Results of IDS Rural Intersection
Data Collection Lee AlexanderPi-Ming Cheng
Max DonathAlec GorjestaniArvind Menon
Bryan NewstromCraig Shankwitz
April 20, 2005
Outline Purpose Data collection and archival Data processing Definition of gap Results
General accepted gap analysis Intersection zone analysis Gaps as a function of time of day Gaps for different vehicle maneuvers Gaps as a function of vehicle classification Waiting for a gap Gaps as a function of weather conditions Small accepted gap analysis
Conclusions
Purpose Determine driver behavior at intersection Measure actual accepted gaps in real traffic Correlate accepted gap with other parameters
to find relationships Entry position Maneuver type Vehicle type Waiting time Weather
Use driver behavior results as design input to deployable IDS system (with DII)
Data Collection and Archival
26 sensors at intersection Radar, laser, image processing Different data rates ( radar 10, laser 35,
cameras 30 Hz) Tracking software runs at deterministic 20 Hz Estimates vehicle states using sensor data “Snapshot” of intersection state at 10 Hz Intersection state includes position, speed,
lane, time to intersection of every vehicle in surveillance system coverage region
Data Collection and ArchivalSensor Station
1
Sensor Station
2
Sensor Station
3
Sensor Station
n
Sensor Collector
Tracker
Data Server
Laptops with Visualization Software
Kallman filter based tracking of all vehicles in intersection
Collects data from all
intersection sensors Serves all
engineering data to the wired and wireless network
Data Collection and Archival Hardware
Central control computer• Collects sensor data• Calculates vehicle states at 10 Hz• Sends vehicles states to data server
Image processor• Processes images from the four cameras at the intersection• Calculates vehicle positions
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
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
iDAQ
Data Server
Removable Hard Drive
Tracked Targets
DataGap Data
Raw Sensor Data
Sensor Status Data
Database "friendly "
ASCII files
Intersection Data
Acquisition System
Hard drive removed every two weeks and take by courrier to the Intelligent
Vehicles Lab
Engineering Data
C
Visible Camera 1
C
Visible Camera 2
C
IR Camera 2
C
IR Camera 1
Video File x 4
iDAQ Receives images from
four cameras Digitizes and
compresses to MPEG layer 4 video files
Recieves engineering data from Data Server (Ethernet)
Writes all video and data channels to removable SCSI disk drive
Data Processing Hard drives couriered to the University every
two weeks Batch programs import data into database Engineering data permanently stored, video
files of interest stored, rest discarded Data in raw format, needs to be processed
to determine maneuvers Creates intermediary databases
Contains Vehicles of Interest (VOI) Vehicles accepting a gap Zone and region location, times Maneuvers assigned Classification assigned (length, height)
Query program cross references database tables and produces reports
Removable Hard Drive
Tracked
Targets
Table
Gap
Data
Table
Raw
Sensor
Table
Sensor
Status
Table
Hwy 52 Database
ARWIS
Weather
Table
Tracked
Targets
Table
Gap
Data
Table
Raw
Sensor
Table
Sensor
Status
Table
Hwy 52 Database
ARWIS
Weather
Table
VOI
Intermetiary
Results
Table
Selected
Gaps
Table
Batch program copies data from removable
hard drive and inserts into database stored on
Terrabyte server
Results File
Batch program finds vehicles
entering intersection from minor road (Vehicles of Interest (VOI ) )
and consolidates tracking information to new tables
GUI program takes user
input , queries the database and creates table of gaps
selected by drivers on
minor road . Produces file with compiled results .
