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  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  Assemble/analyze key input data: Current surveys Current surveys Transit network Transit network Ridership counts data Ridership counts data  Develop a seed matrix INRO developed macro INRO developed macro

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  Transit On-Board Most Frequently Used Most Frequently Used Self Administered Self Administered On Board/Stations/Key Transfer Points On Board/Stations/Key Transfer Points  Intercept Surveys Personal Interviews Personal Interviews On Board/Stations/Key Transfer Points On Board/Stations/Key Transfer Points  Other Types Telephone Telephone Web Based Web Based Mail Surveys Mail Surveys

Limitations of Surveys  Difficulties in conducting Significant planning required Significant planning required Choosing the right methodology Choosing the right methodology Resource allocation Resource allocation  Low Participation Rate Respondents lack of interest Respondents lack of interest Complex/long questionnaire Complex/long questionnaire Language/literacy barriers Language/literacy barriers Large sample size to compensate Large sample size to compensate

Limitation of Surveys (Cont…)  Sample Bias Sample not representative Sample not representative Coverage area not extensive Coverage area not extensive Response errors Response errors Measurement/processing errors Measurement/processing errors  Affordability High Costs High Costs Significant time investment Significant time investment Highly detailed analysis required for OD estimation Highly detailed analysis required for OD estimation

Limitation of Surveys (Cont…)  Legal Challenges Restrictions on certain surveys Restrictions on certain surveys Ban on roadside interviews in Florida Ban on roadside interviews in Florida Privacy laws Privacy laws

Automated Passenger Counts  Automated  Relative ease in collection Improvements in technology Improvements in technology Reduction in Bias Reduction in Bias  Data Quality Richer Data than a survey Richer Data than a survey Elimination of driver involvement Elimination of driver involvement  Accurate load profiles for each route  Rich Data Source  Cheaper Computer Storage and Processing

Matrix Estimation  Networks PM Peak (3 Hrs) PM Peak (3 Hrs) Off Peak (18 Hrs) Off Peak (18 Hrs) Updated to existing conditions Updated to existing conditions  Model Coverage Three County Region Three County Region Five different transit operators Five different transit operators  Modes Bus, Light Rail, Commuter Rail, Street Car Bus, Light Rail, Commuter Rail, Street Car

ME (Cont…)

Matrix Estimation (Cont…)  Seed Matrix Created originally from 1992 Survey Created originally from 1992 Survey Separate for PM Peak & Off Peak Separate for PM Peak & Off Peak Filling of zero value cells Filling of zero value cells Rescale of trip length frequency from regional PSRC model Rescale of trip length frequency from regional PSRC model Updated with enriched data from recent surveys Updated with enriched data from recent surveys Specific route level surveysSpecific route level surveys Journey to Work DataJourney to Work Data

Filling of Zero Cells  Need Changes in transit service since 1992 Changes in transit service since 1992 New transit lines New transit lines New transit markets New transit markets Update with new travel patterns Update with new travel patterns  New opened cells given a value of 0.5

Filling of Zero Cells (Cont…) PM Peak Seed Matrix Analysis Off Peak Seed Matrix Analysis # Cells% Cells # Cells% Cells Step Survey Data18, % 16, % Step 2New Routes Surveys1, % 2, % Step 3Regional Model73, % 75, % Step 4JTW Transit Survey4, % 4, % Step 5Key Markets2, % 2, % Total 99, % 99, %

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 Total Routes – 398 Routes with detailed counts – 263 Routes with detailed counts – 263 Total number of count locations – 4,203 Total number of count locations – 4,203 Average counts per line – 16 Average counts per line – 16

Daily Counts PM Peak Counts FromToInboundOutbound InboundOutbound LAKE CITY WY/ NE 133 LK CITY WY / NE 125 ST RAVENNA NE / NE 80 ST AV NE / NE 80 ST AV NE / NE 65 ST UNIVERSITY / NE 45 ST EASTLAKE E / HARVARD E CONVTN PL STA UNIVERSITY STA INTER DIST STA Counts (Cont…)

Placement of Counts for ME  Multiple locations  Based on load profiles  Park & Ride demand estimation  Key features Locations for the 263 routes – 782 Locations for the 263 routes – 782 Average counts per line – 3 Average counts per line – 3 Maximum count locations – 15 Maximum count locations – 15 Locations for the rest of 135 routes – 177 Locations for the rest of 135 routes – 177

Placement of Counts (Cont…)

Validation  Rigorous Approach  Comparisons with Observed data Segment level loads Segment level loads Route level boardings Route level boardings Line travel times Line travel times Screenlines Screenlines Average trip length Average trip length Boardings by operator Boardings by operator

Validation (Cont…)

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