FHWA MIRE Management Information System (MIS)
Carol H. Tan, PhD FHWA, Office of Safety R&D 2013 Traffic Records Forum
29 October 2013
Overview
• Background • Purpose • Overview of Activities
– Data Collection • Research • Lead Agency Program • MIRE Data Collection Guidebook
– Structure of MIS – Performance Measures
• Summary of Results • Questions
Project Team FHWA
Carol Tan & Robert Pollack Project Managers
VHB Nancy Lefler
Principal Investigator
Applied Research Assoc Jag Mallela
Roadway Data Collection
Data Nexus Inc Barbara DeLucia
Information Systems
UNC-HSRC Forrest Council
Performance Measures
MIRE MIS
• Evolution of MIRE from listing to Management Information System (MIRE MIS)
Federal Data Requirements
• Moving Ahead for Progress in the 21 Century Act (MAP-21) – Requires States have in place a safety data system
– Requires States to collect a subset of MIRE – FDEs
• Highway Performance Monitoring System (HPMS) – Requires geospatial network on all public roads
• FHWA Guidance for State Data Systems: http://www.fhwa.dot.gov/map21/guidance/guidesafetydata.cfm
Purpose
• Determine feasibility of implementing MIRE:
–Mechanisms for data collection
–Process for data handling and storage and linkage among files
–Performance measures
• Provide guidance for improving roadway data for safety
Overview of Project Activities
• Collection of MIRE data elements
• Development and testing of MIS structure
• Development/expansion of roadway data performance measures
Data Collection
Collection of MIRE Data Elements
• Research:
– Current and emerging practices and gap analysis
– “Collective data” concept
• Lead Agency Program: pilot MIRE data collection
• MIRE Data Collection Guidebook
Research Synopsis • Products
– MIRE Element Collection Mechanisms and Gap Analysis report
– Exploration of the Application of Collective Information to Transportation Data for Safety White Paper
• Benefits – Provide information and current and emerging
collection methodologies
– Help guide agencies collection practices
Lead Agency Program
• Pilot MIRE data collection
• Test feasibility of collecting MIRE data
• New Hampshire DOT and Washington State DOT
Data Collection
• NHDOT: Intersection inventory for SafetyAnalyst
– 10,300 S/S and S/L intersections
• WSDOT: Intersection inventory for SafetyAnalyst and HSIS
– 15,800 S/S and S/L intersections
• Opportunity to compare collection methodologies
Data Collection Process - Similarities
1. Determine what elements to collect
2. Develop detailed Work Plan
3. Develop tool/model to extract existing data
4. Develop user interface
5. Populate inventory
6. QA/QC
7. Provide sample
8. Provide final data set
New Hampshire DOT
–Pre-populated intersection inventory with existing data
–Then collected remaining needed data
Washington State DOT
– Extracted existing data concurrently with data collection
– Then combined into one dataset
Data Collection Process – Differences
New Hampshire DOT
• All non-proprietary GIS-based
• Easy to develop and implement
• Less labor to develop
• Provided tool to NHDOT
Washington State DOT
• Proprietary software
• More automated/ sophisticated
• More difficult to develop (more time, higher cost)
• More labor to develop
Data Collection Tools
Level of Effort and Cost – NHDOT Activity Hours
Coordination 455
Development of model 75
Development of the data collection tool/interface 175
Develop node layer for the 24,000 local/local intersections 30
Hiring and training of data collection clerks 135
Collection of intersection data:
In-office collection for 10,300 intersections 1,600
In-field data collection for 200 intersections 60
Management and QA/QC 360
Development and delivery of a dataset of traffic volumes 375
Providing the dataset, model, and tool to NHDOT 25
Total Cost $210,000
Level of Effort and Cost – WSDOT
Activity Hours
Coordination 425
Develop data collection tool and interface 815
Collection of intersection data – 15,820 intersections 2,105
Acquire and incorporate third-party traffic volume data 140
Automated import of existing data from WSDOT 445
Conduct QA/QC 590
Exporting the final dataset 250
Total Cost* $340,000
Data Collection: Lessons Learned
• MIRE flexibility
• Develop the work plan
• Constant contact/feedback between the contractor and the State DOT
• Use of the sample dataset
• Use of existing data
• Temporality of the collected data
Lead Agency Program Synopsis
• Products
– Intersection inventory tool
– Intersection inventory for NHDOT and WSDOT
– MIRE MIS Lead Agency Data Collection report
• Benefits
– Lessons learned can help other agencies interested in similar data collection effort
MIRE Data Collection Guidebook
• Definition
• Attributes
• Accuracy statement
• Considerations
• Existing resources
• Collection methods
• Concerns with collection methods
Guidebook Focus
• Focus on elements not traditionally collected
• Collection methodologies
– Data mining
– Manual collection
– Mobile mapping/LiDAR
– Aerial imaging
• Efficiency and concerns
Images courtesy: Mandli Communications, Inc.
