Adding MIRE Attribution To The Enterprise Network Asset Data Model

Greg CiparelliTransportation Planner – Roadway Information Systems

Connecticut DOT – Bureau of Policy & Planning

Marc KratzschmarSenior Consultant

Bentley Systems

2

Roadway Information Systems

Roadway InventoryPhotolog Traffic Monitoring

Photographic InventoryGeometric Data

Pavement Condition Data

LRS/EXOR Development & MaintenanceOfficial Roadway Mileage

Roadway Characteristics - MIRE

Traffic Volume CollectionVehicle Classification

Turning Movement StudiesWeigh In Motion

HPMSField review of visual data

Office concatenation of calculated/technical data

HPMSRideability (IRI, Rutting, Faulting,

Cracking)Horizontal Curve/Grade

HPMSAnnual Average Daily Traffic % Truck (single unit, combo)

Vehicle Summary Data

Local Data Integration/CollectionRoad acceptance through ENG-29 process

Road Ownership/Status/Eligibility

Digital HiWAYFront ROW view (bi-directional)

Pavement Condition Imagery

Division of ResearchField data collection tool

ATLAS DevelopmentUCONN CTSRC Collaboration

3

3,733 miles

State Routes/Roads

17,394 miles

Local Roads

423 miles

State Ramps

Linear Referencing System (LRS)

4

Transportation Enterprise Database (TED)Geospatial Data

Composite Project Database (CPD)

CORE

OBLIG

SITE MGR

Traffic Signals

Bridges(InspectTech)

EXOR (LRS)

Linear Data

Web GIS

VIP (Maintenance)

No Thru TrucksSpeed Limit

Rumble Strips

Signs

State & Local Road Network Attribution

Functional Class

Pavement Configuration

Ownership

HPMS Database

Intersections

Peak Parking

Widening Obstcl/Ptntl

Database Linkages – TED & Exor

The MIRE provides a structure for roadway inventory data elements through the use of common consistent definitions and attributes.

August 2007 - FHWA released a report that listed roadway inventory and traffic elements critical to safety management, and proposed standardized coding for each, entitled Model Minimum Inventory of Roadway Elements (MMIRE).

That report has been revised and now includes over 205; becoming more of a comprehensive listing of elements necessary for safety management rather than a minimum listing. Minimum was dropped from the title to remove any implication that all elements were mandatory.

A revised version, MIRE 2.0, was released with a publication date of July 2017 in early 2018.

What Is MIRE?

By comparing the proposed MIRE elements to other databases, safety analysis tools, and guidance documents to identify common

elements and to ensure as much consistency as possible between MIRE and the other datasets.

How Was MIRE Developed?

Categories and Subcategories of MIRE

There are a total of 205 elements that comprise MIRE Version 2.0. The MIRE elements are divided

among three broad categories: roadway segments, roadway alignment, and roadway junctions. Each

category is then broken down further into a subcategory that identifies the associated data item

types.

Elements within MIRE can be concatenated from a variety of sources and databases, but for it to be most effective, adaptation to/utilization of the

prescribed attribution is recommended.

There is leeway for states to determine what data has the most utility and adjust MIRE to best fit their needs.

How Is MIRE Structured?

8

MIRE Requirements of

23 CFR 924.17

3 Tiers Based Upon Functional Classification and

Surface Type

Related to Road Segments and Road Junctions (Intersections &

Interchanges)

Determining Asset/Network Requirements

MIRE FDE

9LRS Node Base – 103,000 Potential Intersections

Creating Intersection & Approach Assets

Adding Structure & Functionality

CTDOT & CTSRC efforts to identify uniform

intersection locations and assign unique IDs

for consistent referencing

Utilizing LRS Structure – Developing Assets

Utilizing LRS Structure – Creating Int & App

Fully Developed

Attributable

Linear

Uniquely Identified

Intersection and

Approach Assets

Able to automate intersection and approach

grouping & creation

Automated script utilized compass bearing to create

Unique Approach ID

Approach ID Bearing can be utilized to create MIRE

attribute of Intersection Angle

All automated off the geometry of the network

Utilizing LRS Structure – Developing Attributes

13

MIRE - 205 Data Elements

Determining Asset/Network Attribution

14

• Fundamental Data Elements are required at different levels depending on functional classification/surface type

• HPMS elements with MIRE counterparts (Sample or Full Extent)

What data in the MIRE is required on a federal or state level?

• Collaborated with UCONN CTSRC to identify data needs/priorities

• Created weighted ranking based upon data necessity, availability, and difficulty of collection/maintenance

What data in the MIRE is required to perform safety analysis consistent with methods outlined in the Highway Safety Manual?

• Identifying data stewards and custodians is a complicated process both internally and externally

• Some roadway characteristic data on local roads has an authoritative review process within the state DOT offices

• Examples include: Functional Classification, Speed Limit, No Thru Trucks, Right Turn On Red Prohibitions

Who stewards the data on a state or local level? Is there state level stewardship of local road attributes?

