Measuring Travel Time
Reliability in the Miami Valley


   Technical Advisory Committee
    Dayton, OH
      y ,
    Sept...
Agenda
A d
   Overview
    O er ie of FMS Data
   Definitions
   Travel Time Analysis
   Average Speed Maps
          ...
Purpose
   Travel
    Tra el Time reliability is the consistency or
                 reliabilit        consistenc
    dep...
Miami V ll T
Mi i Valley Travel Time Data
                 l Ti D
                                          MIAMI         ...
FMS D (
    Data (contd.)
              d)
   Continuous raw data provided recorded travel time for
    approximately eve...
FMS Data (contd.)
         (contd )
   The raw data was checked for estimation errors
   It was then summarized to produ...
Definitions
D fi i i
   95th Percentile Travel Time: how much delay will be
                                  ho m ch del...
Travel Time Analysis: I-70




     Free Flow Travel Time   Average Travel Time   95th Percentile Travel Time
Travel Time Analysis: I-75




     Free Flow Travel Time   Average Travel Time   95th Percentile Travel Time
Travel Time Analysis: US 35




       Free Flow Travel Time   Average Travel Time   95th Percentile Travel Time


Corrido...
Distribution of Peak Hour Average Speeds
Average S d Di ib i
  A       Speed Distributions by:
                              b
   Time of Day




Month            ...
Misery Index
Mi     I d
   Seeks to measure the length of delay of only the worst
    trips
   It is computed according ...
Comparative Congestion Indices
 C       i C        i I di
                   City             Congested Hours      Travel ...
Conclusions
C l i
   I-75
    I 75 N corridor bet een US 35 and I 70 is the most
                     between            ...
Challenges
   Data Intensive Exercise: continuous supply of error-
    free data
    f d t requiredi d
   Hardware Capab...
Future S
F      Steps
   Continue pdating
    Contin e updating database with new data from
                             ...
Questions and Answers
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Measuring Travel Time Reliability in the Miami Valley

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Measuring Travel Time Reliability in the Miami Valley

  1. 1. Measuring Travel Time Reliability in the Miami Valley  Technical Advisory Committee Dayton, OH y , September 16, 2010
  2. 2. Agenda A d  Overview O er ie of FMS Data  Definitions  Travel Time Analysis  Average Speed Maps g p p  Corridor Ranking  Comparison with Other Regions  Future Steps
  3. 3. Purpose  Travel Tra el Time reliability is the consistency or reliabilit consistenc dependability in travel times, as measured from day to day and/or across different times of the day. day  Measures of Travel Time Reliability:  Are important indicators of the health of a transportation system;  Can reveal changes in system conditions from year to year;  Can supplement existing congestion measures such as v/c ratios, vehicle hours of delay, and mean speed.
  4. 4. Miami V ll T Mi i Valley Travel Time Data l Ti D MIAMI CLARK  ODOT provided data from the Intelligent Transportation p System sensors for 36 corridor segments  Data initially provided in MS Excel spreadsheets and later dh t dl t MS Access databases GREENE MONTGOMERY Miles WARREN 0 1 2 3
  5. 5. FMS D ( Data (contd.) d)  Continuous raw data provided recorded travel time for approximately every minute for each travel segment resulting in nearly a million records of data for each month y
  6. 6. FMS Data (contd.) (contd )  The raw data was checked for estimation errors  It was then summarized to produce hourly average travel times for each segment for each day of each month of the year  Several statistics were calculated for each segment from g this condensed data:  Average Travel Time by Hour and Weekday  95th percentile Travel Time by Hour and Weekday  Free Flow Travel Time  Indices including Buffer Time Index, Travel Time Index and Planning Time Index.
  7. 7. Definitions D fi i i  95th Percentile Travel Time: how much delay will be ho m ch dela ill on the heaviest travel days  Free Flow Travel Time: travel time when there is no l i h h i congestion delay  Travel Time Index: average time it takes to travel during peak hours compared to free flow conditions  Buffer Index: extra time so one is on time most of the time  Planning Time Index: total time needed to plan for an on time on-time arrival 95% of the time
  8. 8. Travel Time Analysis: I-70 Free Flow Travel Time Average Travel Time 95th Percentile Travel Time
  9. 9. Travel Time Analysis: I-75 Free Flow Travel Time Average Travel Time 95th Percentile Travel Time
  10. 10. Travel Time Analysis: US 35 Free Flow Travel Time Average Travel Time 95th Percentile Travel Time Corridor Rankings
  11. 11. Distribution of Peak Hour Average Speeds
  12. 12. Average S d Di ib i A Speed Distributions by: b Time of Day Month Weekday
  13. 13. Misery Index Mi I d  Seeks to measure the length of delay of only the worst trips  It is computed according to the following formula: Corridors Misery Index I-75: Between US 35 and I-70 0.43 I-75: Between I-675 and US 35 0.34 I-70: Between SR49 and I-75 I 70 B t d I 75 0.26 0 26 I-70: Between I-75 and I-675 0.32 US 35: Between I-75 and I-675 0.29
  14. 14. Comparative Congestion Indices C i C i I di City Congested Hours Travel Time Index Planning Time Index Chicago, IL 9:39 1.39 1.74 Los Angeles, CA 6:28 1.29 1.59 Seattle, WA 5:22 1.29 1.72 Philadelphia, PA 6:14 1.28 1.66 Boston, B t MA 5:26 5 26 1.27 1 27 1.63 1 63 Houston, TX 3:31 1.24 1.51 Portland, OR 1:39 1.23 1.62 Atlanta, GA 4:13 1.22 1.58 Detroit, MI 3:20 1.2 1.5 Pittsburgh, PA 7:53 1.2 1.43 Minneapolis-St. Paul, MN 4:18 1.19 1.48 Orange County, CA g y, 3:34 1.19 1.46 Dayton, OH (I-75 SB PM Peak Hour) 2:00 1.15 1.5 San Francisco, CA 2:53 1.13 1.32 Riverside – San Bernardino, CA 2:58 1.1 1.25 St. Louis St Louis, MO 1:44 1.1 11 1.27 1 27 San Diego, CA 2:14 1.1 1.29 Providence, RI 2:14 1.08 1.24 Sacramento, CA 1:55 1.08 1.21 Tampa, FL 2:21 1.08 1.21 Oklahoma City, OK 1:37 1.06 1.19 Salt Lake City, UT 3:15 1.05 1.16
  15. 15. Conclusions C l i  I-75 I 75 N corridor bet een US 35 and I 70 is the most between I-70 congested freeway corridor in the Miami Valley while I-70 I 70 E from I-75 to I-675 is the least congested I 75 I 675 congested.  Average speeds remain close to the set speed limits on all corridors except the I-75 corridor during peak ll id h id d i k hours.  No discernable variation exists between different months, but the average speeds tend to be lower on weekdays and much higher on weekends except in certain construction zones.
  16. 16. Challenges  Data Intensive Exercise: continuous supply of error- free data f d t requiredi d  Hardware Capability: need appropriate computer hardware to process and store large amounts of data  Software Capability: need software applications that p y pp can handle large databases and process results fairly q quickly y
  17. 17. Future S F Steps  Continue pdating Contin e updating database with new data from ith ne ODOT  Do h l monthly and annual comparisons to hourly, hl d l i determine travel time reliability  Determine project impacts on travel times by comparing pre- and post construction data  Document findings in the Congestion Management Process Report updates. p p
  18. 18. Questions and Answers

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