Iowa State University Gainesville, Florida March 20, 2001

Iowa State University

Gainesville, FloridaMarch 20, 2001

N C R S TINFRASTRUCTURE

ISU/CTRE Projects

1. Access Management2. Collection of Inventory Elements


Project #1: Access Management

source: http://www.fhwa.dot.gov/////realestate/am_mich.pdf


The Problem

• One person dies every 13 minutes (all crashes)

• Economic Cost Crashes in US - $150.5 billion/year (1994)Congestion – $72 billion/year (For 68 major

Metropolitan areas in U.S.A)

• System-wide crash data now available• No comprehensive inventory available• On-road data collection is resource

intensive


What Is Access Management?

“Access Management is the process that provides access to land development while simultaneously preserving the flow of traffic on the surrounding road system in terms of safety, capacity, and speed”.

(Source: Federal Highway Administration,United States Department of Transportation)


Statistical Relationship Between Access Density and Crash Rates

Crashes Versus Commercial Driveways

y = 0.4102x + 3.0947

R2 = 0.8408

0

5

10

15

20

25

30

35

40

45

50

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00Commercial Driveways Per Mile

Cra

shes

Per

Mile

There is evidence of a strong relationship betweencommercialdrivewaydensity andcrashes


There isa strongcorrelationbetweenaccess densityand rear-endcollisions


Safety Benefits: Iowa Case Studies

• Seven Iowa case studies were made on a “before and after” basis

• Case studies show nearly a 40 percent average reduction in accident rates after projects incorporating access management treatments were completed

0

1

2

3

4

5

6

7

Accident Rate (perMVMT)

BeforeAfter


Safety Benefits: Crash Reduction By Type For Iowa Case Studies

0 50 100 150 200 250

Rear End

Left/Broadside

Right Angle

Other

Total

Before

After


Research Problem

• Access management data collection methods time consuming resource intensive process.

• Lack of quantitative, comprehensive access data makes systematic identification of locations that would benefit from improved access management difficult, if not impossible.


Research Approach

• Survey DOTs• Perform quantitative analysis• Develop qualitative method• Evaluate qualitative method• Make recommendations


Survey of State DOTs

• 10 state DOTs (8 responded) Florida -- Kansas South Dakota -- Wisconsin Michigan -- Colorado Oregon -- Iowa

• Access management data elements collected

• Method of collecting data



• None maintain a comprehensive database of access related data elements

• Usually collect as needed (corridor level)• Several

are in the process of developing one or have indicated an inclination towards maintaining

one.



• None maintain a comprehensive database of access related data elements

• Severalare in the process of developing one orhave indicated an inclination towards maintaining

one.

DOT Data Collection Method

Comments

Florida Video logging and surveying

Driveway locations are collected if part of an improvement project or permit

Kansas Location reference system and GPS receivers

KDOT is investigating the option of utilizing aerial imagery for data validation and display

South Dakota

Plan sheets from construction projects

Aerial photography is used for planning and project development, but not as a data collection tool for access management

Wisconsin Photo logs and from driveway permits

Aerial photography is only used for route layout and design, but not as a data collection tool for access management

Michigan Video logs Collects as needed

Colorado Video logs, aerial photos

Vertical and oblique aerial imagery for access management but do not store.

Oregon Video logs, Manual Data collection, Aerial photos


Perform quantitative analysis

• Select statistical access management/crash model Other research organizations Crash rate are ƒ(#commercial

driveways, median type, etc.)

• 10 study segments US 69 corridor in the city of Ames, IA

**Crash rate is # of crashes per million vehicles or per million vehicle miles



• Identify access-management related features required by crash models

• Extract access-management related elementsEvaluate aerial photographs at

different resolutionsMake recommendations on level

required


Data

• Aerial Images Iowa DOT (6-inch pixel, panchromatic) Story county engineer’s office (2-foot pixel,

panchromatic)1 meter

• Crash Data Iowa Department of Transportation

• Attributed Road networkAADTSpeed Limit


Access Related Data Elements

• Access roads Presence Configuration

• Driveways Number Dimensions Frequency Continuity Vertical grade

• Medians Type Length

• Turn lanes Length presence

• Intersections Proximity Frequency


Data Extraction


Identifying Medians

• Look for object markers along the center of the Road.

