Connecticut Department of Transportation

Connecticut’s 

Transportation 

Enterprise Data

SAFETY DATA CASE STUDY 

FHWA-SA-22-008 

Federal Highway Administration Office of Safety 

Roadway Safety Data Program 

http://safety.fhwa.dot.gov/rsdp
ii SAFETY DATA CASE STUDY FHWA-SA-22-008

Notice 

This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

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iii SAFETY DATA CASE STUDY FHWA-SA-22-008

1. Report No.

FHWA-SA-22-008

2. Government Accession 

No.

3. Recipient's Catalog No.

 

4. Title and Subtitle

Connecticut’s Transportation Enterprise Data

5. Report Date

October 2022

6. Performing Organization Code

7.Author(s) 

Michael Dunn and Ian Hamilton

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

940 Main Campus Drive 

Raleigh, NC 27606 

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D-00052

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Case Study, January 2020-November

2022 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Jerry Roche. Sarah Weissman Pascual provided editorial review and support for 

technical content.

16. Abstract

This case study highlights the Connecticut Department of Transportation’s (CTDOT’s) recent advancements in its 

enterprise data systems and processes. The Moving Ahead for Progress in the 21st Century Act (MAP-21) established 

several requirements to help States advance their roadway safety data development and analysis capabilities. Two key 

requirements for States were to: 1) collect Model Inventory of Roadway Elements (MIRE) Fundamental Data Elements 

(FDE) data for all public roads, and 2) develop safety data management systems to support the Highway Safety 

Improvement Program, Strategic Highway Safety Plan, and data driven safety analysis (DDSA) and evaluation. The need 

to meet Federal requirements and develop an enterprise-level approach to safety analysis on all public roads drove many of 

the recent advancements in CTDOT’s data infrastructure. CTDOT created the Transportation Enterprise Data (TED) 

warehouse to meet the need for enterprise data specifically supporting robust safety management, but also meeting other 

critical department data analysis needs. The safety data systems are managed under formal data governance policies 

expressed in the Data Governance Charter drafted by CTDOT alongside implementation of the TED warehouse. These 

advancements have helped CTDOT scale its data infrastructure and meet emerging needs for robust roadway safety 

management on all Connecticut roads.

17. Key Words: 

Data governance, enterprise data, TED, linear 

referencing system, capability maturity model

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

 18 

22. Price

Technical Documentation Page
iv SAFETY DATA CASE STUDY FHWA-SA-22-008

Acronyms 

Acronym Description

AASHTO American Association of State Highway and Transportation Officials

ARNOLD All Roads Network of Linear Referenced Data

CRSMS Connecticut Roadway Safety Management System

CTDOT Connecticut Department of Transportation

DDSA Data Driven Safety Analysis 

DOT Department of Transportation 

ETL Extract, Transform, and Load

FDE Fundamental Data Element

FHWA Federal Highway Administration

FME Feature Manipulation Engine 

GIS Geographic Information System

HPMS Highway Performance Monitoring System

IT Information Technology

KPI Key Performance Indicator

LRS Linear Referencing System

MAP-21 Moving Ahead for Progress in the 21st Century

MAVRIC Mobile Asset Verification & Roadway Inventory Collection

MIRE Model Inventory of Roadway Elements

RACI Responsibility, Accountability, Consulted, Informed

RIS Roadway Information System 

SME Subject Matter Expert 

TED Transportation Enterprise Data

TDS Transportation Data Server

UCONN University of Connecticut 

XSP Cross-Sectional Positioning 
v SAFETY DATA CASE STUDY FHWA-SA-22-008

Table of Contents 

Introduction........................................................................................................................ 1

Data Governance in Connecticut .................................................................................... 2

TED and Enterprise Data ................................................................................................. 5

Conclusions......................................................................................................................... 9

References ........................................................................................................................11

List of Figures

Figure 1. Graphic. Change request workflow................................................................. 3

Figure 2. Graphic. TED operational model. ................................................................... 4

Figure 3. Graphic. Example of XSP data visualization. ................................................. 7

Figure 4. Graphic. Roadway safety management process (FHWA, 2013).................. 9
vi SAFETY DATA CASE STUDY FHWA-SA-22-008

