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An Approach for Base Transit Trip Matrix Development: Sound Transit EMME/2 Model Experience. Sujay Davuluri Parsons Brinckerhoff Inc., Seattle October, 2006. Project Motivations. Need to create an accurate base transit trip matrix

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an approach for base transit trip matrix development sound transit emme 2 model experience

An Approach for Base Transit Trip Matrix Development: Sound Transit EMME/2 Model Experience

Sujay Davuluri

Parsons Brinckerhoff Inc., Seattle

October, 2006

project motivations
Project Motivations
  • Need to create an accurate base transit trip matrix
  • Difficult to obtain such a matrix from traditional regional models
  • Survey data have limitations
  • But, ridership counts are rich and readily available
matrix estimation process
Matrix Estimation Process
  • Assemble/analyze key input data:
    • Current surveys
    • Transit network
    • Ridership counts data
  • Develop a seed matrix
    • INRO developed macro
transit surveys
Transit Surveys
  • Primary Source of User Data
  • Travel Patterns (O-D Estimation)
  • System/Route Level Planning
  • Consumer Feedback
  • Improvement of Service
  • Demographics Characteristics
  • Marketing
types of transit surveys
Types of Transit Surveys
  • Transit On-Board
    • Most Frequently Used
    • Self Administered
    • On Board/Stations/Key Transfer Points
  • Intercept Surveys
    • Personal Interviews
    • On Board/Stations/Key Transfer Points
  • Other Types
    • Telephone
    • Web Based
    • Mail Surveys
limitations of surveys
Limitations of Surveys
  • Difficulties in conducting
    • Significant planning required
    • Choosing the right methodology
    • Resource allocation
  • Low Participation Rate
    • Respondents lack of interest
    • Complex/long questionnaire
    • Language/literacy barriers
    • Large sample size to compensate
limitation of surveys cont
Limitation of Surveys (Cont…)
  • Sample Bias
    • Sample not representative
    • Coverage area not extensive
    • Response errors
    • Measurement/processing errors
  • Affordability
    • High Costs
    • Significant time investment
    • Highly detailed analysis required for OD estimation
limitation of surveys cont1
Limitation of Surveys (Cont…)
  • Legal Challenges
    • Restrictions on certain surveys
    • Ban on roadside interviews in Florida
    • Privacy laws
automated passenger counts
Automated Passenger Counts
  • Automated
  • Relative ease in collection
    • Improvements in technology
    • Reduction in Bias
  • Data Quality
    • Richer Data than a survey
    • Elimination of driver involvement
  • Accurate load profiles for each route
  • Rich Data Source
  • Cheaper Computer Storage and Processing
matrix estimation
Matrix Estimation
  • Networks
    • PM Peak (3 Hrs)
    • Off Peak (18 Hrs)
    • Updated to existing conditions
  • Model Coverage
    • Three County Region
    • Five different transit operators
  • Modes
    • Bus, Light Rail, Commuter Rail, Street Car
matrix estimation cont
Matrix Estimation (Cont…)
  • Seed Matrix
    • Created originally from 1992 Survey
    • Separate for PM Peak & Off Peak
    • Filling of zero value cells
    • Rescale of trip length frequency from regional PSRC model
    • Updated with enriched data from recent surveys
      • Specific route level surveys
      • Journey to Work Data
filling of zero cells
Filling of Zero Cells
  • Need
    • Changes in transit service since 1992
    • New transit lines
    • New transit markets
    • Update with new travel patterns
  • New opened cells given a value of 0.5
counts
Counts
  • Provided by local transit agencies
  • Detailed counts for majority of the routes
  • Hourly data for a 24-hr period
  • Key features
    • Total Routes – 398
    • Routes with detailed counts – 263
    • Total number of count locations – 4,203
    • Average counts per line – 16
placement of counts for me
Placement of Counts for ME
  • Multiple locations
  • Based on load profiles
  • Park & Ride demand estimation
  • Key features
    • Locations for the 263 routes – 782
    • Average counts per line – 3
    • Maximum count locations – 15
    • Locations for the rest of 135 routes – 177
validation
Validation
  • Rigorous Approach
  • Comparisons with Observed data
    • Segment level loads
    • Route level boardings
    • Line travel times
    • Screenlines
    • Average trip length
    • Boardings by operator
conclusions
Conclusions
  • Matrix Estimation – a viable approach to complement survey data
  • Requires extensive ridership counts
  • Possible to match load profiles
  • Special analysis to create a seed matrix
  • Periodical update of base trip matrix
  • Not recommended for areas with sparse transit markets/coverage
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