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FHWAs Mobility Monitoring Program Monitoring
Mobility and Reliability Using Archived Traffic
Operations Data Shawn Turner (shawn-turner_at_tamu.e
du) and Tim Lomax (t-lomax_at_tamu.edu), Texas
Transportation Institute Rich Margiotta
(ram_at_camsys.com), Cambridge Systematics,
Inc. Dale Thompson (dale.thompson_at_fhwa.dot.gov),
Office of Operations, Federal Highway
Administration
EXECUTIVE SUMMARY
PROGRAM DESCRIPTION

• WHAT WE DO
• Monitor freeway performance and analyze trends in
  mobility and reliability using archived traffic
  operations data.
• Provide proof of concept and technical
  assistance to foster local/regional performance
  monitoring programs and the supporting data
  collection and archives.
• SOME MORE DETAILS ON WHAT WE DO
• Gather archived data from traffic management
  centers (for 2002 data, 23 cities participated by
  providing archived data).
• Perform quality checks and create standard
  datasets (5-minute traffic volume and speed by
  lane, with processing metadata).
• Calculate travel time-based mobility and
  reliability measures.
• Use a variety of graphics and tables to
  illustrate mobility and reliability trends, as
  well as underlying data quality
• Share and discuss the results with FHWA, state,
  and local agencies.
• Buy more data storage and return to Step 1
  above.
• WHAT WE HAVE LEARNED IN THE PAST 3 YEARS
• Data Quality Affecting Applications Data quality
  varies widely and may be limiting the credibility
  and application of results in some cities.
  Several cities have acceptable data quality and
  are integrating the archived data and performance
  measures into key decision-making processes. In
  some cities, limited roadway coverage precludes
  certain analyses. In other cities, incomplete or
  inaccurate data limits its credibility and
  hampers use of the data for decisions.
• Agencies Are Implementing Program Elements
  Various elements of the Mobility Monitoring
  Program are being locally implemented. For
  example, several agencies are using the quality
  checks for their data archives. Other agencies
  have adopted some of the analysis techniques
  and/or performance measures.

• Mobility Measures
• Travel Time Index ratio of average peak travel
  time to an off-peak (free-flow) standard, in this
  case 60 mph for freeways. For example, a value of
  1.20 means that average peak travel times are 20
  longer than off-peak travel times.
• Percent of Congested Travel the congested
  vehicle/person-miles of travel divided by total
  VMT/PMT. A relative measure of the amount of
  travel affected by congestion.
• Reliability Measures
• Buffer Time Index the extra time (buffer) needed
  to ensure on-time arrival for most trips. For
  example, a value of 40 means that a traveler
  should budget an additional 8 minute buffer for a
  20-minute average peak trip time to ensure 95
  on-time arrival.
• Planning Time Index Statistically defined as the
  95th percentile Travel Time Index, this measure
  also represents the extra time most travelers
  include when planning peak period trips. For
  example, a value of 1.60 means that travelers
  plan for an additional 60 travel time above the
  off-peak travel times to ensure 95 on-time
  arrival.
• Archived Operations Data Source
• Uses archived freeway data from traffic
  operations centers
• Level of source data varies from 20-second by
  lane to 15-minute by direction
• For 2002 data (23 cities) 7 billion total data
  records, 500 GB (uncompressed)
• Developed quality control processes and data
  quality measures
• See the charts below for information on data
  quality

• Data Completeness
• Completeness of data varies within cities and
  from year to year.
• For 2002, the percent of compete data ranged
  from 9 to 94.
• Data completeness may affect results for
  particular routes and may hamper use of data for
  decisions.

• Freeway System Coverage
• For 2002, the percent of freeway mileage covered
  ranged from 9 to 99.
• The archived data is often collected from the
  most congested freeways. Because of this
  non-random sample bias, the areawide mobility and
  reliability measures should not be used to
  compare one city to another.

2
Participating Cities and Agencies (2002 data)
Research Conducted By
Research Sponsored By
Cambridge Systematics, Inc.
Office of Operations, Federal Highway
Administration

• A Tale of Traffic in Two Cities Austin, Texas
  vs. Los Angeles, California
• The chart below contrasts the congestion levels
  in Austin, Texas with those in Los Angeles,
  California. The time-of-day chart clearly shows
  the morning and evening peak periods in both
  cities. The intensity of congestion (magnitude of
  the travel time index) and the duration of
  congestion (width of the hump) is also
  apparent. The morning peak period in Los Angeles
  is nearly as severe as the evening peak, but
  Austins morning peak is only half as severe as
  their evening peak.
• Relationships between Mobility and Reliability

SELECTED FINDINGS

• Each year, the Mobility Monitoring Program
  prepares a performance report for each city
  represented in the Program. The city reports are
  about 10 to 12 pages in length, and include a
  variety of charts and tables that describe the
  mobility and reliability trends for that specific
  city, both at the areawide level as well as for
  each directional route that is monitored.
  Selected graphics from these city reports are
  shown below to illustrate key findings.
• Ramp Metering Operations in Minneapolis-St. Paul
• The trends chart below reflects the changes to
  ramp metering in Minneapolis-St. Paul from 2000
  through 2002. The freeway ramp metering system
  was disabled in late 2000 as part of an
  experiment mandated by the state legislature.
  After the experiment, freeway ramp metering was
  continued in early 2001, but in a less aggressive
  manner that took ramp and surface street delay
  into greater consideration. The trends chart
  indicates that freeway mobility and reliability
  has not returned to the levels experienced prior
  to the ramp meter experiment.