Results of IDS Rural Intersection Data Collection Lee Alexander Pi-Ming Cheng Max Donath Alec Gorjestani Arvind Menon Bryan Newstrom Craig Shankwitz April 20, 2005

Outline u Purpose u Data collection and archival u Data processing u Definition of gap u Results v General accepted gap analysis v Intersection zone analysis v Gaps as a function of time of day v Gaps for different vehicle maneuvers v Gaps as a function of vehicle classification v Waiting for a gap v Gaps as a function of weather conditions v Small accepted gap analysis u Conclusions

Purpose u Determine driver behavior at intersection u Measure actual accepted gaps in real traffic u Correlate accepted gap with other parameters to find relationships v Entry position v Maneuver type v Vehicle type v Waiting time v Weather u Use driver behavior results as design input to deployable IDS system (with DII)

Data Collection and Archival u 26 sensors at intersection u Radar, laser, image processing u Different data rates ( radar 10, laser 35, cameras 30 Hz) u Tracking software runs at deterministic 20 Hz u Estimates vehicle states using sensor data u “Snapshot” of intersection state at 10 Hz u Intersection state includes position, speed, lane, time to intersection of every vehicle in surveillance system coverage region

Data Collection and Archival

u Hardware v Central control computer Collects sensor data Calculates vehicle states at 10 Hz Sends vehicles states to data server v Image processor Processes images from the four cameras at the intersection Calculates vehicle positions v Data/web server computer Hub for accessing real time data Bridge between wired and wireless networks Web server to share status information and images Web Site v Intersection Data Acquisition System (iDAQ) Video capture board Captures four channels of MPEG layer 4 video Engineering data Removable hard drive bay

Data Collection and Archival u iDAQ v Receives images from four cameras v Digitizes and compresses to MPEG layer 4 video files v Recieves engineering data from Data Server (Ethernet) v Writes all video and data channels to removable SCSI disk drive

Data Processing u Hard drives couriered to the University every two weeks u Batch programs import data into database u Engineering data permanently stored, video files of interest stored, rest discarded u Data in raw format, needs to be processed to determine maneuvers u Creates intermediary databases v Contains Vehicles of Interest (VOI) v Vehicles accepting a gap v Zone and region location, times v Maneuvers assigned v Classification assigned (length, height) u Query program cross references database tables and produces reports

Definition of gap u Spatial database contains all relevant road features u Database divided into zones (entry regions) u Zones subdivided by regions(16 x 12 ft), within each lane u Sections assigned for each vehicle maneuver (right, left, straight) on each entry path u Time when vehicle leaves the designated region is when the gap is calculated u Gap associated with that point in time is used v Time for vehicle on major leg to arrive at the intersection if its speed and acceleration are held constant v Time gap used – normalizes speed v Primary gap – smallest gap to middle of intersection v Calculated for each lane u Captures gap when vehicle in harm’s way u Captures risk drivers accept

Definition of gap u Minor road vehicle (green) arrives at the intersection at time t o u Minor road vehicle in section 114 at t 1 u Major road vehicle (blue) is visible in section at t 1 u Minor road vehicle completely leaves section 113 at t 2 u Gap calculated at t2 u Gap time estimated by state of major road vehicle at t 2

Results u Data collected from February 1 to March 29, 2005 u 24/7 u Over 9,000 measured gaps

All Accepted Gaps Total Measured Gaps Gaps < 10sMean GapSTD50% Gap95 % Gap99% Gap All<10sAll<10sAll<10sAll<10sAll<10s u Gaussian, mean 10.2s, median 9.7s u 10 seconds chosen as upper limit for lower gap statistics u 53% gaps less than 10 seconds u Mean gap 7.0 s for accepted gaps < 10 seconds u 5% of drivers accepted a gap of 4.4 s or less u 1% of drivers accepted a gap of 3.1 s or less

