2
Motivation
Traffic applicationsReal time traffic congestion informationRoute planning - traffic aware routingTraffic delay prediction
Traffic delays
and congestion
Wasted fuel
Commuter frustration
4.2 billion hours in 2007 spent struck in traffic
Estimate current delay on each road segmentSlide3
Vtrack Goal
Route planning
Hot spot detection
Road segment delay estimatesSlide4
Approaches
Flow monitoring sensorsHigh deployment costGPS equipped probe vehiclesCover large areasDeployment costEnd user smart phonesLarge penetration and massive amount of data
Sensors: GPS, Wi-Fi, GSMOn roads and time useful for other usersSlide5
Challenges
Inaccuracy
of position samplesEnergy
consumption
GSM
GPS
Wi-Fi50m
200m
5m
VTrack
Wi-Fi
Infrequent GPS samplesSlide6
Wi-Fi localization
War driving: Access point - GPS mappingAP observations -> Centroid location
Noise
Outliers
OutagesSlide7
Delay estimation
Map matching
- Sequence of segments
Find delay on road segmentsSlide8
Map matching
Hidden Markov Model
S1
S2
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
- InterpolationSlide9
Dealing with outagesSlide10
Delay on segments
S1
S2
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))Slide11
VTrack
ApplicationsRoute PlanningShortest time path between a source and a destinationHotspot detectionFinding road segments that are highly congested
Evaluation
Analyzed
over 800 hours of drive data
25 cars with both GPS and Wi-FiSlide12
Key Results
HMM based map matching is robust to noiseTrajectories with median error less than 10%Delay estimates from Wi-Fi are accurate enough for route planningThough individual segment delay estimates have 25% median errorOver 90% of shortest paths have travel times within 15% of true shortest path
Accurately detect over 80% hotspots with less than 5% false positivesSlide13
Further workSampling GPS infrequently
Improves the accuracy of Wi-Fi based estimatesAnalyzed energy consumptionAdaptive samplingDynamically selects best sensorBased on road networks, accuracy, energySegment delay prediction