Evaluating Remotely Sensed Images For Use In Inventorying Roadway Infrastructure Features N C R S T INFRASTRUCTURE

The Problem DOT use of spatial inventory data –Planning –Infrastructure Management –Safety –Traffic engineering –Meet federal requirements (HPMS) Inventory of large systems costly – e.g., 110,000 miles of road in Iowa Current Inventory Collection Methods –Labor intensive –Time consuming –Disruptive to traffic –Dangerous (workers located on/near roadways)

Research Objectives Investigate use of remotely sensed images for collection of roadway inventory features Evaluate level of resolution required for various inventory features Identify feasible features for future automation Make recommendations

Research Approach Identify common inventory features Identify existing data collection methods Extract inventory features from aerial photos Performance measures –Feature identification –Accuracy of linear measurements –Positional accuracy Define resolution requirements Recommendations

Identify Common Inventory Features HPMS requirements Additional elements (Iowa DOT)  Number of signals at intersections  Number of stop signs at intersections  Type of area road passes through (residential, commercial, etc)  Number of business entrances  Number of private entrances  Railroad crossings  Intersection through width

Required HPMS Physical Inventory Features Shoulder Type Shoulder Width –Right and Left Number of Right/Left Turn Lanes Number of Signalized Intersections Number of Stop Intersections Number of Other Intersections Section Length Number of Through Lanes Surface/Pavement Type Lane Width Access Control Median Type Median Width Peak Parking

Data Collection Methods Manual (advantages/disadvantages)  low cost  visual inspection of road  accurate distance measurement  workers may be located on-road  difficult to collect spatial (x,y) Video-log/photolog vans (advantages/disadvantages)  rapid data collection  permanent record  difficult to collect spatial (x,y)  may interfere with traffic stream

Data Collection Methods GPS (advantages/disadvantages)  highly accurate (x,y,z)  can record elevation  time consuming  workers may be located on-road Traditional surveying (advantages/disadvantages)  highly accurate (x, y, z, distance)  time consuming

Pilot Study Ames, Iowa

Remote Sensing Datasets 2-inch dataset - Georeferenced 6-inch dataset - Orthorectified 2-foot dataset – Orthorectified 1-meter dataset – Orthorectified –Simulated 1-meter Satellite Imagery * not collected concurrently

Inventory Features Collected Through lanes  Number of lanes  Width Shoulder  Presence, type  Width Pedestrian islands Access  Private  Commercial/Industrial Adjacent land use Median  Presence, type  Width Crosswalks Left turn lanes  Presence  Length  Width Stopbar Signal  Structure  Width On-street parking  Presence  Type Intersection design

Extraction 6” resolution image

Performance Measures Feature Identification Accuracy of Linear Measurements Positional accuracy

Feature Identification Number of features identified in aerial photos versus ground truth e.g. only 44% of the time can the number of through lanes be correctly identified (24-inch resolution)

Can the Feature be Identified?

Feature Identification *** Observations is the number of features tested. Differences by datasets indicate a smaller available sample size

Linear Measurement Accuracy Linear features –Measured in the field using handheld DMI –Measured with 4 datasets Use of linear measurements –Turn lane width -- intersection capacity analysis –Driveway width – access management Recommended accuracies from NCHRP Report 430 –Lane lengths within ± 3.28 feet (± 39.4 inches) –Lane, median, and shoulder widths within ± feet (± 3.9 inches) NCHRP Report 430: Improved Safety Information To Support Highway Design

Thru Lane Width Error Left Turn Lane Length Error

Linear Measurement Accuracy Lane width, turn lane length, and driveway width measurement relied heavily on pavement marking Expect less error with better identifiers (i.e. length of raised median) Accuracy required depends on application

Positional accuracy 50 GPS points were collected for comparison –kinematic GPS –5mm to 10mm horizontal –4 cm vertical accuracy Compared to the same points located with the 4 datasets Root mean square (RMS)