Query Program
Definition of gap Spatial database contains all relevant road features Database divided into zones (entry regions) Zones subdivided by regions(16 x 12 ft), within each lane Sections assigned for each vehicle maneuver (right, left, straight)
on each entry path Time when vehicle leaves the designated region is when the gap is
calculated Gap associated with that point in time is used
Time for vehicle on major leg to arrive at the intersection if its speed and acceleration are held constant
Time gap used – normalizes speed Primary gap – smallest gap to middle of intersection Calculated for each lane
Captures gap when vehicle in harm’s way Captures risk drivers accept
t0
t1
t2
Definition of gap Minor road vehicle (green)
arrives at the intersection at time to
Minor road vehicle in section 114 at t1
Major road vehicle (blue) is visible in section 10174 at t1
Minor road vehicle completely leaves section 113 at t2
Gap calculated at t2 Gap time estimated by state of
major road vehicle at t2
Results
Data collected from February 1 to March 29, 2005
24/7 Over 9,000
measured gaps
All Accepted Gaps
Total Measured
Gaps
Gaps < 10s Mean Gap STD 50% Gap 95 % Gap 99% Gap
All <10s All <10s All <10s All <10s All <10s
9108 4808 10.2 7.0 4.1 1.9 9.7 7.2 4.4 3.8 3.1 2.8
Gaussian, mean 10.2s, median 9.7s 10 seconds chosen as upper limit for
lower gap statistics 53% gaps less than 10 seconds Mean gap 7.0 s for accepted gaps <
10 seconds 5% of drivers accepted a gap of 4.4
s or less 1% of drivers accepted a gap of 3.1
s or less
Intersection Zone Analysis
Zone 7
Zone 8
Zone 1
Zone 2
Zones encompass entry ways into major leg traffic
Used to determine maneuver type
Determine whether gap selection related to region where maneuver originates
Intersection Zone Analysis Zones 1 and 8 have significantly smaller
mean accepted gap time than zones 2 and 7
Zones 1 and 8 have smaller variance than zones 2 and 7
Vehicles in zones 1 and 8 merge/cross south bound traffic on US52
Major leg (US52) traffic volumes similar in both directions
Time Period Total Gaps
Gaps < 10 s
Mean Gap STD 50% Gap 95 % Gap 99% Gap
All <10s All <10s All <10s All <10s All <10s
Zone 1 1466 980 8.6 7.1 2.8 1.8 8.6 7.3 4.3 4.0 2.9 2.8
Zone 2 2307 616 12.9 7.7 4.1 1.7 12.9 8.1 6.3 4.1 3.9 3.1
Zone 7 2888 1449 10.6 7.3 4.2 1.8 10.0 7.5 4.6 4.0 3.5 3.0
Zone 8 2106 1592 7.8 6.6 2.8 1.9 7.5 6.6 3.7 3.5 2.7 2.7
Intersection Zone Analysis Sample surveillance system data
every 10 sec for number of vehicles within surveillance system on Hwy 52
South 2.9 vehicle detected per sample
North 3.1 vehicles detected per sample
STD 2.8 for north bound STD 3.4 for south bound Signalized intersection in Cannon
Falls, 8 miles north No signalized intersections in
Zumbrota Falls, 12 miles south
Gaps as a Function of Time of Day
Gaps decrease during the day, largest at night
Smallest gaps accepted in evening rush
Largest gaps accepted at night time
Mean gap < 10 sec similar, slightly smaller for evening rush
Time Period Total Gaps
Gaps < 10 s
Mean Gap STD 50% Gap 95 % Gap 99% Gap
All <10s All <10s All <10s All <10s All <10s
Morning Rush 1630 750 10.9 7.2 4.2 1.8 10.4 7.4 4.6 4.0 3.7 3.0
Day Time 4583 2464 10.1 7.1 4.0 1.8 9.6 7.3 4.3 3.8 3.1 2.9
Evening Rush 2313 1360 9.7 6.9 4.1 1.9 9.1 7.0 4.2 3.7 3.1 2.6
Night Time 582 234 11.3 7.2 4.3 1.8 11.0 7.5 4.6 3.9 3.4 3.0
Gaps as a Function of Time of Day
Lowest traffic volume at 10 UTC (6 AM CST)
Highest traffic rate at 23 UTC (5 PM CST)
