Towards improving data presentation in the TripCheck

system

Rafael J. Fernández-Moctezumarfernand@cs.pdx.edu

You’re about to leave…… and you’re addicted to TripCheck. So you

go and check it out and see this:

What’s wrong with this picture?

What’s wrong with this picture?

Gray is not the new black!

Estimation may be betterthan no data on final products

• Data may not be displayed for various reasons– Sensor failure– Data quality

• May prefer to estimate the system state instead of displaying gray areas

• Not enough sensors – but may still be able to recover information.

• Must be careful with estimation – at least report a confidence factor.

System State Estimation

• Must carefully choose good sources of correlated data

• Every sensor station has its own estimator• The PORTAL project does a great job at

archiving data – this makes statistical regressors for state estimation a good alternative.

• May consider to rely on observed features from the past in addition to well-known transportation theory.

Regression

• Find a description of data in terms of a function

• Example: height (H) and weight (W) data transformed into a function F(H) = W.

Which functional family?

• May consider a linear family first…

… which can easily be derived (Least squares). May also consider the expected value of a conditional Gaussian:

• A conditional Gaussian buys us statistics: The conditional mean is a linear regressor! Plus, estimating the joint is easy.

bmxy +=

)()]|([2| xx

xyyxy xxypE μ

σσ

μμ −+==

Which functional family?

• May also want to consider non-linear functions. A good first approach is an Artificial Neural Network

Experimental results

• Looked at rush hour (06:00 – 10:30) data from a “typical” Portland week, from US 26 E (Oct. 16 – Oct. 20 2006)

• Found a segment that is typically shown gray (it is my commute, so I notice these things)

• Inputs: current measurements of speed at nearby stations

• Goal: come up with a good enough estimate to color the TripCheck map

Segment

Confusion matrices

0 0 0

0 55 0

0 0 0

14 5 0

0 9 0

0 6 21

0 0 0

0 55 0

0 0 0

Milepost 73.62 Milepost 71.37

Linear

ANN 17 2 0

2 7 0

0 2 25

Prediction R Y G

Observed

R

Y G

Prediction R Y G

Prediction R Y G

Prediction R Y G

Observed

R

Y G

Observed

R

Y G

Observed

R

Y G

80%

89%100%

100%

Future work

• May still be able to recover system information with a nonlinear model from far away stations

• Still need to explore other segments and build a representative amount of model regressors (20% ?) to demonstrate effectiveness of the approach

• What keeps us from using this approach to estimate intermediate location states?

Future work

• May want to consider regressors with more inputs (shifted speeds, time, etc.)

• If nonlinear regressors are effective, we may want to use Gaussian Mixture Models (cheaper to train, statistically rich)

• Addressing quality in data presentation can be a sub-product of a more general problem: construct a framework for reliable system state estimation.