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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