Executive Summary

This case study highlights the Connecticut Department of Transportation’s (CTDOT’s) recent 

advancements in its enterprise data systems and processes. The Moving Ahead for Progress in 

the 21st Century Act (MAP-21) established several requirements to help States advance their 

roadway safety data development and analysis capabilities. Two key requirements for States 

were to: 1) collect Model Inventory of Roadway Elements (MIRE) Fundamental Data Elements 

(FDE) data for all public roads, and 2) develop safety data management systems to support the 

Highway Safety Improvement Program, Strategic Highway Safety Plan, and data driven safety 

analysis (DDSA) and evaluation. The need to meet Federal requirements and develop an 

enterprise-level approach to safety analysis on all public roads drove many of the recent 

advancements in CTDOT’s data infrastructure. CTDOT created the Transportation Enterprise 

Data (TED) warehouse to meet the need for enterprise data specifically supporting robust 

safety management, but also meeting other critical department data analysis needs. The safety 

data systems are managed under formal data governance policies expressed in the Data 

Governance Charter drafted by CTDOT alongside implementation of the TED warehouse. 

These advancements have helped CTDOT scale its data infrastructure and meet emerging 

needs for robust roadway safety management on all Connecticut roads.
1 SAFETY DATA CASE STUDY FHWA-SA-22-008

Introduction 

In 2019, the Federal Highway Administration (FHWA) completed the second U.S. Roadway 

Safety Data Capabilities Assessment (FHWA, 2019). This nationwide effort documented the 

safety data processes, policies, and procedures of all 50 States plus Washington D.C. and 

Puerto Rico. This effort not only highlighted the current state of practice with respect to all 

phases of safety data collection, management, integration, and analysis, but it also revealed that 

State departments of transportation (DOTs) were eager to improve their capacity for data 

management and integration.

This case study highlights the Connecticut DOT’s (CTDOT’s) recent advancements in its 

enterprise data systems and processes. CTDOT’s coordination of data infrastructure, such as 

the Transportation Enterprise Data (TED) warehouse, and internal processes, such as the TED 

Data Governance Charter and supporting teams, have greatly enhanced the State’s safety data 

management and analysis capabilities (CTDOT, 2020). This includes the collection of many 

Model Inventory of Roadway Elements (MIRE) data elements recommended by FHWA to

support States’ roadway safety management processes. 

Purpose and Need

The Moving Ahead for Progress in the 21st Century Act (MAP-21) established several 

requirements to help States advance their roadway safety data capabilities (MAP-21, 112-141). 

Two key requirements for States were to: 

1. Collect MIRE Fundamental Data Elements (FDE) data for all public roads.

2. Develop safety data management systems to support the Highway Safety Improvement 

Program, Strategic Highway Safety Plan, and data driven safety analysis and evaluation.

FHWA introduced the MIRE policy in 2010 and updated it in 2017 (Lefler et al., 2017). MIRE is 

a list of roadway characteristic and traffic inventory data elements that can support agencies as 

they conduct data driven safety management, analysis, and decision making. It includes 205 

elements, 37 of which are FDEs on non-local1 paved roads, 9 on local paved roads, and 5 on 

unpaved roads. States are required to have access to the requisite FDEs for all public roads by 

September 30, 2026 (23 CFR § 924.11). States are encouraged to collect, maintain, and facilitate 

access to the full MIRE as applicable to specific locations.

CTDOT is responsible for over 21,000 miles of public roads throughout the State. Connecticut 

does not have county government agencies and, as a result, CTDOT maintains both the State 

and local road network linear referencing systems (LRS) and roadway attribution. This involves 

collecting and maintaining safety-related data (i.e., MIRE) for these roadways. The need to meet 

Federal requirements and develop an enterprise-level approach to safety analysis on all public 

roads drove many of the recent advancements in CTDOT’s data infrastructure.

1 Defined by functional classification.
2 SAFETY DATA CASE STUDY FHWA-SA-22-008

Target Audience

• State transportation agencies. 

• Information Technology (IT) staff. 

• Data managers, analysts, and stewards. 

• Roadway safety practitioners and safety data users. 

Data Governance in Connecticut

The Data Governance Charter is the core document supporting CTDOT’s data program. It 

clearly outlines the responsibilities of individual roles and working groups (e.g., the Change 

Management Team, Data Governance Team, and TED-IT Planning Team). It describes the flow 

of data throughout the organization.

