45
Integrating Operations Data for Decision Making Transportation leadership you can trust. June 2012 Anita Vandervalk, P.E. Cambridge Systematics, Inc.
Integrating Operations Data for Decision Making
Transportation leadership you can trust.
June 2012
Anita Vandervalk, P.E.
Cambridge Systematics, Inc.
Agenda
Why is this important?
Which data?
What decisions?
Challenges
Solutions and tools
Future outlook
2
Why is this Important?
Performance Management in an era of declining budgets
Information industry - Rapid changes in technology and infrastructure for data sharing
» Breadth of technologies for data management and dissemination continues to increase
» Complexity and cost of deploying these tools continues to fall» Complexity and cost of deploying these tools continues to fall
» Rapid growth in open source software solutions and low-priced, third-party hosting services
Access to data necessary
» Analyzing and evaluating management and operations strategies
» Making the case for greater levels of investment in operations
3
4
Transportation Agency Challenges
Faced with
» Big Data, lots of (new) data
» Technology/Innovation advances
» Aging Infrastructure
» Outdated IT strategies
» Evolving customer expectations» Evolving customer expectations
» Information Management challenges
Need
» INFORMATION – Organized and Fused for Decision Making
» A well designed strategy for data, analytical tools and information technology to support and institutional new processes around economics, freight, project selection, PMs
5
Transportation Agency Challenges
Operations/Real time – New data sources, capability to merge with travel demand, anlalytics, predictive, integration of sources
Safety – Increasing Federal push for data assessments, tools
Asset management – Re-emergence with AASHTO guide and other clients “getting it”other clients “getting it”
Program Management – More scrutiny and demand for timely communication of relevant information
Decision making, PMs - MAP 21, TRB focus, Executive level awareness
6
Data Management Services Framework
DecisionsDecisionsInformationInformationDataData
OperationsPlanningFunding
Tools
Forecasting/
Modeling Communication
7
DecisionsDecisionsInformationInformationDataDataFundingProjectsPolicy
Programming
Collection Analysis Integration Visualization
BUSINESS REQUIREMENTS
Perform
Partner with VendorsCreate Value -Visualization
Cycle of Data
ReportBusiness
Requirements
Partner with Vendors
Evaluation, QA/QC, Integration, Fusing,
Imputing, Aggregating, GIS
Create Value -Visualization
8
Analysis Collection
Which Data?
Traveler Information Systems
» Current traffic conditions (e.g., travel time, speed, level of congestion)
» Traffic incidents, work zone, and/ or lane closures
Traffic Control Systems
» Time and location of traffic control actions (e.g., ramp metering, » Time and location of traffic control actions (e.g., ramp metering, traffic signal control, lane control signals, message board content)
Incident and Emergency Management Systems
» Location, cause, extent, and time history of roadway incident/ emergency detection and clearance, incident timeline.
Roadway Infrastructure Data
» Roadway characteristics, intersection data, traffic control data
9
Which Data?
Vehicle Probe Data: Recent “real-time” speed and travel time
» Historical speed and travel time; data quality measures –completeness, accuracy, latency; and validation
State/Local Generated Detector Data
» Recent “real-time” volume, occupancy, speed, travel times, vehicle type; and historical volume, occupancy, speed, travel timestype; and historical volume, occupancy, speed, travel times
State/Local Work Zone
» Location, agency, lanes closed, date/ time, duration
Weather Data
» State/ local RWIS data, NWS (or equivalent) data – date/ time, road-ways affected, type, intensity
10
Which Data?
Advanced Public Transit Systems
» Transit vehicle passenger boardings by time and location, vehicle trajectories, passenger origins, and destinations
Network Typology
» Link capacity, free flow speed, number of lanes
Origin/Destination Data
» Surveys, travel diaries, or direct monitoring
Traffic Monitoring and Detection Systems
» Vehicle volume, speed, classification, weight, turning movements, and position trajectories
11
Operations Data for Planning
ITS-Generated
Data Category
Figure 2. Planning Uses of Operations Data
• AADT
• K/D Factors
• Temporal Distributions
• Free Flow Speeds
Traffic Monitoring
Planning Function
Specific Data Items
12
ITS-Generated Traffic Data
• Free Flow Speeds
• PHVs
• Peak-Hour Speeds
• Link Capacities
• Hourly Speeds
• Hourly VMT
• Exceedance Days
• Delay Indices
• Reliability
• Congestion by Source
• Unreported Crashes
• PAR Supplemental Data
Incident Data
Weather Data
Travel Demand Forecasting Models
Air Quality Models
Performance Monitoring
Safety Planning
Which Decisions?
Planning for Operations
» Investment
» Project priorities
» Types of strategies
Real Time ManagementReal Time Management
» Traveler Information
» Work Zones
» Weather
13
Which Programs?
Traveler Information Systems
Active Traffic Management
Traffic Incident Management
Maintenance Activity Planning
Work Zone Management
Event Management
Traffic Control Device Synchronization
Road Weather Information Systems
Regional Traffic Management Centers
14
Which Programs?