NSSDA: Spatial accuracy test suggested by National Standard for Spatial Data Accuracy Results of RMSE and NSSDA Test (95 th Percentile)

Cost Comparison Aerial photos –Iowa DOT estimates $100/mile for images + in-house costs to orthorectify –1.5 hours in-house to locate 55 features – hour to measure turn left turn, 2 approach lanes, lane and median lengths, lane and median widths for 1 intersection (see david’s) Field manual data collection –1 hour to measure and record turn lane and median lengths & lane and median widths for 1 intersection in field not including (see david’s)

Cost Comparison GPS –Cost $1500 for 55 points w/ kinematic GPS from consultant –24 person hours  10.5 hours for 1 person  3 hours processing  All sites within 2 miles Videolog van –$35/mile to collect –How many miles can they collect per hour realistically, not including travel time to location –Processing time Manual see David’s Costs for on-road data collection can increase significantly when sites are located at distances from data collectors and equipment –2 hours for Iowa DOT Mandli van to reach Iowa City from Ames, Iowa

Conclusions Majority of inventory features studied could be identified in the 2-inch, 6-inch, and 24-inch datasets Ability to identify features in 1-meter dataset is significantly reduced If identified, most features could be located spatially and measured Positional accuracy and linear measurement accuracy varied by dataset Acceptability of positional/linear measurement accuracies depends on application

RS for Inventorying of Roadway Features Advantages Rapid field data collection Multiple uses of data Data can be shared among state, local, etc. Do not need to return to the field for missed items Can collect most inventory elements (depending on resolution) Easily integrated with GIS Rapid in-house data collection Disadvantages Costly for initial collection of images  (although multiple uses would decrease costs) Difficult to detect features such as signs

Applications Iowa DOT’s linear referencing system (LRS) Identification of passing zones

Iowa DOT’s LRS Iowa DOT is implementing a linear referencing system (LRS) Requires method to create accurate spatial representation of network for creation of datum Current accuracy requirements –Anchor points (nodes) – ± 3.28 feet –Anchor sections (links) – ± 6.89 feet –Business data located - ± feet

Data Collection Methods Tested by Consultants for Iowa DOT Anchor points –Kinematic GPS (reference dataset) –Heads-up digitizing of 24-inch orthophotos for coordinates (meets) –Heads-up digitizing of 6-inch orthophotos for coordinates (meets accuracy) –Project plans (did not meet) –Existing cartography (did not meet)

Data Collection Methods Tested by Consultants for Iowa DOT Anchor sections –Videolog van DMI (reference dataset) –Videolog van DGPS (did not meet) –Heads-up digitizing of 24-inch orthophotos for distances (did not meet) –Heads-up digitizing of 6-inch orthophotos for distances (meets accuracy) –Project plans (did not meet) –Existing cartography (did not meet)

ISU Research Team Results All datasets but 1- meter meet anchor point accuracy requirements In progress -- distance measurements –DMI (reference) –Roadware van –2-inch photos –6-inch orthophotos –24-inch orthophotos –1-meter orthophotos All datasets meet positional accuracy requirements for business data

Creation of Datum Using Aerial Photos Cartography centerline Heads-up digitized centerline for datum 6-inch resolution aerial photos

Passing Zones on Rural 2-lane Roadways Provide guidance to drivers as to whether the geometric layout of the roadway allows sufficient sight distance for a following vehicle to pass a slower moving one Are identified by pavement marking Inventory of passing zones useful for: –Safety analysis –Design –Evaluation of deficiencies –Capacity studies –Roadway maintenance and rehabilitation

Identification of Passing Zones Identification of changes in pavement marking for passing zones  Point feature to delineate changes in pavement marking — Street database centerline Begin and end point represented as points Attribute tables created Point features snapped to street centerline Distance from reference location calculated

Using Point Features and Attributes to Linearly Reference Passing Zones