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.