Accurate Speed and Density Measurement for Road Traffic in India
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Transcript of Accurate Speed and Density Measurement for Road Traffic in India
Accurate Speed and Density Measurement for Road Traffic in
India
Rijurekha Sen (IIT Bombay) Andrew Cross, Aditya Vashishtha,
Venkat Padmanabhan, Ed Cutrell, Bill Thies
User: How would travel time shifts change commute time?
Home
Office
Bengaluru Traffic Control Center
Operator: can measure traffic density, speed, and flux, and trigger automated alerts?
Researcher: How are different traffic parameters like speed, density and flux related?
Aren't these solved problems?
Yes, but for traffic like ....
Loop detectors Traffic cameras
While Indian traffic looks like ....
Prior Work to Sense Unlaned Traffic
Lakshminarayanan et al. DEV 2011 (-) binary classification of density based on grayscale histograms with limited evaluation
Quinn et al. AAAI-D 2010 (-) only detects motion of vehicles with limited evaluation
Trazer from Kritikal Solutions (IIT Delhi) (-) proprietary solution costing INR 3-5 Lakhs per license (-) frontal view of traffic to match vehicle Haar features, no evaluation for density measurements in case of occlusion
Sen et al. Mobisys 2010, SenSys 2012 (IIT Bombay) (-) binary or 4-level classification of density (-) low accuracy for acoustic sensors, no speed for radio sensors
Measuring Density and Speed using Video
Experimental Setup
Standard mounting ― Aimed at intersection
Experimental Setup
Standard mounting ― Aimed at intersection
Indiranagar Malleshwaram Mekhri Windsor
Video recorded using Canon FS100
camcorder. Processed on IBM
R61 Thinkpad laptop using
OpenCV.
Our mounting ― Looking down on traffic
Density With Background Subtraction?
subtract
a vehicle frame
an empty frame
But, Bengaluru buses surprised us!
The tops of the buses look exactly like the road, so background subtraction yields zero density.
Density With Yellow Tape Analysis?
Tape on road Density for empty road
Density With Yellow Tape Analysis?
Tape on road Density for two buses
But, shadows surprised us!
Treated as part of vehicle! Need perspective correction
Final Density Estimation Algorithm
Spatial condition: Does contrast between yellow and black rectangles disappear due to uniform vehicle top?
Final Density Estimation Algorithm
Spatial condition: Does contrast between yellow and black rectangles disappear due to uniform vehicle top?
Temporal condition: Does average RGB of rectangle pixels change by more than a threshold between two consecutive frames? (Consecutive frames reduce light change issues.)
Final Density Estimation Algorithm
Spatial condition: Does contrast between yellow and black rectangles disappear due to uniform vehicle top?
Temporal condition: Does average RGB of rectangle pixels change by more than a threshold between two consecutive frames? (Consecutive frames reduce light change issues.)
Linear regression on a training vehicle set to reduce systemic under-estimation.
Moving averages to extend 1-d density estimation to 2-d density estimation.
Speed Estimation Algorithm
For pixels that moved by more than a threshold,
Speed Estimation Algorithm
For pixels that moved by more than a threshold,
search in the neighborhood of size covering high speeds, for pixels of similar RGB.
Speed Estimation Algorithm
For pixels that moved by more than a threshold, search in the neighborhood of size covering high speeds,
for pixels of similar RGB.
The displacement that maximizes the similarity over all pixels, is considered speed in pixels between consecutive frames.
Density Algorithm Evaluation
Density Algorithm Evaluation
Density Algorithm Evaluation
The relative errors are higher for smaller vehicles
like two-wheelers.
2-D Density Evaluation
In our applications, we use moving averages
over 30 seconds for density.
Speed Algorithm Evaluation
Speed Algorithm Evaluation
Speed Algorithm Evaluation
Vehicle height differences variation in speed estimates. Taller vehicle higher speed
Decrease in Speed Error with Increase in Averaging Window Size
In our applications, we use moving averages over 30 seconds for speed values.
Some Applications of the Density and Speed Estimates
Users would like shorter commute times
In Indian cities, spatial shifting (rerouting) is often not effective since all routes are likely congested
An alternative is temporal shifting of traffic (e.g., the work of Balaji Prabhakar @ Stanford)
Avoiding Congestion
Temporal Shifting
20 minutes moving averages of speed and density values between 8:15 am – 11:15 am on Jul 10, 2012 at Malleshwaram.
Temporal Shifting
20 minutes moving averages of speed and density values between 8:15 am – 11:15 am on Jul 10, 2012 at Malleshwaram.
Speed and density are inversely related
there exist opportunities for users to shift and gain.
But how about the traffic authorities?
Estimating Fundamental Curves of Transportation Engineering
speed vs. density flux vs. speed
High flux needs speeds in 26-38 kmph range
High flux needs density < 40%
Fundamental Curves of Transportation Engineering
High flux values need < 40% density values.
speed vs. density flux vs. speed
Fundamental Curves of Transportation Engineering
High flux values need < 40% density values.
95% of the flux in congestion correspond to densities less than 80%, thus very high densities are outliers.
Just 20% reduction in density
can double the speed.
flux percentages at high densities
Effect of Uniform Flux Redistribution
Flux percentages for different speed bins for 8:15 to 11:15 am, Jul 10, 2012 at Malleshwaram
Flux percentages for different speed bins for flux values 4.5 – 5.5
Uniform redistribution over 3 hours flux of 5.04. This will increase speeds for vehicles, corresponding to
about 80% flux, to above 35 Km/hr.
Simple, accurate density and speed estimation for un-laned traffic using videos.
Conclusion
Non-trivial insights informed our algorithm design.
Some applications of the density and speed estimates.
Several avenues for improvement.
Auto-calibration of cameras.
Future Work
Combination with night vision.
Evaluation on temporally and spatially larger datasets.
System development to reduce computation and communication overhead.
Sharing methods and insights with the traffic authorities.