E STIMATING F REEWAY T RAFFIC S PEEDS FROM S INGLE L OOPS U SING R EGION G ROWING Presented at the TransNow Student Conference At Portland State University By: Ryan P. Avery Friday, November 19, 2004 Ryan Avery Dr. Yinhai Wang Dr. Nancy Nihan

19 November Overview Why is Speed Important? Speed Detection Technologies What is Region Growing? Methodology Test Results Conclusion Future Improvements

19 November Why is Speed Important? It is a useful measure of freeway conditions Easily understood by the public It is required in order to estimate travel time Unfortunately, few detectors directly measure traffic speeds. ©

19 November Speed Detection Technologies Manual Collection (via a radar gun) –Expensive and impractical –Boring! Remote Traffic Microwave Sensor (RTMS) –Can measure speed directly or detect vehicles over multiple lanes –Requires elevated structure or pole to mount

19 November Traffic Detection Technologies Video Detection –Rich data source –Typically used for presence detection for intersection control, few robust speed algorithms available –Requires elevated structure or pole to mount Loop Detectors –Popular method, widely deployed –Single loops collect only volume and occupancy –Double loops also collect speed and bin volume

19 November What is Region Growing? Region Growing is a technique typically used in computer vision systems to segment images into different parts “A region grower begins at a position in an image and attempts to grow each region until the pixels being compared are too dissimilar to the region to add them” - Computer Vision, by Shapiro and Stockman

19 November What is Region Growing? How it works… X

19 November Methodology Apply region growing to loop detector data to separate data intervals into groups containing only short vehicles (SV) and those containing at least one long vehicle (LV) Based on vehicle population distributions observed by Wang and Nihan on I-5 in Seattle, Washington

19 November Methodology SV LV

19 November Methodology Classical speed estimation from single loops is performed via the following formula (Athol 1965): WSDOT uses a constant g = 2.4 to produce speed estimates from single loop detectors

19 November Methodology However, g should not be regarded as a constant. In fact, Wang and Nihan demonstrated that g is actually the inverse of the mean effective vehicle length: Using a constant g will therefore not give good results when the vehicle length distribution varies

19 November Methodology To overcome this shortcoming, region growing is applied to a group of second intervals of data, or a five minute period. The intervals are sorted in order of increasing occupancy per vehicle (O/V)

19 November Methodology A Primary Assumption The first non-zero O/V interval after sorting consists of only SVs This is necessary as a “seed” for the region growing algorithm Wang and Nihan found that when assuming the first TWO intervals contained only SVs, this was violated less than 3% of the time.

19 November Methodology Region growing is then performed, and the following statistics are calculated for each successive interval: If the next interval deviates too greatly from the previous intervals, the first region is closed and a new one is begun.

19 November Methodology The first region consists of only SVs, so the characteristics of the SV population can be used to produce a speed estimate:

19 November Test Results Tested using data from southbound I-5 at NE 130 th and NE 145 th St. on May 13, 1999 Estimates compared to dual-loop ground truth data at both locations Periods of 3, 4, and 5 minutes were tested

19 November Test Results Table of results:

19 November Test Results Graphical results from NE 145 th St for a period of 5 minutes:

19 November Conclusions Region growing can be successfully used to group SV and LV-containing intervals This enables better speed estimates from single loop detectors than the traditional method currently used by WSDOT It also provides a future opportunity to estimate LV volumes

19 November Future Improvements Use a statistical t-test for inference of group membership Simplify the algorithm to eliminate some thresholds Expand the algorithm to estimate the number of LVs in traffic flow Test vehicle length distributions found by Wang and Nihan for temporal and spatial transferability

19 November Thank You! Any Questions?