Data Needs, Availability, and Opportunities for Work Zone Performance Measures Webinar
March 19, 2013
Presentation

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Data Needs, Availability and Opportunities for Work Zone Performance Measures

March 19, 2013

Presenters: Jawad Paracha (FHWA), Gerald Ullman (TTI), Geza Pesti (TTI) and Rachel Klein (Battelle)

   

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Webinar Structure

• Introduction (FHWA)
• Guidance Development Challenges and Process
• Structure of the Guidance Document
• Mobility Measures and Data Sources
  • Q&A
• Safety Measures and Data Sources
  • Q&A
• Customer Satisfaction Measures and Data Sources
• Agency/Contractor Measures and Data Sources
  • Q&A

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Work Zone Performance Measures

Metrics that help to quantify how work zones impact travelers, residents, businesses and workers.

• Project-level metrics
• Agency program-level metrics

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Work Zone Performance Measurement

• Quantifying work zone impacts
• Manage work zone impacts
• Guides investment decisions
• Identify trends
• Refine policies and procedures
• Assists in public information and outreach

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Work Zone Safety and Mobility

23 CFR 630.1088(c)

• States shall use field observations, available work zone crash data, and operational information to manage work zone impacts for specific projects during implementation.
• States shall continually pursue improvement of work zone safety and mobility by analyzing work zone crash and operational data from multiple projects to improve State processes and procedures.

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Work Zone Performance Measurement Challenges

Which measures are most important? What data are needed? Where and how do we get that data? What is available/accessible? How applicable is it? How do we compute the measures from that data?

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Guidance Development Process

• Initial list of 13 possible measurement categories
• Reduced and collated along three key dimensions
• Practitioner expert panel identified and prioritized performance measures for each category/dimension

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Performance Measure Data Needs

• Performance data
  • Quantifies the amount of the effects
  • Dimensions: mobility, safety, customer satisfaction, and agency/contractor productivity
• Exposure data
  • Quantifies who or what was affected
  • Dimensions: counts, distances traveled, durations
• Indicator data
  • Specifies activities, phases, time periods, or events of interest when effects occurred

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Performance Measure Selection

Step 1. Determine performance measurement categories of interest

Step 2. Decide which work zones to measure

Step 3. Decide what work zone conditions to measure

Step 4. Determine data sources to use

Step 5. Compute specific measures of interest

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Where Can We Get Data?

• Extract it from existing sources
• Collect it (manually, electronically)
• Interpolate it from existing or collected data

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Guidance Document Structure

Introduction Selecting Useful Performance Measures Data Sources/Methods Mobility-related Performance Measures Safety-related Performance Measures Customer Satisfaction-related Performance Measures Customer Satisfaction-related Performance Measures

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Mobility-Related Performance Measures

• Mobility impacts commonly measured as
  • Throughput
  • Delays
  • Travel times
  • Travel time reliability
  • Vehicle queues

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Throughput

Existing Agency Data Sources TOC or traffic signal system vehicle count data Toll facility usage data Automatic traffic recording (ATR) station data Planning and programming AADT estimates
Work Zone Specific Throughput Data Data from work zone ITS deployment Temporary mechanical data collection device Manual vehicle count at key times & locations Person Throughput Data Manual sampling of per-vehicle occupancy levels Manual sampling or video detection of pedestrian throughput

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Throughput

• Potential Future Data Source
  • Connected vehicle technology
    To be useful, sufficient market penetration of V2V and V2I technology is needed

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Throughput

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Considerations and Trade-Offs of Throughput Data Sources

Data Source	Key Considerations and Trade-offs
All data types	Depending on collection location, data is demand or throughput Multiple days of data is needed to reduce day-to-day variations
TOC sensor data and toll facility usage data	Important to verify data availability once work has started
ATR station data	Need to verify that counts are "true" values (not adjusted)
Agency AADT estimates	Reasonable when capacity < demand at any time during the day If diversion occurs, AADT overestimates throughput and exposure
Work Zone ITS data	Data must be archived and available for PM computations
Mechanical counters or manual counts	May not be practical for high-volume, high-speed roadways Manual counts are labor intensive
Manual collection of person/vehicle occupancy levels	Useful if "green" and HOV travel is part of the WZ management plan
Manual or electronic collection of pedestrian throughput	Useful if "green" and HOV travel is part of the WZ management plan Pedestrian and vehicle traffic peak hours may not always coincide
Connected vehicle data	Date of availability still uncertain