Intersection Zone Analysis u Zones encompass entry ways into major leg traffic u Used to determine maneuver type u Determine whether gap selection related to region where maneuver originates

Intersection Zone Analysis u Zones 1 and 8 have significantly smaller mean accepted gap time than zones 2 and 7 u Zones 1 and 8 have smaller variance than zones 2 and 7 u Vehicles in zones 1 and 8 merge/cross south bound traffic on US52 u Major leg (US52) traffic volumes similar in both directions Time PeriodTotal Gaps Gaps < 10 s Mean GapSTD50% Gap95 % Gap99% Gap All<10sAll<10sAll<10sAll<10sAll<10s Zone Zone Zone Zone

Intersection Zone Analysis u Sample surveillance system data every 10 sec for number of vehicles within surveillance system on Hwy 52 u South 2.9 vehicle detected per sample u North 3.1 vehicles detected per sample u STD 2.8 for north bound u STD 3.4 for south bound u Signalized intersection in Cannon Falls, 8 miles north u No signalized intersections in Zumbrota Falls, 12 miles south

Gaps as a Function of Time of Day u Gaps decrease during the day, largest at night u Smallest gaps accepted in evening rush u Largest gaps accepted at night time u Mean gap < 10 sec similar, slightly smaller for evening rush Time PeriodTotal Gaps Gaps < 10 s Mean GapSTD50% Gap95 % Gap99% Gap All<10sAll<10sAll<10sAll<10sAll<10s Morning Rush Day Time Evening Rush Night Time

Gaps as a Function of Time of Day u Lowest traffic volume at 10 UTC (6 AM CST) u Highest traffic rate at 23 UTC (5 PM CST) u Smallest gaps occur with largest traffic volume u No night time effect, day/night mean gap time < 10 s same

Gaps for different vehicle maneuvers u Few left hand turns u Largest accepted gap for left hand turns followed by right u Smallest accepted gap for straight through maneuvers ManeuverTotal GapsGaps < 10 sMean GapSTD50% Gap95 % Gap99% Gap All<10sAll<10sAll<10sAll<10sAll<10s Straight Right Left

Gaps as a Function of Vehicle Classification u Four categories v Small passenger vehicles (motorcycles, sedans, small SUVs) v Large passenger vehicles (SUV, Pickups) v Small commercial vehicles (delivery trucks, dump trucks) v Large commercial vehicles (semi trucks) u No significant difference in accepted gap ClassificationTotal Gaps Gaps < 10 s Mean GapSTD50% Gap95 % Gap99% Gap All<10sAll<10sAll<10sAll<10sAll<10s Small Passenger Large Passenger Small Commercial Large Commercial

Gaps as a Function of Vehicle Classification u Larger vehicles take longer to leave region due to their length and lower acceleration capabilities u Gap definition ignores time to accelerate and leave the stopped region u Larger vehicles decided (gap selection) to take the gap before smaller vehicles u Gap/Risk acceptance the same

Waiting For a Gap – Stop Bar u Total time spent in zone 1 or 2, stop bars u Peak at 12 seconds u Chose waiting periods based on histogram peak v 5 – 12 s v 12 – 17 s v 17 – 25 s v 25 – 60s

Waiting for a Gap – Stop Bar u Mean gap largest for vehicles waiting the least amount of time (5 – 12) u Median (50%) gaps similar u < 10s mean gap was lowest for s wait, similar for other wait times Time Waiting for Gap (s) Total Gaps Gaps < 10 s Mean GapSTD50% Gap95 % Gap99% Gap All<10sAll<10sAll<10sAll<10sAll<10s 5 –

Waiting For a Gap – Cross Road (Median) u Half the vehicles spend less than 3.6s in cross roads u Time periods selected for analysis v 0 – 3 s v 3 – 5 s v 5 – 10 s v 10 – 60 s