Smallest gaps occur with largest traffic volume
No night time effect, day/night mean gap time < 10 s same
Gaps for different vehicle maneuvers
Few left hand turns Largest accepted gap for
left hand turns followed by right
Smallest accepted gap for straight through maneuvers
Maneuver Total Gaps Gaps < 10 s Mean Gap STD 50% Gap 95 % Gap 99% Gap
All <10s All <10s All <10s All <10s All <10s
Straight 6104 3724 9.4 6.9 3.9 1.9 8.8 7.1 4.1 3.7 3.0 2.8
Right 2945 1064 11.8 7.5 4.1 1.8 11.5 7.8 5.3 4.2 3.6 2.9
Left 59 20 12.7 6.8 5.0 1.7 13.1 6.8 4.4 4.2 4.2 4.2
Gaps as a Function of Vehicle Classification
Four categories Small passenger vehicles
(motorcycles, sedans, small SUVs)
Large passenger vehicles (SUV, Pickups)
Small commercial vehicles (delivery trucks, dump trucks)
Large commercial vehicles (semi trucks)
No significant difference in accepted gap
Classification Total Gaps
Gaps < 10 s
Mean Gap STD 50% Gap 95 % Gap 99% Gap
All <10s All <10s All <10s All <10s All <10s
Small Passenger 2537 1384 10.1 7.1 4.0 1.9 9.5 7.3 4.3 3.8 3.0 2.9
Large Passenger 4479 2298 10.3 7.0 4.2 1.9 9.8 7.2 4.3 3.8 3.3 2.9
Small Commercial 757 403 10.1 7.0 4.1 1.9 9.6 7.2 4.4 3.6 2.7 2.6
Large Commercial 1280 697 10.1 7.1 4.0 1.8 9.6 7.3 4.4 3.9 3.3 2.9
Gaps as a Function of Vehicle Classification
Larger vehicles take longer to leave region due to their length and lower acceleration capabilities
Gap definition ignores time to accelerate and leave the stopped region
Larger vehicles decided (gap selection) to take the gap before smaller vehicles
Gap/Risk acceptance the same
Waiting For a Gap – Stop Bar Total time spent in
zone 1 or 2, stop bars
Peak at 12 seconds Chose waiting
periods based on histogram peak 5 – 12 s 12 – 17 s 17 – 25 s 25 – 60s
Waiting for a Gap – Stop Bar Mean gap largest for
vehicles waiting the least amount of time (5 – 12)
Median (50%) gaps similar
< 10s mean gap was lowest for 17-25 s wait, similar for other wait times
Time Waiting for Gap (s)
Total Gaps
Gaps < 10 s
Mean Gap STD 50% Gap 95 % Gap 99% Gap
All <10s All <10s All <10s All <10s All <10s
5 – 12 219 127 9.9 7.0 4.2 1.8 8.8 7.4 4.0 3.7 1.7 1.7
12 - 17 388 245 9.3 7.1 3.6 1.8 8.8 7.3 4.2 3.9 3.2 3.1
17 - 25 274 157 9.5 6.7 3.9 1.9 9.1 6.9 4.2 3.7 2.8 2.8
25 - 60 213 138 9.2 7.0 3.6 1.9 8.8 7.2 4.3 4.0 1.9 1.9
Waiting For a Gap – Cross Road (Median)
Half the vehicles spend less than 3.6s in cross roads
Time periods selected for analysis 0 – 3 s 3 – 5 s 5 – 10 s 10 – 60 s
Waiting for a gap – Cross Road (Median)
Mean accepted gap larger for shortest (0 – 3) and longest (10 – 60) wait times
< 10 s mean gap time similar, slightly smaller for longest wait
Vehicles that took the cross road as a one step maneuver (0 – 3 s) did not increase their risk
Time in Median (s)
Total Gaps Gaps < 10 s Mean Gap STD 50% Gap 95 % Gap 99% Gap
All <10s All <10s All <10s All <10s All <10s
0 – 3 202 139 8.9 7.1 3.5 1.9 8.6 7.2 4.1 4.0 2.8 1.7
3 – 5 416 244 9.7 7.0 3.9 1.8 9.1 7.2 4.2 3.7 3.2 3.1
5 – 10 247 142 9.7 7.1 3.7 1.8 9.1 7.3 4.3 3.9 2.7 1.9
10 - 60 240 148 9.3 6.8 3.9 1.9 8.7 7.0 4.0 3.3 2.3 2.1
Gaps as a Function of Weather Conditions
ARWIS weather station located one mile north of intersection
Provides subsurface, surface and atmospheric data
Downloaded weather data nightly from a MNDOT web site
Visibility and precipitation rate was cross correlated with accepted gaps
Gaps as a Function of Weather Conditions - Visibility