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Delay, Travel Time, Travel Time Reliability

Existing Agency Data Sources TOC spot speed sensor data TOC tracking of vehicles through use of cameras TOC point-to-point travel time data using AVI, AVL, or license-plate recognition technology Work Zone Specific Travel Time and Delay Data Data extracted from a work zone ITS deployment Portable point-to-point travel time data collection devices Manual spot speed sampling using radar or lidar devices Travel time runs through the work zone Estimation of travel time delays from observed queue length data

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Delay Estimation from Observed Queue

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Delay, Travel Time, Travel Time Reliability

• Potential Future Data Source
  • Travel Times from Bluetooth Address Matching
    • Several states (e.g., Texas, Indiana) have used anonymous matching of Bluetooth devices in vehicles to track point-to-point travel times in work zones.
  • Private (3rd Party) Sources of Travel Time and Speed Data
    • The Virginia Department of Transportation examined the potential of obtaining historical private-sector traffic data for the purposes of computing work zone performance metrics
  • Connected vehicle technology

 

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Example: Work Zone Delay Estimation from Bluetooth Address Matching

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Example: Corridor Delay Estimation from Bluetooth Address Matching

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Considerations and Trade-Offs of Delay, Travel Time, and Reliability Data Sources

Data Source	Key Considerations and Trade-offs
TOC spot speed sensor data	Tend to be less accurate when congestion is present Important to verify data availability once work has started
TOC point-to-point travel time data	Important to verify data availability once work has started Accuracy depends on market penetration of tracking technology Represents recently completed, rather than current, trip times.
Work zone ITS data	Data must be archived and available for PM computations
Portable point-to-point travel time data collection	Accuracy depends on market penetration of tracking technology Represents recently completed, rather than current, trip times.
Manual spot-speed data	Labor intensive Most useful if work zone impacts occur in a fairly small section Most useful for assessing short time periods
Manual travel time data collection by driving through the work zone	Labor intensive Most useful for assessing short time periods Multiple runs increase accuracy & precision of travel time estimates
3rd party (private-sector) travel time and speed data	Level of detail available may vary by vendor Translation to agencies' data mapping protocol is needed
Bluetooth data	Accuracy depends on market penetration of Bluetooth technology Represents recently completed, rather than current, trip times.
Connected vehicle data	Date of availability still uncertain

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Traffic Queue Data Sources

• Existing Data Sources
  • Speed data extracted from a work zone ITS deployment
  • Observation of queues from a permanent or work zone TOC
  • Observation of queues by field personnel at the work zone

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Queue Estimation

Example:

• Spot traffic sensors are located 0.2 mile, 0.8 mile, and 1.3 miles upstream of the temporary lane closure.
• Project diary information indicates that a lane closure began at 9:00 AM and ended at 3:30 PM.

Time	Estimated Location of Upstream End of Queue	Estimated Queue Length
11:00 AM	None	0
12:00 PM	Between Sensors 1 & 2	0.2 + (0.6/2) = 0.5 mile
1:00 PM	Between Sensors 2 & 3	0.2 + 0.6 + (0.5/2) = 1.05 mile
2:00 PM	Between Sensors 2 & 3	1.05 mile
3:00 PM	Between Sensors 2 & 3	1.05 mile
4:00 PM	None	0

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Traffic Queue Data Sources

• Potential Future Data Source
  • Screenshot Captures from 3rd Party Traveler Information Providers
  • Private (3rd Party) Sources of Travel Time and Speed Data
  • Connected vehicle technology

 

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Considerations and Trade-Offs of Traffic Queue Data Sources

Data Source	Key Considerations and Trade-offs
All data types	Definition of queues (e.g., min speed threshold) is critical Both queue duration and queue length over time are important
TOC or work zone ITS data using spot speed sensors	Requires detailed speed data analysis on sensor by sensor basis Important to verify data availability once work has started
Visual queue identification by TOC operators	Requires adequate camera coverage upstream of work zone
Collection of queue data by field personnel	Data collection protocol training is needed May be difficult to accurately monitor the end of queue Ensure that field personnel understands its importance
Screenshot of real-time traffic condition maps	Required screen resolution depends on max. expected queue length Time-lapse capabilities do not exist in most screen capture software.
3rd party traveler information data	Level of detail available may vary by vendor Translation to agencies' data mapping protocol is needed
Connected vehicle data	Date of availability still uncertain

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Identifying and Computing Specific Mobility Measures of Interest

Once work zone mobility-related data sources are identified, a jurisdiction will have to make its own decisions as to what performance measures it chooses to track.