Roles and Teams in Data Governance

The list of roles in CTDOT’s data governance structure includes: 

• General Geographic Information System (GIS) Users. 

• Advisors from the University of Connecticut (UConn).

• Data Stewards and Data Owners/Custodians. 

• GIS Lead.

• GIS Subject Matter Experts (SMEs).

• Bureau (e.g., Public Transportation, Maintenance and Highway Operations, etc.) SMEs. 

• Bureau Leads. 

• IT Lead. 

Each role has a formal definition and a list of activities that cover day-to-day operations, as well 

as planning-level coordination. CTDOT outlines the individual responsibilities of each role using 

a Responsibility, Accountability, Consulted, Informed (RACI) Chart. The RACI Chart 

documents the following for each activity under data governance:

• Responsibility (R): The staff in this role are responsible for performing the specific task.

• Accountability (A): The staff in this role are accountable for ensuring the specific task is 

completed by those responsible.

• Consulted (C): The staff in this role are consulted as the specific task is performed.

• Informed (I): The staff in this role are kept informed during task execution. 
3 SAFETY DATA CASE STUDY FHWA-SA-22-008

In addition to individual responsibilities, the Data Governance Charter organizes the staff 

associated with these roles into three different teams:

1. Change Management Team.

2. Data Governance Team.

3. TED-IT Planning Team.

The Change Management Team meets weekly. It is responsible for tactical and technical

management of TED and associated data and applications. This team is responsible for 

programming software and application updates and changes to, or initiating and monitoring 

development of, enterprise data in TED. Figure 1 shows how the individual activities relate in 

the change management sequence reflecting the formal data governance process.

Figure 1. Graphic. Change request workflow.

© CTDOT
4 SAFETY DATA CASE STUDY FHWA-SA-22-008

The Data Governance Team meets quarterly (or as needed), and it is responsible for 

programmatic management across CTDOT. This includes monitoring key performance 

indicators (KPIs) and applying a GIS Capability Maturity Model to assess KPIs (Green et al., 

2018). The TED-IT Planning Team meets weekly and is responsible for project-level 

management and resource allocation of IT and business resources for TED. The TED-IT 

Planning Team conducts the activities planned and initiated through the Change Management 

Team and relies on the Change Management Team for working-level data governance. 

Data Flows

The Data Governance Charter diagrams how data flows through the organization using the 

TED Operational Model (figure 2). This diagram distinguishes between individual bureau-level 

activities (e.g., Policy & Planning, Maintenance, Highway Operations, etc.) and enterprise-level 

activities. Although these activities support different objectives within the DOT, figure 2 shows 

how data flows from collectors and stewards to internal and external users, as well as the 

positions responsible for each step in the TED Operational Model. 

Figure 2. Graphic. TED operational model.

© CTDOT © CTDOT
5 SAFETY DATA CASE STUDY FHWA-SA-22-008

TED and Enterprise Data

CTDOT efficiently collects and integrates large amounts of data through the agency’s 

enterprise approach, specifically through the “backbone” of its enterprise data platform, the 

geospatially located authoritative public road network. Prior to CTDOT’s enterprise data

advancements, Connecticut relied on a Microsoft AccessTM-based application to manage the 

State’s public road network and inventory data on a non-geospatial LRS. This application had 

limited scalability due to character limits, data entry methodology, and time-consuming postprocessing requirements. The MIRE FDE requirement (23 CFR § 924.11) greatly expanded the 

amount of data to be collected on all paved public roads, as well as documenting specific 

relationships between types of data. In response, CTDOT identified a need for a more 

comprehensive solution to data management. 

TED is a centralized and accessible, authoritative source for timely data, linking data geospatially 

from a variety of business units within CTDOT. It is the result of a collaborative effort among 

engaged stakeholders, each using their particular expertise in transportation data management, 

and the development of a collective mission and vision for enterprise data. It also builds on the 

creation of a formal data governance structure to aid in consistent data delivery and use. TED 

improves two key components of CTDOT’s overall safety data program: scalability and 

accessibility. Scalability refers to TED’s capacity to standardize and integrate new datasets while 

avoiding redundancy across the agency. Accessibility refers to CTDOT’s ability to collaborate 

with internal and external partners as it develops, maintains, and uses its safety and asset data. 