• Object markers are an important source of identifying the type and length of raised medians

• Pavement markings• Depressed medians can

be identified with ease as most of them are covered with Vegetation


Identifying Driveways

• Sharp difference in shade from the surrounding area

• Cuts along the curb• Vehicular movement captured

at the time of taking the photograph and parked vehicles may also be used as a source to identify driveway entrances

• Problems Tree Cover (Dense

Vegetation) Several close driveways

appear as one



• Calculate baseline crash rates for each location


Develop qualitative method

• Establish method to rank locations using aerial photographs according to “perceived” level of access management

• Such as 1 – good access management 2 – average access management 3 – poor access management


Develop qualitative method

• Define characteristics of ranking category, i.e. 1 = good

o Defined by few driveways, presence of medians

• Get expert input (multiple assessors)• Compute descriptive statistics for

assigned scores (mean, deviation)

• Develop qualitative crash model


Evaluate qualitative method

• Compare crash rates for quantitative versus qualitative

• Is qualitative “good” enough?


Cost Analysis

• Compute cost of data collection and database development tasks on a unit basis

• Extrapolate for systematic analysis• Compare cost of quantitative and

qualitative methods, at various scales


Anticipated Results (late April)

• Recommendations for resolution required for quantitative assessment (6-inch)

• Recommendations for resolution and methods required for qualitative evaluation (1-meter)

• Comparison of model performance using quantitative vs. qualitative

• Cost benefit assessment


Project #2: Collection of Inventory Elements


The Problem/Opportunity

• DOT use of spatial data Planning Infrastructure Management Traffic engineering Safety, many others

• Inventory of large systems costly e.g., 110,000 miles of road in Iowa


Research Objective

• Can remote sensing be used to collect infrastructure inventory elements?

• What accuracy is possible/necessary?

• Cost effective?


Research Approach

• Identify common inventory features • Identify existing data collection methods• Use aerial photos of different resolutions to

extract inventory features • Statistical comparison• Define resolution requirements• Benefit/cost analysis• Recommendations


Identify common inventory features

• Requirements of Highway Performance Monitoring System requirements

• Survey States LRS requirements Pavement management system Data for planning and design functions Highway needs studies Safety studies


Traditional Data Collection Methods

• Field data collection GPS traditional surveyingmanual

• Video-log van• Aerial photography


Datasets

• 2-inch dataset• 6-inch dataset• 2-foot dataset• 1-meter dataset

* not collected concurrently


Pilot Study Locations


Statistical comparison

• Percent Recognition• Accuracy• Between operator variability


Percent Recognition

• Performance measure• Number of features recognized in

aerial photos versus ground truth e.g. 90% of driveways can be

identified using 6-inch resolution photos


Percent Recognized

Description Photo Ground DOR Photo Ground DORSignals 42 44 95% Cannot be identifiedRailroad Crossings 4 4 100% 3 4 75%Number of lanes between intersections 47 47 100% 28 47 60%Number of sidewalks 41 41 100% 37 41 90%Number of Railroad tracks at crossings 7 7 100% 7 7 100%Number of bridges 2 2 100% 2 2 100%

6 inch 24 inch


Percent Recognition (Presence of Medians)

• 2-foot images Identified 5 of the 9

cases where medians were present (55.6%)

4 not recognizedCould not identify type

• 6-inch images correctly Identified 9 of the 9

casesCorrectly identified type

of median 7 out of 9

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2-foot 6-inch


Percent Recognition (surface type)

2-foot: pavement type was identified 0% of the time

6-inch: pavement type was identified 100% of the time


Accuracy

• Required accuracy depends on application

• Positional accuracy• Linear measurements

lane width, length of turning lane, etc. measure from aerial photos vs. ground

truth


Positional Accuracy

• Located versus actual position• RS vs.GPS• Collected 50 points with kinematic GPS• Centimeter accuracy• Compared to 4 datasets

Selected same 50 points on photos Latitude/longitude

• Root mean square (RMS)


RMS

6-inch

rms = 2.3

3.9 feet at 95th confidence interval

2-foot

rms = 3.0

5.3 feet at 95th confidence interval


Accuracy

• Linear Measures• On-road collected/measured

Driveway widths Median length


Between operator variability

• Amount of variability between different operators in selecting and locating a feature’s position

• Measure of operator input error


Comparison of Methods

• Types Field (kinematic GPS) RS Video log

• Cost• Advantages/Disadvantages


Field Data Collection (Cost)

• Cost $1500 for 50 points w/ kinematic GPS for services from engineering co.

