pavemon: a gis web-based pavement monitoring...

ESRI UC 2014 | 1 | PAVEMON www.neu.edu/voters

This work was performed under the support of the U.S. Department of Commerce, National Institute of Standards and Technology, Technology Innovation Program, Cooperative Agreement Number 70NANB9H9012

PAVEMON: a GIS Web-based PAVEment MONitoring System

Salar Shahini Shamsabadi, Ming Wang and Ralf Birken Northeastern University


Outline • Motivation

o General: Road Infrastructure Problem o Specific: VOTERS Project

• PAVEment MONitoring system o Data Handling o Design and Architecture o Features and Parts

• Live Demo: PAVEMON

• Summary and Future Work

ESRI UC 2014 | 3 | PAVEMON www.neu.edu/voters www.neu.edu/votersESRI UC 2014 | 3 | PAVEMON

General Motivation

Road Infrastructure Problem


Conditions of US roads are poor and unsafe

Conditions of US roads are poor and unsafe

Personal experience (local roads) Professional organizations

Insufficient inspection coverage — Can’t fix it if don’t know a problem exists

Infrequent inspections — Can’t fix it based on 5 year snapshots

Repair prioritization based on outdated data — Not enough resources to fix everything at once — Can’t optimize impact and cost of repair

NEED: Mapped up-to-date

networkwide inspection results

www.infrastructurereportcard.org


Specific Motivation

VOTERS Project


Versatile Onboard Traffic Embedded Roaming Sensors

Goal • Monitor network-wide road

conditions on a frequent basis without stopping traffic.

Framework • Install acoustic, optical,

electromagnetic sensors on vehicles.

• Collect pavement-related features roaming through traffic and driving at normal speed


Specific Motivation

Implementation of a GIS system to visualize, analyze, provide easy access and enable effective decision making from the huge amounts of data pouring in after each survey of VOTERS vehicle

7


Size, Modalities and Geo-referencing

VOTERS Data


Outputs of each survey

Positioning Data • GPS Location of sensor mounted vehicles

• Collected every one second

• stored as text files.

• Only stream with geo-spatial columns



Sensors’ Raw Data • Microphone

• Tire Pressure Sensor

• Accelerometer

• Mm-wave Radar

• 50 KHz (50,000 data points per second)

• stored every 30 seconds as binary files

• Time synchronized with Positioning Data



Video Images • Collected every 1 meter

• stored as jpg files

• Distance triggered



Processed Data • Processed from individual sensors

• Pavement-related features

o Roughness, Friction, Distresses, etc.

• stored as txt files



Fused Data • Processed from multiple sensors

• Pavement-related features

• Overall Condition

• Distresses

• stored as txt files




List of all the layers

Layer Feature Shape Segments Source

FUSED

Pavement Condition Index (PCI) Polyline As small as 10meters Microphone DTPS

Road Features (Cracks, Patches, Potholes, Manholes) Symbol Where feature exists

Microphone DTPS mm-wave

PROCESSED

Mean Texture Depth (MTD) Polyline As small as 10meters Microphone

Roughness Index (IRI) Polyline As small as 10meters DTPS

Crack Density Piechart Where feature exists Camera

Pavement Layer Thickness 3D Every 1 cm GPR

Dielectric Constant 3D Every 1 cm GPR

VIDEO Mosaic Layers Mosaic Where feature exists Camera

Image Pop-ups Point Where feature exists Camera

Cracks Statistics Point Where feature exists Camera

POSITIONING Vehicle Location Point Where feature exists GPS

Vehicle Path Line Where feature exists GPS

RAW Raw Binary Streams Point Where feature exists All Sensors

NON-VOTERS

Manually Collected PCI Polyline Every street CDM Smith City of Boston

IRI of MA Highways Polyline Every street MassDOT

Traffic (AADT) and Load (ESAL) Polyline Every street LTPP Database

Climate and Extreme Events Polygon Geographic Regions NOAA


Design and Architecture

Data Processing and GIS Server


Data Handling

VOTERS Data Challenges • Proper Geo-referencing of data with different modalities • Huge amounts of data (TB) in each survey

o Demands automation • Road features in increments as small as 1 m • Time triggered and distance triggered datasets • Geo-referencing Images from camera

o Captured every 1 m, Source is not stationary

Current GIS Pavement Condition Maps • An entire road (from intersection to intersection) is one line

segment • All road features along that street appended to line



File Server After each survey is completed, raw data are uploaded automatically to the file server.

Data Structure Hierarchical, common meta data structure, binary raw data


Plugin Executor • For processing raw data, “plugins“

are defined. • Plugins: scripts that transform one

stream type (e.g. unprocessed data) to another stream type (e.g. feature extracted data).

• Plugin executor triggers processing of the survey data when they are available on the file server.



GIS Server • Data intake plugins: Feeds streams

into the work station • ArcGIS Desktop: Geo-references

streams and does spatial analysis • Oracle database: Stores data

streams from GIS software • Web adopter: Pushes data to web • Flex API: Consumes services from

the GIS server



PAVEment MONitoring System


PAVEment MONitoring system

PAVEMON

Demo

Preface: Available Layers

Exporting Practical Data and Statistics - Statistics (overall and customizable)

- Exporting of data from Database

- Pop-up menus

Images of Camera - Pop-ups

- Mosaics

Google-Street view for comparison

High resolution temporal data analysis

Paveman Toolbox - Condition Projection

- M&R Suggestions

- Prioritization based on budget

- Long-term Planner

3D Data Layers

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66 Miles

134 Miles

Above Critical Below Critical

Critical if PCI <55

Stats- Critical Level

Total: 200 miles (out of 600)


66 Miles

134 Miles

Above Critical Below Critical

Critical if PCI <55

Stats- Critical Level

Total: 200 miles (out of 600)

Poor

Serious Failed

Satisfactory

Fair

Good

Very Poor

Miles 10 20 30 40 50 60 70 80

Stats- PCI Classes

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PCI = 98 MTD = 0.7 IRI = 1.0


PCI = 98 MTD = 0.7 IRI = 1.0

PCI = 71 MTD = 1.3;IRI = 3.2


PCI = 98 MTD = 0.7 IRI = 1.0

PCI = 14 MTD = 1.7;IRI =12.4

PCI = 71 MTD = 1.3;IRI = 3.2


Summary of PAVEMON Features

• Monitoring 400,000+ images, 1TB+ of VOTERS data and GB of third party data

• Query raw or processed sensors’ data • Export road locations and characteristics • Navigate through time and observe changes • Examine quality of data through provided current pavement

images • Study relationship between pavement parameters • Paveman: A data driven customizable Pavement Management

System developed for decision making • Extract actionable information and features for doing the Right

Repair, at the Right Place, in the Right Time


Future Work

• Integrating VOTERS automated data acquisition system with PAVEMON, full automation from data acquisition on the field to data display on PAVEMON.

• Further analysis of 3D data layers.


Thank you !

Salar Shahini Shamsabadi: [email protected]

Ralf Birken: [email protected]

This work was performed under the support of the U.S. Department of Commerce, National Institute of Standards and Technology (NIST), Technology Innovation Program (TIP), Cooperative

Agreement Number 70NANB9H9012

Supporting Organizations

pavemon: a gis web-based pavement monitoring...

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