Safety Data Collection and Scalability

Historically, CTDOT had managed its existing roadway characteristic and limited asset location

information using a non-geospatial LRS, the Roadway Information System (RIS), as a system of 

record. This legacy system was only able to manage a limited number of datasets compatible 

with the LRS, and it was not consistent nor compatible with the requirements of the All Roads 

Network of Linear Referenced Data (ARNOLD) guidance (FHWA, 2014), the Highway 

Performance Monitoring System (HPMS), or MIRE. The programmatic changes required to 

accommodate ARNOLD, HPMS, and MIRE data elements meant that these systems could no 

longer expand to meet the DOT’s needs. TED and supporting data systems helped CTDOT 

address Federal requirements (23 CFR § 924.11) and advance the State’s safety data capabilities.

CTDOT’s development of a geospatial LRS with roadway characteristics was critical to 

supporting safety data and the TED effort. 

An example of CTDOT expanding its capabilities under TED includes the development of the 

Mobile Asset Verification & Roadway Inventory Collection (MAVRIC) tool. The introduction of 

MAVRIC has significantly improved CTDOT’s data collection capabilities, allowing the agency to 

expand its compliance with Federal data collection recommendations (Lefler et al., 2017) and 

improve the efficiency and reliability of statewide safety analysis.

MAVRIC is a browser-based roadway and asset data collection application initially customized 

to meet field data collection requirements. CTDOT has since repurposed the application to 
6 SAFETY DATA CASE STUDY FHWA-SA-22-008

also support office editing of LRS-based data (e.g., speed limits, passing zones, use restrictions, 

etc.). CTDOT staff can access the tool anywhere, in either a connected (i.e., “live”) or 

disconnected (i.e., “check-out”) environment. This means that field data collectors do not 

require internet or cellular reception to use the application, a critical component in areas of 

Connecticut that have limited cellular service or “urban canyon” downtown areas in cities. In 

the absence of an internet connection, data can be cached on a user’s device and manipulated in 

the field through a standard browser interface. 

MAVRIC also allows data collectors to create new segments of the road network and add 

substantial geospatially accurate asset data immediately without having any additional processing 

steps. This removes the need for multiple trips into the field to review the same areas. These 

features greatly improve efficiency and safety for data collectors. Additionally, MAVRIC 

supports parallel data collection where the user can collect multiple linear or point assets 

(e.g., lanes, curbing, shoulders, intersections, etc.) simultaneously during a single review path. 

This also offers a considerable efficiency improvement for both field and office data collection.

CTDOT houses MAVRIC within the Transportation Data Server (TDS), an administrative 

application which provides a way to control access to view and edit specific assets. This is

critical to maintain high data quality and integrity. Field crews, office crews, administrators, and 

other users each have set permissions to edit their own assets and attributes. Data collectors 

can sync data up with the MAVRIC database upon returning from the field if working in a 

disconnected environment. Office-based editors/connected users can sync edits after they are 

satisfied with completing an editing session. The agency performs an extract, transform, and 

load (ETL) process nightly to repopulate the MAVRIC database feeding the application, which 

improves the timeliness of data sharing between field crews, office personnel, and enterprise 

data custodians.

Integrate Cross-Sectional Data, Parallel Collection, and MAVRIC

CTDOT’s legacy collection systems allowed collectors to update cross-sectional data along the 

non-spatial LRS for the State-owned portion of the network. Cross sectional positional (XSP) 

data allows for a single asset to be fully attributed multiple times at the same location without 

having to maintain separate individual assets for each location. This valuable component of 

CTDOT’s legacy data collection tools has been expanded as the agency advanced its capabilities

into the geospatial environment. 

An example of this type of data is the Roadway Inventory Unit’s maintenance of individual lane 

information on the road network. “Lanes” is a single geospatial data asset, but by using the XSP

approach, the Roadway Inventory Unit can maintain a full set of attributes for each individual 

lane based upon its location and lane type relative to their position on the route. Travel lanes, 

slow vehicle lanes, acceleration lanes, deceleration lanes, and turn lanes are all managed as their 

own entity within the single lane asset. Shoulders (i.e., inside shoulder, outside shoulder), 

rumble strips (inside shoulder, outside shoulder, centerline), curbing (log direction and reverse 
7 SAFETY DATA CASE STUDY FHWA-SA-22-008

direction), and many other assets that may occur multiple times in multiple relative locations at 

the same measure are all managed similarly using XSPs. 