• Streamlined project tracking will enable data stewards make necessary adjustments as changes occur

• Development of a collaborative process with local agencies is being initiated – stewardship on a local level

How do we capture changes to the road network & attributes that affect the MIRE data items ?

MIRE – Data Item Assessment

15

• Identified roughly half of the recommended elements that were desired and maintainable

Subset of MIRE/HSM Elements that are Needed for Advanced Safety Analysis

•Baseline inventory is the biggest step – maintenance procedures need to be established

Locate Where Data Exists and to What Extent – Develop Collection Methods

MIRE – Filtered/Prioritized Assessment

16

• Multiple assets in parallel – including location collection & attribution of new roads from field

• Support checking in/out of route information

• Required to sunset legacy Roadway Inventory System to keep LRS and Roadway data synchronized

• Resource intensive

• Research Project with UCONN CTSRC and Transcend Spatial Solutions

• Plan to explore other potential applications of collector – maintenance and asset management

• Comprehensive attribution and ease of use will be imperative

Roadway Information – MAVRIC Field Data Collection Tool (in Development)

• Intended as gap filler to start – not system of record – snapshot in time

• Initial effort for Passing/No Passing Zones

• Scalable for Pavement Markings, Guiderail, etc.

Roadway Information - Photolog – Digital HiWAY Data Collection Tool

• Geo-location of Project, Bridges, Signal Location Management

• Civil Integrated Management (CIM) – CADD to GIS, Project Data Capital Asset Capture/Management

Engineering - ATLAS/TED – Connecticut Asset Tracking & Location System

• MIRE – Safety Analyst/HSM Data Collection – filling gaps, establishing base inventories/attribution

UCONN - CTSRC Team – Intersection/Approach Attribution Collection Tool

• Culvert, Rights of Way, ITS / Signal Data

OIT Collection - ESRI Collector

CTDOT Data Collection Efforts & Tools

17

MAVRICMap Based Asset Collection

Allows for road network segment creation

GPS Trace feature to track asset location geospatially

Allows for geospatially accurate collection of off network assets to be referenced back to network location

Attribution is configurable

Field Data Capture Tool Development

18

MAVRIC ApplicationParallel Asset/Attribute Data Collection

Tracks on route with data collector movement

Able to modify multiple assets and/or attributes at once

Contains multiple views of roadway/asset data to best suit collector

Visual component of cross section and positioning assists in QA

Field Data Capture Tool Development

19

SQL Based ApplicationDeveloped by UCONN CTSRC

Intersection/Approach Data Collection

20

CSV LoaderIntersections & Approaches Linearly Represented

Intersection/Approach Data Population

Attributes of Int000001-093

Attributes of IntApp000003-093-200

21

Timeliness How quickly is a change in field conditions or status reflected in the data?

Accuracy How do we ensure that individual populated data fields are error-free?

Completeness How do we minimize the absence of data records & populate all applicable data fields?

Uniformity How do we ensure that all jurisdictions within the state are location referencing in the same manner and using the same definitions for data elements and attributes?

Integration How do we link databases utilizing common or unique identifiers?

Accessibility How do we ensure that the data is available and useable to all appropriate interested parties?

Authority Who programs road network or attribute changes on a state and local level or is responsible for maintaining state or local road data? Stewardship?

Challenges In Collaborative Data Integration

CTDOT LRS/TEDDevelopment &

Maintenance Efforts

CTDOT Assessment of Available State

Data & Desired/Required

Local Data

CTDOT Available & Developing

Data Collection & Maintenance

Tools

Assess Department/

Regional/Local Capabilities &

Resources

Identify Opportunities

for Collaboration & Integration (Internally & Interagency)

22

Recap – Data Collection & Integration

Short TermDepartment review of MIRE v 2.0

Department asset steward identification

Data Governance structure development

Increase use of applications and developing technologies (Imagery, COTS solutions, etc.)

Long TermExplore opportunities for integration and access to local agency supplied roadway attribution

Ability to harvest MIRE relevant asset and roadway information initiatives such as CADD to GIS or Image Feature Auto-Detection

HurdlesElement maintenance – an unmaintained data element quickly becomes unreliable, and eventually unusable

Coordination between responsible and affected parties

Obtaining buy-in & support for the collaborative nature of maintaining a dataset of this size and breadth

Next Steps For MIRE Integration

Roadway Inventory data is a key component of any safety data system and

critical to effective safety management. The use of

uniform elements and consistent nomenclature facilitates system linkages

that enable the use of cutting edge safety analysis tools and

methods.

LRS/MIRE And Additional Data Linkages

25

Thank You

Questions?

Greg CiparelliTransportation Planner – Roadway Information Systems

Connecticut DOT – Bureau of Policy & Planning

Marc KratzschmarSenior Consultant

Bentley Systems