Freight Management and Commercial Vehicle Operations;
Electronic Toll and Fare Collection
Congestion Management Process
Performance Measures
ITS Strategic Planning
Managed Lanes
15
EXAMPLES OF PROGRAMS AND DECISIONS
GDOT HERO Incident Response Times
Explaining Performance and Reliability ChangesReliability Changes
18
System Performance -------Operating and Maintenance Benefit/Cost Ratio------- ----------Peak Hour Travel Time Index-----------
------District Involvement------
Has Quarterly Update
Fiscal Year 10/11
Districts
Two Year
Average
TTI
District 1 1.26
District 2 1.20
District 3 1.29
District 4 1.44
District 5 1.41
District 6 1.46
District 7 1.41
District 4
Organizational Performance
I-95
19
-------------------Incident Duration ------------------
--------------Work Zone---------------
No Business Plan
Has Business Plan,
No Quarterly Update
District 1
District 2
District 3
District 4
District 5
District 6
District 7
Limited Access
Facilities
District 4
Districts
Quarterly
Incident
Duration
District 1 64
District 2 35
District 4 44
District 5 52
District 6 31
District 7 54
State-wide 47
Goal < 90 mins
Note: Incident duration data for District 3 is not available
20
Challenges
Technical
» Development and maintenance of hardware and software
» Specifications for data collection, analysis, archiving, and reporting.
Institutional
» Centralized policy-making, and decentralized execution of » Centralized policy-making, and decentralized execution of policies
» Limited appreciation by decision-makers of the role of data systems in supporting business operations
» Lack of formal policies and standards
21
Challenges with Data Integration
Data Integration
Data Resolution
Data Quality
» Maintaining extensive electronic field equipment (sensors and communication)
Active Versus Passive Archiving
Rigid Data Format Standards versus Flexible Formats with Metadata
Data Integration (Fusion) Issues
» Location Matching
» Version Control
22
Transportation Data Formats!
TranXML, TMDD, Data.gov, geospatial reference frameworks (e.g., TMC codes), Apache Software Foundation Object-Oriented Data Technology, NTCIP, and TCIP. Others will be included such as the National Information Exchange Model (FHWA Office of Policy is currently examining this model as a possible standard), UN-recommended UN/EDIFACT (which is the only international standard and is predominant outside of North America and as standard and is predominant outside of North America and as well predominant in life science and pharmaceutical industry), U.S. standard ANSI ASC X12 (X12)(predominant in North America), TRADACOMS standard developed by the ANA (Article Numbering Association) (predominant in the UK retail industry), ODETTE standard used within the European automotive industry, ebXML, used by many Asian community systems, WCO standards, such as WCO data model and ASYCUDA.
23
Speed and Traffic Technology Types
Probe Measure Technologies
» Direct measurement of travel time
– Roadside reader devices
– Cell phone triangulation or GPS
» O/D data collected
Spot Measure TechnologiesSpot Measure Technologies
» Direct collection of data
– Roadside detection devices
» O/D data not collected
24
Speed and Traffic Technologies
Probe Measure Technologies
» Toll tag
» Bluetooth
» Cellular phone
» Crowd-sourcing
» Private Data Providers» Private Data Providers
Spot Measure Technologies
» Video Image Detection
» Radar
» In-pavement loops
» Magnetic detector
25
Private Data Providers
Advantages (INRIX, Navteq, TomTom)
» No installation or maintenance cost
» Great incentive to provide accurate, quality data at a low cost
Disadvantages
» Speeds accurate on freeways but less so on arterials
» Method of speed calculation, underlying data and mix of real time » Method of speed calculation, underlying data and mix of real time and historic data used for estimates is not known
» Cannot provide volume data
Cost
» Negotiated for projects
26
Additional O/D Data Collection Technologies
GPS-Enabled Smartphone Travel Surveys
Automatic License-Plate Capture
GPS-Tracking Systems Installed by Vehicle Manufacturers
GPS Survey Data
Smartphone Track Logs via User-Installed Applications
Wireless Network Locationing Technologies
27
Data Will Continue to Expand and Improve
Private sector travel time data enables performance measurement, but there are issues that must be addressed
» Common definitions for performance measures (how to calculate from low-level data)
» Integration with agency data: volumes, incidents, work zones, weather, highway location
» Distinguish O/D travel times» Distinguish O/D travel times
28
SOLUTIONS AND TOOLSTECHNICAL AND INSTITUTIONAL
Virtual Data Integration
Virtual Data Integration
31
Web
Access
Archive Inrix Archive
TMC
SoftwareCARS
511
Inrix or Other
Providers
PeMs
34
RITIS
35
Importance of a Data Business Plan
Establish goals
Assess agency data programs
Establish data governance
Ensure proper use of technology/tools
Link data management to performance measures and target-setting
Data Governance
“The execution and enforcement of authority over the management of data assets and the performance of data functions”
Defines the important relationship between data management and performance measurement
Data Governance: Steps
Identify the business objectives of the agency
Identify the functions or services of the agency that support the business objectives
Identify which business functions are supported by which data programsprograms
Establish policies, standards, and procedures which mandate how data is to be collected and used within the agency
WHAT THE FUTURE HOLDS
What the future holds
Increasing private sector data sources
Increasing public involvement in managing transportation
Evolving role of ITS and Operations
Technology and Innovation – ????
40
Programs will Continue and Grow
Assessing the Synergistic Benefits of Integration
May 22, 201242
Benefit/Cost of Operations
Desk Reference Document
» Provide comprehensive, one-stop-shopping for B/C information related to TSM&O (June 2012)
Companion Operations B/C Decision Companion Operations B/C Decision Support Tool
» TOPS-BC
Interim Draft of the Desk Reference Document
» http://www.camsys.com/kb_pubs_oper.htm
FHWA Planning for Operations Website
» http://www.plan4operations.dot.gov
43
Guidance Available in the Desk Reference Document
B/C analysis primer information
Definition of Ops strategies and likely impacts
Available tools/methods and selection criteria
Strategies for addressing identified challenges of B/C analysis for operationsfor operations
Methodologies for assessing travel time reliability
Discussion of multi-scenario analysis methods
44