• Included: 2 days x 2 people for

field data collection 1 day x 1 person for

data reduction


Field Data Collection (cost)

• Also required 5 days x 1 person (ISU student): Mission planning (select locations, select points) integrate data into GIS Accompany field crew

• Total cost: $1500 engineering services $600 for ISU student effort $2100 total


RS Data Collection Cost

• Cost $6000 for 2 miles of orthophotos (estimate from commercial source)

• Iowa DOT estimates $100 per mile for photos + in-house costs to ortho-rectify


RS Data Collection Cost• Required 1 day x 1 person (ISU student) for 2 miles:

Obtain existing photos from DOT Set up photos in ArcView Select points and set up database

• Estimated Cost w/ Aerial Services for 2 miles (50 points) $6000 for photos from commercial service $150 for ISU student effort $6150 total

• Actual Cost to project team for 2 miles (50 points) $0 for photos (already available at DOT, 6-inch) $150 for ISU student effort $150 total


Video Log

• Cost not yet estimated


Costs

GPS Aerial Video Log

$2,100 $150 to $6,150

Cost not yet available


Advantages/Disadvantages to Data Collection Methods


Field Data Collection Advantages

• Centimeter accuracy or better

• Can do visual inspection

• 3-D data (x,y,z)• Easily integrated

with GIS


Field Data Collection Disadvantages

• Missed data entails new trip

• Data collectors on/near busy roads

• Difficult to collect certain dataHorizontal curvatureRoadway width


Video-Log Van• GPS• Video


Video-Log Van• Advantages

Rapid collection of data Multiple types of data collected DOT’s may already have in-house

• Disadvantages Missed data entails new trip Data collection on-road may interfere w/ traffic Data difficult to use or share among agencies Not easily integrated with GIS Cannot collect

elevation data Horizontal curvature


RS Data Collection Advantages

• Multiple uses of data• Data can be shared among state, local, etc.

barring institutional and license agreements with data providers

• Data can be collected fairly rapidly• Can “go” back to data• Can collect most inventory elements

(depending on resolution)• Can get elevation data with certain types• Easily integrated with GIS


RS Data Collection Disadvantages

• Costly for initial data collection

• May not be able to detect certain features

• Difficult to establish elevation

Photo source: http://www.horizonsinc.com/page7.html


Interim Conclusions

• Positional accuracy of both 2-foot and 6-inch may be adequate for most inventory data elements

• Percent recognition is limiting factor for 2-foot

• Assume applicable to 1 meter as well


Year 1 Deliverables

• 3 abstracts submitted -- GIS-T, April Washington DC/ 2 accepted

• 2 abstracts submitted to student paper contest/ 2 accepted

• 1 abstract submitted -- Second International Symposium on Maintenance and Rehabilitation of Pavements and Technological Control, July, Auburn, Alabama/ 1 accepted

Year 2


Projects

• NCSRT-I/Iowa DOTTask 0: write cookbooks and participate in

international efforts (host late spring meeting?)Task 1: track Iowa DOT experience with LIDAR

for road designTask 2: evaluate LIDAR/IFSAR vs

photogrammetry for ongoing preliminary design


Projects (cont.)

• Midwest Transportation Consortium/Iowa DOTObtain LIDAR/IFSAR products and Satellite/aerial

digital ImageryTask 1: impact of as built road environment and off

system characteristics (clutter, site distance and other road features) on aging population

Task 2: pavement “distortions” and drainage impact on pavement performance

Task 3: watershed/terrain modeling for flood flow prediction/impact on surety of bridges and culverts


Project Alternates

• Identification of highway features related to high crash locations (curve identification and measurement of radius/superelevation, etc.)

• Hybrid machine/manual update of R/W centerline


Alt. Project #1

• ID curves and measure• Inputs: cartography, GPS van tracks, USGS

DOQQ, satellite• Methods: segment bearing, segment length,

officer ID, visual ID, closed form series expansion for radius calculation from chord

• Use: attribute database using LRS, correlate crash history with curve metrics


Alt. Project #2

• Start with existing centerline• Obtain statewide imagery (sample only for this

project, e.g., county)• Filter to ID “potential roads”• Buffer existing centerline and remove

proximate “potential roads”, leaving “potential new roads”


Alt. Project #2





Alt. Project #2





Alt. Project #2





Alt. Project #2

• Update centerline and populate only those attributes

• Benefits: uses best of machine and human, eliminates need to conflate entire database, focuses only on changes


Questions/Suggestions?

Documents

Iowa State University Gainesville, Florida March 20, 2001