sensys 2009 speaker:lawrence. introduction overview & challenges algorithm travel time...
TRANSCRIPT
VTrack: Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones
Sensys 2009
Speaker:Lawrence
Outline
Introduction Overview & Challenges Algorithm Travel Time Estimation Evaluation Conclusion
Outline
Introduction Overview & Challenges Algorithm Travel Time Estimation Evaluation Conclusion
Introdution
Motivation Traffic delays and congestions Real time traffic information
Challenges Energy consumption Inaccurate position samples
VTrack Vehicles as probes A real time traffic monitoring system
Motivating Problem How the quality of VTrack’s travel time estimates on the sensor
being sampled and the sampling frequency.
Introdution
Key finding HHM-based map matching is robust to noise
Travel times estimated from WiFi localization alone are accurate enough for route planning
Travel times estimated from WiFi localization alone cannot detect hotspots accurately
Sampling GPS periodically to save power
Introdution
Contribution Quantitative evaluation of the end to
end quality of time estimates from noisy and sparsely sampled locations.
Outline
Introduction Overview & Challenges Algorithm Travel Time Estimation Evaluation Conclusion
System Overview
Key Application Detecting and visualizing hotspots Real time route planning
iPhone web page
Requirements
Accuracy For route planning , errors in the 10%~15%
range.
Efficient enough to run in real time Some existing map-matching algorithm run A*
style shortest path algorithm
Energy efficient GPS excessively drains the battery
Challenges
Map matching with outages and errors.
Time estimation - even with accurate trajectories is difficult
Localization accuracy is at odd with energy consumption
Outline
Introduction Overview & Challenges Algorithm Travel Time Estimation Evaluation Conclusion
Algorithm
HMM A Markov process with a set of hidden
states and observables.
Viterbi Decoding Dynamic programming tech Find the maximum likelihood sequence
of hidden states given a set of observables and emission probability and transition probability.
HMM
Hidden state: road segments Observables: position samples Transition probability: from one road to next Emission probability: conditional probability of
<segment, position>
Match mapping process
1 2 3 4
Outline
Introduction Overview & Challenges Algorithm Travel Time Estimation Evaluation Conclusion
Travel Time Estimation
The traversal time T(s) for any segment S:
Estimation Errors Outages during transition times.▪ Intersection delay
Noisy position samples▪ Noisy sensor
Outline
Introduction Overview & Challenges Algorithm Travel Time Estimation Evaluation Conclusion
Data collection
Raw data 800 hours 25 cars
Evaluation of Route Planning
WiFi good enough
Evaluation of Hotspot Detection
Detect 80%~90% of hotspots. Not too aggressive.
Evaluation of Energy Accuracy
Estimating WiFi Cost The cost per sample of GPS is 24.9X the cost per sample
of WiFi. 8% of total power consumption
Offline Energy Optimization (Assuming the WiFi cost is 1 unit)
Impact of Noise
Outline
Introduction Overview & Challenges Algorithm Travel Time Estimation Evaluation Conclusion
Conclusion
Using mobile phones to accuracy estimate travel times using inaccurate samples.
Address key challenge 1. reducing energy consumption 2. accurate travel time from inaccurate rate
positions
VTrack uses an HMM-based map matching scheme.
Successfully identify highly delayed segments and accuracy route planning with noisy.