Critical to fully utilizing the XSP approach is the ability to edit multiple data assets 

simultaneously, known as “parallel data collection.” This approach supports CTDOT’s

application of XSPs in its inventory data (i.e., an individual asset can have multiple 

measurements at the same point). For example, the through lane asset can have XSP locations 

for both the log direction and reverse log direction all within the same asset class. This crosssectional view provides a more holistic visualization of the transportation system (figure 3). 

Figure 3. Graphic. Example of XSP data visualization.

© CTDOT
8 SAFETY DATA CASE STUDY FHWA-SA-22-008

Advancing Data Capabilities

CTDOT has used TED and MAVRIC to manage MIRE attribution along the network, as well as

the State’s HPMS data. Based on advancements in its enterprise approach, CTDOT collects, 

maintains, or derives 93 MIRE data elements across its entire network, including all MIRE FDEs. 

Furthermore, integration of other enterprise data assets has allowed CTDOT to add new nonpublic road networks (e.g., private roads, commercial driveways, etc.) and mileage to the 

network model, as well as expand assets and attributes with improved scalability and precision. 

Legacy data systems could only maintain extensive data on State-owned roads, with only limited 

data on local roads. Maintenance of a complex geospatial road network, along with MAVRIC 

editing capabilities and TED integration, accommodates a much larger data model that includes 

fully attributed State and local roads. It also supports expanded access to other multimodal 

networks with cross integration (e.g., trail networks) that intersect and share geometry with 

the road network). This institutional approach has positioned CTDOT to follow Federal 

guidance more efficiently. 

An additional benefit from CTDOT’s enterprise systems has been the adaptation of varied

datasets from within the DOT as assets along the LRS, leveraging the management of the 

geometry and route designations to easily locate other department assets to a single LRS with 

shared geometry and measures. CTDOT plans to expand its capabilities to use the LRS for 

several diverse applications, such as maintenance of regulatory data (e.g., posted speed limits, 

no thru truck prohibitions, and highway restrictions) and trails of regional or statewide 

significance. CTDOT has leveraged Esri’s Hub platform to create an Open Data access point for 

the public and other critical partners, where many of these authoritative and maintained 

datasets are accessible. These can be integrated into external applications through public web 

services. 

Data Accessibility: Partnerships and Safety Analysis Capabilities

Enterprise data supports several bureau-level business needs within CTDOT, as well as key 

partnerships outside of the organization. CTDOT developed the Connecticut Roadway Safety 

Management System (CRSMS) in coordination with UConn’s Transportation Safety Research 

Center. CRSMS is a web-based enterprise application designed to implement the roadway 

safety management process defined in the First Edition of the American Association of State 

Highway and Transportation Officials’ (AASHTO) (2010) Highway Safety Manual (figure 4). The 

tool enables CTDOT to make data driven decisions regarding safety investments, particularly 

with respect to developing refined costs and potential benefits associated with improvements. 

A key contributor to the success of CRSMS is its linkage to TED and the State’s enterprise data 

structure. The LRS and roadway attribute data model provided to the CRSMS through TED is

compatible with the CRSMS’s data management module, enabling the CRSMS to ingest TED 

data relatively simply. This allows users to dynamically define roadway segments based on 
9 SAFETY DATA CASE STUDY FHWA-SA-22-008

identified homogenous attributes and ingest 

data that are native to the LRS, as well as 

additional data provided through other 

means (i.e., not ingested from TED). 

CTDOT uses a Feature Manipulation 

Engine (FME) to manage business data in its 

native format before linking to the system 

geospatially.

CRSMS enables safety analysis on all public 

roads, and data collected and integrated in 

TED support these methods. For instance, 

CTDOT used to rely solely on frequencybased methods for network screening; 

crash rates formed the foundation for 

Suggested List of Surveillance Study Sites. 