Accepted gaps increased with decreasing visibility < 10s mean gaps similar, similar risk Speed on major leg decreased slightly as visibility
decreased Lower speed means larger gap time for same gap
distance
Visibility (m)
Total Gaps
Gaps < 10 s
Mean Gap STD 50% Gap 95 % Gap
99% Gap Mean speed(m/s)All <10s All <10s All <10s All <10
sAll <10s
1200+ 4138 2340 9.9 7.1 4.0 1.9 9.3 7.2 4.3 3.8 3.0 2.8 30.3
900-1200 1729 906 10.2 7.0 4.2 1.9 9.8 7.2 4.2 3.6 3.0 2.6 29.7
500-900 723 375 10.3 6.9 4.3 1.8 9.8 7.0 4.2 3.9 3.3 3.0 29.2
0-500 2164 1013 10.8 7.2 4.2 1.8 10.3 7.4 4.7 4.1 3.5 2.8 29.4
Gaps as a Function of Weather Conditions – Precipitation Rate
Precipitation rate cross referenced with accepted gap Mean gap increases with increasing precipitation rate Speed decreases slightly with precipitation High precipitation rate has lowest < 10s gap, but
highest overall mean accepted gap
Precipitation Rate (cm/hr)
Total Gaps
Gaps < 10 s
Mean Gap STD 50% Gap 95 % Gap 99% Gap Mean Speed(m/s)All <10s All <10s All <10s All <10s All <10s
0 8044 4247 10.2 7.1 4.1 1.9 9.7 7.3 4.4 3.8 3.1 2.8 30.3
0.01 – 0.25 343 193 10.0 7.1 4.2 1.9 9.4 7.3 4.4 4.0 2.9 2.3 29.7
0.25 – 0.9 193 105 10.5 7.1 4.6 1.8 9.5 7.2 4.6 4.3 3.5 2.7 28.7
0.9 – 1.5 258 130 10.6 6.6 4.9 1.8 10.0 6.7 4.1 3.9 3.3 3.0 29.5
Small Accepted Gap Analysis Need metric to demonstrate effectiveness of IDS system Crashes are rare at any one intersection over small time sample Use small (unsafe) gaps (< 4 sec) as measure of poor gap selection If percentage of small gaps decrease, system shows positive effect on gap
selection 3.2% of accepted gaps were less than 4 sec Maneuver type
67% of all maneuvers were straight 86% of all small gaps were straight
Zone Zone 1: 16% of total, 20% of small gaps Zone 2: 25% of total, 8% of small gaps Zone 7: 33% of total, 20% of small gaps Zone 8: 24% of total, 48% of small gaps
Classification type had similar representation of small gaps compared to total number of gaps
Vehicles performing straight maneuver across south bound lane of highway 52 from the median (zone 8) had highest percentage of small accepted gaps
Conclusions Mean accepted gap for all vehicles was 10.2s Mean accepted gap for gaps < 10 s was 7.0s 5% gap was 4.4 s, 1% gap was 3.1 s Vehicles crossing/merging south bound lanes of Hwy 52 had
significantly smaller accepted gap than vehicles crossing/merging north bound lanes
Due to inconsistent traffic patterns, signalized intersections in Cannon Falls
Accepted gaps smaller with increasing traffic rate Smallest at evening rush hour, largest at night time Straight maneuvers exhibited smallest accepted gap, followed by
right turn then left turn Little difference in accepted gap between different vehicle classes
Conclusions Gap definition did not take into account time to accelerate past the stop
bar, larger vehicles likely selected a bigger gap At stop bar, mean accepted gap largest for vehicles waiting the least time.
<10s gap smallest for 17 – 25 s wait. 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.
Mean gaps increased with decreasing visibility. Less significant for gaps < 10s. Speed on main leg decreased slightly with lower visibility.
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.
Small gap analysis (< 4s) showed that straight maneuvers over represented.
Vehicles crossing south bound 52 from median had greatest percentage of small gaps
3.2% of accepted gaps were less than 4 sec
Two Second Gap Video