• Example: In some jurisdictions with TOCs, efforts are underway to develop simple-to-use computer dashboards that can provide current traffic conditions in and around a work zone

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Q&A

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Safety-Related Performance Measures

• Safety impacts commonly measured as
  • Crashes
  • Safety Surrogates
  • Worker Accidents

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Crashes

Existing Agency Data Sources Statewide traffic crash records database entries Crash report forms (hard-copy or electronic) TOC incident database entries Emergency response/service patrol dispatch logs Future Sources Agency-collected work zone crash information Connected vehicle initiative data

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Considerations and Key Trade-offs

Data Source	Key Considerations and Trade-offs
Statewide Crash Records Database	Limited work zone features and activities information Time lags in obtaining crash data for a given work zone
Electronic or hard copy crash report forms	Limited work zone features and activities information Requires manual coding May need to work with multiple enforcement agencies
TOC operator incident logs	Includes non-reported as well as reported crashes Includes non-crash events
Dispatch Logs of Emergency Response or Service Patrols	Likely to include non-traffic crash events as well Potential privacy concerns
Agency-collected crash and work zone database	Significant agency effort required Requires upper agency support and emphasis
Connected vehicle data	Date of availability still uncertain

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Safety Surrogates

Existing Agency Data Sources Speed data collected by hand-held devices Speed data extracted from ITS sensors Travel times Videotaped traffic behaviors at key locations Work zone inspection scores Future Sources Microscopic traffic simulation output Connected vehicle initiative data

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Considerations and Key Trade-offs

Data Source	Key Considerations and Trade-offs
All data types	Correlation to crashes not yet fully verified Most can be obtained relatively quickly
TOC or work zone ITS speed sensor data	Value of data depends on the locations of the sensors. Need to verify data availability and archival once work starts
Speed data collected with hand-held radar or lidar	Data collection easy to accomplish Useful for assessing speed behaviors Inconspicuous data collection techniques required
Travel times through the work zone	Speed change locations can indicate problems Can be used to assess compliance with wz speed limit
Videotaped traffic behavior	Can be difficult to find a unobtrusive viewing point Data analysis is labor intensive Requires precise definition of behaviors of interest
Work zone inspection scores	Requires significant effort to establish scoring/ratings Correlation of scores to actual safety levels not yet verified
Traffic simulation output (analyzed with SSAM)	Significant coding and calibration effort required Correlation to actual work zone safety conditions not yet verified
Connected vehicle data	Date of availability still uncertain

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Worker Accidents

• Existing Agency Data Sources
  • Agency or contractor worker injury records
  • State worker compensation commission accident statistics
  • Bureau of Labor statistics database
• Future Sources
  • Connected vehicle initiative data

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Key Considerations and Trade-offs

Data Source	Key Considerations and Trade-offs
Agency or contractor worker injury records	Use must be monitored due to privacy concerns Small sample size for many companies will make it difficult to identify trends
State worker compensation commission statistics	Useful for comparisons to agency or contractor accident trends Level of detail will be limited
BLS, OSHA worker accident statistics	Useful for comparisons to agency or contractor accident trends Level of detail will be limited
Agency-collected work zone crash and accident database	Significant effort required Requires upper agency support and emphasis Use of accident reports must be monitored carefully due to privacy concerns

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Identifying/Computing High Priority Safety Measures

• Change in crash frequency (by type)
• Change in crash rate per vehicle-miles-traveled (for a given time period)
• Compliance with work zone speed limit
• Speed variance at a location
• Frequency of worker accidents
• Worker injury rate per hours worked
• Injury type, severity, contributing factor distributions

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Example: Tracking Crash Frequency Trends at a Work Zone

• Work zone on roadway that normally experiences 5 crashes per month
• Have had 7, 3, 10, 7 crashes in past 4 months during work zone

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Example: Tracking Crash Frequency Trends at a Work Zone

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Q&A

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Customer Satisfaction Performance Measures

• Who are our customers?
  • Travelers, residents, and businesses
• Impact of Work Zones?
  • Delays, congestion, and inconveniences are challenging for maintaining good relationships with customers
• Why are measures necessary?
  • Infrastructure is largely publicly-owned and funded

Therefore...

