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VTrack: Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones
Arvind Thiagarajan, Lenin Ravindranath, Katrina LaCurts, Sivan Toledo, Jakob Eriksson,
Hari Balakrishnan, Samuel MaddenSenSys 2009
Slides from: http://www.eecs.ucf.edu/~turgut/COURSES/EEL6788_AWN_Spr11/Lectures/VTrack.pptx
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Motivation
• Road Traffic Problem– Results– Causes inefficiency– Fuel Waste– Frustration
• Trends– More cars on road (1B now and projected to double)– More time wasted in traffic (4.2B Hrs in 2007 and increasing)
• Smartphone Capabilities– Massive Deployment– GPS , WiFi, and Cellular Triangulation Capabilities
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Motivation
• Real-time traffic information (e.g., travel time or vehicle flow density) can be used to alleviate congestion – Informing drivers of “Hotspots” to avoid– Traffic aware routing– Combining historic and real-time information (prediction)– Observing times on segments
• Improve operations (e.g., traffic light cycle control) • Plan infrastructure improvements• Assess congestion pricing and tolling schemes
• Vehicles as probes to collect traffic data!– From dedicated/commercial fleets to mobile phones
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Challenges
• Energy consumption: – GPS is energy hungry– Supporting adaptive sampling rate
• Inaccurate position samples: – Urban canyons: GPS is not always available..– Forcing to use other alternatives (less accurate
schemes), e.g., WiFi and cellular
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VTrack
• VTrack:– Estimate travel time– Apps: hotspot detection and route planning
• Contributions:– HMM-based map matching (trajectory)
• Not new, but none tried with sparse samples
– WiFi localization can alone provide fairly accurate “travel time” estimation• Yet, hotspots may not be detected well (quite a few misses)
– Periodic GPS sampling is OK (for energy saving)
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Architecture
• Users with smartphones
• Run VTrack reporting application
• Report position data periodically to the server
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VTrack Server
• Server algorithm components– Map-matcher– Travel-time estimator
• If GPS not available– Access point (AP) observations
are used– AP observations converted to
position estimates– Done via the wardriving
database where APs have been in previous drives
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VTrack Application Support
• Detecting and visualizing hotspots: – Hotspot: a road segment on which the
observed travel time exceeds the time that would be predicted by the speed limit by some threshold
– Optimized for lowering miss rate + false positive
• Real-time route planning: – Plans least time path (finding a good route!) – Uses individual segment estimated from
VTrack
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Server Algorithm Constraints
• Accurate– Errors 10% -15% range, at most 3-5 minutes on a 30 minute drive
• Efficient enough to use real-time data (map matching) – Existing map-matching use A*-type
• Prohibitively expensive• Hard to optimize
• Energy Efficient– Meet accuracy goals– Maximize battery life
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Server Challenges
• Map matching with errors (GPS outage, noisy data)
• Time estimation is difficult (even with accurate trajectories)– Source data noisy
• Difficult to determine best route• Difficult to point a travel time to a segment
• Localization accuracy is at odds with energy consumption– GPS more accurate but more power hungry– GPS takes up to 20x more power than WiFi– WiFi only available where APs available
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Vtrack: Map Matching• Input: A sequence of noisy/sparse
position data• Output: A sequence of road segments• Essential for travel time estimation• Steps
– 1. Samples are pre-processed to remove outliers.
– Outliers are classified as those > 200mph from last reading.
– 2. Outages handling via interpolations– 3. Viterbi decoding over Hidden Markov
Model (HMM) to estimate the route driven
– 4. Bad zone removal (low confidence matches)
• Use this data to estimate travel time of each road segment
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Time Estimation Errors
• Main source is inaccuracy of the map-matched output• Two reasons
– Outages during transitions• Cannot determine if delay is from
– Leaving segment– Entering segment
– Noisy positions samples• Inconsistencies when the sample is at the end of a short segment
and it’s the only sample in the segment
• Note: Though small segment estimates were sometimes inaccurate, the total path results, were accurate!
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VTrack Deployment
• Extensive Evaluation of Collection Results– Large Dataset– ~800 Hrs of Commuter Data– Deployed on 25 vehicles
• Deployment Method– iPhone 3G– Embedded in-car computers with GPS and WiFi
• Looking for– Sensor(s) Used– Sampling Frequency
• Ground Truth: GPS samples – Fails in regions with GPS Errors and dropouts
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VTrack Deployment Coverage• Initial collection of 2998
drives from the 25 cars with GPS and WiFi sensors
• Urban Area
• Simultaneous GPS and WiFi location estimates
• WiFi locations via centroid calculations of Aps
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• HMM-based map matching– robust to noise– Error < 10% (even only with WiFi)
• Large errors in individual segments, yet low errors in end-to-end trajectories– VTrack uses trajectories!
• WiFi is relatively poor in detecting hotspots due to service outage– If WiFi available: Detection > 80% +
False Alarm < 5%• GPS available and accurate,
periodic sampling helps with both
(hotspot: if estimated time of a segment is above this threshold)
Results