This document presents a method for accurately measuring traffic speed and density in India using video analysis. The researchers developed algorithms to estimate density and speed that account for challenges with Indian traffic conditions. They evaluated the accuracy of the methods and explored applications like temporal shifting of traffic to reduce congestion. Future work areas include improving the system for real-world deployment to help traffic authorities manage traffic flow.

1. Accurate Speed and Density
Measurement for Road Traffic in
India
Rijurekha Sen (IIT Bombay)
Andrew Cross, Aditya Vashishtha,
Venkat Padmanabhan, Ed Cutrell, Bill Thies

2. Home
Offic
e
User: How would travel time shifts change commute time?

3. Bengaluru Traffic Control Center
Operator: can measure traffic density, speed, and flux,
and trigger automated alerts?

4. Researcher: How are different traffic parameters like
speed, density and flux related?

5. Aren't these solved problems?

6. Yes, but for traffic like ....
Loop detectors Traffic cameras

7. While Indian traffic looks like ....

8. 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

9. Measuring Density and Speed
using Video

10. Experimental Setup
Standard mounting ― Aimed at intersection

11. Experimental Setup
Video recorded
using Canon FS100
camcorder.
Processed on IBM
R61 Thinkpad
laptop using
Standard mounting ― Aimed at intersection OpenCV.
Indiranagar Malleshwaram Mekhri Windsor
Our mounting ― Looking down on traffic

12. Density With
Background Subtraction?
subtract
a vehicle frame
an empty frame

13. But, Bengaluru buses surprised us!
The tops of the buses look exactly like the road, so
background subtraction yields zero density.

14. Density With
Yellow Tape Analysis?
Tape on road Density for empty road

15. Density With
Yellow Tape Analysis?
Tape on road Density for two buses

16. But, shadows surprised us!
Treated as part of vehicle! Need perspective correction

17. Final Density Estimation Algorithm
Spatial condition:
Does contrast
between yellow and
black rectangles
disappear due to
uniform vehicle top?

18. Final Density Estimation Algorithm
Spatial condition: Temporal condition:
Does contrast Does average RGB of rectangle pixels
between yellow and change by more than a threshold
black rectangles between two consecutive frames?
disappear due to (Consecutive frames reduce light
uniform vehicle top? change issues.)

19. Final Density Estimation Algorithm
Spatial condition: Temporal condition:
Does contrast Does average RGB of rectangle pixels
between yellow and change by more than a threshold
black rectangles between two consecutive frames?
disappear due to (Consecutive frames reduce light
uniform vehicle top? 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.

20. Speed Estimation Algorithm
For pixels that moved by more than a threshold,

21. 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.

22. 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.

23. Density Algorithm Evaluation

24. Density Algorithm Evaluation

25. Density Algorithm Evaluation
The relative errors are
higher for smaller vehicles
like two-wheelers.

26. 2-D Density Evaluation
In our applications,
we use moving averages
over 30 seconds
for density.

27. Speed Algorithm Evaluation

28. Speed Algorithm Evaluation

29. Speed Algorithm Evaluation
Vehicle height differences 
variation in speed estimates.
Taller vehicle  higher speed

30. Decrease in Speed Error with Increase in
Averaging Window Size
In our applications, we use moving averages over 30 seconds
for speed values.

31. Some Applications of the Density
and Speed Estimates

32. Avoiding Congestion
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)

33. Temporal Shifting
20 minutes moving averages of speed and density values
between 8:15 am – 11:15 am on Jul 10, 2012 at Malleshwaram.

34. 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?

35. Estimating Fundamental Curves of
Transportation Engineering
High flux needs density < 40%
speed vs. density flux vs. speed
High flux needs speeds in 26-38 kmph range

36. Fundamental Curves of Transportation
Engineering
High flux values need < 40% density values.
speed vs. density flux vs. speed

37. 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

38. Effect of Uniform Flux Redistribution
Flux percentages for different speed bins for Flux percentages for different speed bins
8:15 to 11:15 am, Jul 10, 2012 at Malleshwaram 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.

39. Conclusion
Simple, accurate density and speed estimation for un-laned
traffic using videos.
Non-trivial insights informed our algorithm design.
Some applications of the density and speed estimates.
Several avenues for improvement.

40. Future Work
Auto-calibration of cameras.
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.