Measuring customer satisfaction associated with work zones is critical to an agency's or contractor's set of work zone performance measures

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Existing Customer Satisfaction Data Sources (1 of 3)

• Focus Group Transcripts
  • Participants opinions, experiences, and suggestions
  • Not representative of overall driving population
  • Anecdotal findings
• In-Person or Telephone Interview Responses
  • Responses may vary at location over time
  • In-person interviews require short surveys
  • Fairly labor intensive to administer

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Customer Satisfaction Data Example

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Existing Customer Satisfaction Data Sources (2 of 3)

• Mail, Email, or Website Survey Responses
  • Quantitative statistical analysis
  • Qualitative assessments
  • Predetermined options
  • Statistically significant findings
  • High cost
  • Slight negative bias

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Customer Satisfaction Data Example

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Existing Customer Satisfaction Data Sources (3 of 3)

• Customer complaint database entries
• Databases track complaint arrivals and disposition
• Some complaints easily associated with a work zone
• Some complaints may be more indirect
• Complaints effective for identifying operational or safety problems
• Not indicative of overall driver satisfaction
• Small sample sizes

Travelers, residents, or nearby businesses may embellish conditions somewhat when making a work zone-related complaint Those who are not unhappy generally do not contact the agency to indicate their general satisfaction

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Future Customer Satisfaction Data Sources

Social Media Technologies Facebook Twitter Selection biases and similar traditional survey techniques issues Responses negatively skewed
Web-Based Tools to Conduct On-Line Focus Groups System capabilities may include: Polling group Private chat sessions "Groupthink" area

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Considerations and Trade-Offs of Customer Satisfaction Data Sources

Data Source	Key Considerations and Trade-Offs
Focus groups	Best for gathering opinions, perceptions A properly trained facilitator is critical Data from multiple groups may be needed
One-on-One Interviews	Best for obtaining responses during or right after drivers have passed through a work zone May need to do surveys multiple times as conditions in the work zone change
Surveys/ Questionnaires	Multiple dissemination mechanisms (mail, email, website) possible Potential to reach a larger sample size more efficiently Properly designed surveys can yield statistically significant results
Complaints	Work zone effects may trigger complaints directly or indirectly Customers may embellish the magnitude of the problem Statistical analyses are usually not possible with the data
Social Media Uses	Important to rely on trained survey designers for these applications Responses will be biased towards younger, more technology-savvy users
On-Line Focus Groups	Allows participants to remain at their computers to participate Effectiveness of on-line efforts to mimic the interactions that occur in face-to-face focus groups is unknown

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Identifying/Computing High Priority Customer Satisfaction Measures

• Ratings of the quality of work zone features seen while driving through a work zone
  • Signs
  • Information provided regarding delays, queues, work activities
• Satisfaction ratings with travel conditions through multiple work zones
  • Multiple work zones
  • Corridor in a region or network
• Frequency/rate of complaints
• Satisfaction ratings for traveling through work zone

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Agency/Contractor Productivity Performance Measures

Existing Agency Data Sources Construction management system databases Lane closure request/approval databases Daily project diary notes
Future Sources Mobile data collection applications of work activities Electronic maintenance work databases

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Key Considerations and Trade-Offs

Data Source	Key Considerations and Trade-offs
Construction management system databases	Focus mainly on contract-related data Data elements of interest are often narratives in the system, with minimal consistency in entries across projects
Lane closure request and approval databases	May include closures across multiple agencies and contractors Normally limited to high-volume roadways only May contain a large number of "phantom" closures that need to be removed prior to analyses
Daily project diaries	Amount and type of data entered often varies by project
Mobile applications for project activity entry	Use of mobile devices in the field may cause costs and durability of the devices to become an issue An application of this type may not yet exist
Maintenance management system databases	Requires detailed recordkeeping of activities by all maintenance crews and crew members

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Identifying/Computing High Priority Agency/Contractor Productivity and Efficiency Performance Measures

• % of allowable or total days worked
• % of lane closure hours occurring outside of allowable "work windows"
• Production rates

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Resources

• Guidance on Data Needs, Availability, and Opportunities for Work Zone Performance Measures
• A Primer on Work Zone Safety and Mobility Performance Measurement
• Work Zone Performance Measures Pilot Test
• Domestic Scan on Work Zone Assessment, Data Collection, and Performance Measurement

Available at http://www.ops.fhwa.dot.gov/wz/decision_support/performance-development.htm

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Q & A