Waiting for a gap – Cross Road (Median) u Mean accepted gap larger for shortest (0 – 3) and longest (10 – 60) wait times u < 10 s mean gap time similar, slightly smaller for longest wait u Vehicles that took the cross road as a one step maneuver (0 – 3 s) did not increase their risk Time in Median (s) Total GapsGaps < 10 sMean GapSTD50% Gap95 % Gap99% Gap All<10sAll<10sAll<10sAll<10sAll<10s 0 – – –

Gaps as a Function of Weather Conditions u ARWIS weather station located one mile north of intersection u Provides subsurface, surface and atmospheric data u Downloaded weather data nightly from a MNDOT web site u Visibility and precipitation rate was cross correlated with accepted gaps

Gaps as a Function of Weather Conditions - Visibility u Accepted gaps increased with decreasing visibility u < 10s mean gaps similar, similar risk u Speed on major leg decreased slightly as visibility decreased u Lower speed means larger gap time for same gap distance Visibility (m) Total Gaps Gaps < 10 s Mean GapSTD50% Gap95 % Gap 99% GapMean speed (m/s) All<10sAll<10sAll<10sAll<10 s All<10s

Gaps as a Function of Weather Conditions – Precipitation Rate u Precipitation rate cross referenced with accepted gap u Mean gap increases with increasing precipitation rate u Speed decreases slightly with precipitation u High precipitation rate has lowest < 10s gap, but highest overall mean accepted gap Precipitation Rate (cm/hr) Total Gaps Gaps < 10 s Mean GapSTD50% Gap95 % Gap99% Gap Mean Speed (m/s) All<10sAll<10sAll<10sAll<10sAll<10s – – –

Small Accepted Gap Analysis u Need metric to demonstrate effectiveness of IDS system u Crashes are rare at any one intersection over small time sample u Use small (unsafe) gaps (< 4 sec) as measure of poor gap selection u If percentage of small gaps decrease, system shows positive effect on gap selection u 3.2% of accepted gaps were less than 4 sec u Maneuver type v 67% of all maneuvers were straight v 86% of all small gaps were straight u Zone v Zone 1: 16% of total, 20% of small gaps v Zone 2: 25% of total, 8% of small gaps v Zone 7: 33% of total, 20% of small gaps v Zone 8: 24% of total, 48% of small gaps u Classification type had similar representation of small gaps compared to total number of gaps u Vehicles performing straight maneuver across south bound lane of highway 52 from the median (zone 8) had highest percentage of small accepted gaps

Conclusions u Mean accepted gap for all vehicles was 10.2s u Mean accepted gap for gaps < 10 s was 7.0s u 5% gap was 4.4 s, 1% gap was 3.1 s u Vehicles crossing/merging south bound lanes of Hwy 52 had significantly smaller accepted gap than vehicles crossing/merging north bound lanes u Due to inconsistent traffic patterns, signalized intersections in Cannon Falls u Accepted gaps smaller with increasing traffic rate u Smallest at evening rush hour, largest at night time u Straight maneuvers exhibited smallest accepted gap, followed by right turn then left turn u Little difference in accepted gap between different vehicle classes

Conclusions u Gap definition did not take into account time to accelerate past the stop bar, larger vehicles likely selected a bigger gap u At stop bar, mean accepted gap largest for vehicles waiting the least time. <10s gap smallest for 17 – 25 s wait. u At cross roads, mean accepted gap smallest for vehicles waiting the shortest time (0 – 3) and vehicles waiting the longest (10 – 60). Little difference for gaps < 10s. u Mean gaps increased with decreasing visibility. Less significant for gaps < 10s. Speed on main leg decreased slightly with lower visibility. u Mean gaps increased with increasing precipitation rate. Little difference for gaps < 10s for precipitation rate < 0.9 cm/hr. Smaller gap for 0.9 to 1.5 cm/hr. u Small gap analysis (< 4s) showed that straight maneuvers over represented. u Vehicles crossing south bound 52 from median had greatest percentage of small gaps u 3.2% of accepted gaps were less than 4 sec

Two Second Gap Video