Guidebook Synopsis
• Product
– MIRE Data Collection Guidebook
• Benefit
– Provides methodologies for collecting elements that do not already have established methodologies
– Agencies can incorporate into their data collection programs
MIS Structure
MIS Structure
• Development of conceptual model
• Development of prototype
• Testing of prototype
• Reporting
Structure of the MIRE MIS
• Decision support system to improve highway safety
• Developed using relational database model
• Collects data from disparate sources for higher level analysis
Development of MIS Prototype
• Identified concepts for prototype
• Developed relational database structure to match structure
• Added tables and fields to accommodate supplemental data
• Transformed data on import to match the MIS structure
Testing of MIS Prototype
• Developed list of functional targets to test the ability of the MIS to provide data to support safety decisions
• Functional targets areas:
– Querying and analyzing data
– Preparing data for export to other tools
– Providing data quality measures
Conclusions & Lessons Learned
• Data quality matters!
• Using MIRE as guidance - States build system around their own data structure
• Does not address functional aspects of the system
• GIS is an essential part of the MIRE MIS - consistent referencing system for data points
MIS Structure Synopsis
• Product:
– Development of a Structure for a MIRE Management Information System report
• Benefit:
– Demonstrated MIRE MIS is feasible with existing resources
– Agencies can use approach and lessons learned to develop an MIS for their data
Roadway Data Performance Measures
Performance Measures • Tools for measuring data quality and establishing
goals for data improvement
• Primary measures: – Timeliness
– Accuracy
– Completeness
– Uniformity
– Integration
– Accessibility
Roadway Inventory Measures
• NHTSA developed initial performance measures for the six core traffic safety data systems
• Built/expanded on those measures
• Detailed review of measures proposed for roadway data
Performance Measures Synopsis
• Product:
– Performance Measures for Roadway Inventory Data report
• Benefit:
– Provides techniques for assessing and improving the quality of the roadway and traffic data
Summary of Activities
Summary of Project Activities/Products Data Collection:
• Intersection inventory tool
• Intersection inventory datasets for NHDOT and WSDOT
• MIRE Collection Mechanisms and Gap Analysis report
• Exploration of the Application of Collective Information to Transportation Data for Safety White Paper
• MIRE MIS Lead Agency Data Collection report
• MIRE Data Collection Guidebook
All reports available online: http://safety.fhwa.dot.gov/rsdp/
Summary of Project Activities/Products MIS Structure:
• Prototype MIS structure for pilot agency
• Development of a Structure for a MIRE Management Information System report
Performance Measures:
• Performance Measures for Roadway Inventory Data report
All reports available online:
http://safety.fhwa.dot.gov/rsdp/
Summary
• MIRE MIS – how to collect, integrate, manage, and measure data for improved safety decisions
• State and local agencies can realize benefits
Thank you!
Robert Pollack, [email protected]
Dr. Carol Tan, [email protected]
Nancy Lefler, [email protected]
Questions