With the advent of CRSMS and this data partnership, advanced methods that incorporate 

detailed MIRE data support safety performance functions and empirical Bayes analysis methods 

can be applied to all State-maintained roads. To account for locations where there is only basic 

roadway data available (i.e., locations where traffic counts do not exist), CRSMS can perform a 

planning-level analysis that involves historic crash frequency and severity. As CTDOT collects 

more data across a greater share of the road network, more advanced methods can be applied 

to local facilities. The data accessibility, reliability, and quality supported by TED improves safety 

decision-making for the State’s entire program.

An additional benefit of this DOT and university collaboration is the ability to greatly expand 

State-specific safety research. This helps improve the ability of robust safety analysis methods to 

capture Connecticut-specific trends and refine expected performance across the entire State 

road system.

Conclusions

CTDOT’s advancements in the State’s enterprise data began as a need to meet Federal 

requirements (23 CFR § 924.11) and improve the State’s analysis capabilities. This enterprise 

data approach has allowed CTDOT to expand its capabilities and analytical capacity across the 

entire network. Data governance clearly assigns roles and responsibilities to each stakeholder in 

the data collection, management, and consumption process. This framework allows CTDOT to 

effectively manage change and scale its technology to meet new challenges and use cases. The 

MAVRIC data collection application and CRSMS are great examples of comprehensive data 

collection and analysis capabilities, but these tools are only possible because data governance 

and TED are in place. Intra- and inter-agency partnerships, most notably between the DOT and 

Figure 4. Graphic. Roadway safety management 

process (FHWA, 2013).

Source: FHWA
10 SAFETY DATA CASE STUDY FHWA-SA-22-008

the University of Connecticut, showcase what is possible when data are readily accessible to 

each stakeholder, collector, analyst, and custodian alike.
11 SAFETY DATA CASE STUDY FHWA-SA-22-008

References

 Federal Highway Administration. (2019). Second U.S. Roadway Safety Data 

Capabilities Assessment, Report No. FHWA-SA-19-018, Washington, D.C.

Available online: 

https://safety.fhwa.dot.gov/rsdp/downloads/rsdp_usrsdca_final.pdf. 

 American Association of State Highway and Transportation Officials. (2010). 

Highway Safety Manual: 1st Edition, Washington, D.C. Resources available online: 

http://www.highwaysafetymanual.org/Pages/default.aspx. 

 Connecticut Department of Transportation. (2020). TED Data Governance 

Charter.

 Federal Highway Administration. (2013). Quantifying Safety in the Roadway

Safety Management Process, Federal Highway Administration, Report No. 

FHWA-SA-13-010, Washington, D.C. Available online: 

https://safety.fhwa.dot.gov/tools/crf/resources/cmfs/docs/management.pdf. 

 Federal Highway Administration. (2014, September 1). All Road Network of 

Linear Referenced Data (ARNOLD) Reference Manual. 

https://rosap.ntl.bts.gov/view/dot/56299

 Green, M., Lucivero, A., Reovan, A., & Quinton, D. (2018). Slimgim-T: GIS 

Capability Maturity Model For State Departments of Transportation. Available 

online: https://www.gis.fhwa.dot.gov/documents/SlimgimT_GIS_Capability_Maturity_Model.pdf. 

 Lefler, N., Zhou, Y., Carter, D., McGee, H., Harkey, D., and Council, F. (2017). 

Model Inventory of Roadway Elements – MIRE 2.0, Report No. FHWA-SA-17-048, 

Washington, D.C. Available online: 

https://safety.fhwa.dot.gov/rsdp/downloads/fhwasa17048.pdf. 

 Moving Ahead for Progress in the 21st Century Act. Public Law 112-141. (2012, 

July 6). https://www.congress.gov/bill/112th-congress/house-bill/4348/text

 Phone Interview on October 27, 2021, with Greg Ciparelli and Shanshan Zhao.

 23 CFR § 924.11 - Implementation. (n.d.). Code of Federal Regulations.

https://www.ecfr.gov/current/title-23/chapter-I/subchapter-J/

part-924/section-924.11
12 

Contact

Connecticut Department of Transportation

Enterprise GIS Unit

DOT.EnterpriseGIS@ct.gov 

Roadway Inventory Unit

DOT.RoadInfo@ct.gov

University of Connecticut

Shanshan.H.Zhao@uconn.edu