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