ORCA Data Analysis Project: Overview. Regional Staff Committee October 15, 2015 Mark Hallenbeck, Director, TRAC-UW Alex Krieg, PSRC

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1 ORCA Data Analysis Project: Overview Regional Staff Committee October 15, 2015 Mark Hallenbeck, Director, TRAC-UW Alex Krieg, PSRC

2 Project Background Conceived of in early 2014 Partnership between PSRC, Sound Transit, WSDOT, and UW-TRAC Officially began in April 2015 Supported by a Technical Advisory Committee

3 ORCA Data Have the potential to offer wonderful insight into the actual use of transit ORCA transaction data are already collected It is free to us Needs to be combined with other data to answer many questions

4 Project Overview 1) Obtain data and convert it into new, useful analytical datasets Respecting privacy of users and businesses 2) Estimate O/D trip tables of ORCA users 3) Perform Analyses (initial ideas presented shortly) 4) Demonstrate outcomes to agencies 5) Provide coding/data flow instructions 6) Write final report

5 Project Overview Uses 2 months of ORCA Transaction data February 17, 2015 to April 14, 2015 One schedule shake up Four transit agencies + limited WSF Figure out how to do this Prove that it is worth the effort

6 Intro to ORCA Data Tracks the money Money is associated with ORCA cards E-purse or pass (by type of pass) Which transit services used Great for computing revenue sharing between agencies Keeps a record of payments to transit providers Record of trip payments

7 ORCA Transaction Data Type of ORCA card (pass / fare) Date & time of payment Includes transfers Route (and agency) on which payment is made

8 ORCA Data Business ID is maintained for every card purchased (subsidized) by an employer Allows measurement of the number of trips being supported by each employer Is used to justify the cost to the employer

9 Other Data We Have & Need Transit Schedules Real time vehicle location ORCA records agency, route, and time stamp An estimate of location given a specific time Ridership data Estimates/counts at the stop level

10 Other Data We Have & Need CTR data Location of employment Size of worksite Type of subsidy Parking prices / subsidy levels CTR Survey data Mode choice

11 Other Data We Have & Need Land use data Population Employment Built environment data Density Network characteristics Parking availability / cost Socio-economic data

12 Other Data We Have & Need Rider survey data Origins / destinations Lots of other data

13 Link These Data To Produce Analysis Data Sets Must be VERY conscious of privacy Lots of Data Janitorial Work

14 Current Progress and Challenges ORCA transaction records do not describe location, just time and vehicle ID Must connect ORCA to other databases AVL, bus assignment tables, route and stop definitions Step 1 is to obtain these data Completed

15 Step 2: Data Fusion

16 TRAC office motto of the moment Data analysis would be really fun if it weren t for the data

17 Data Processing Steps 3 & 4 Once subsidy data are connected to ORCA cards the Card IDs get hashed to protect privacy Only then do we get to connect the ORCA transaction file to specific bus stops Match time, bus ID & route to AVL information

18 Data Processing Step 5 With (hashed) Card ID by bus stop by time/date/route (including transfers) Then compute O/D Can estimate many (but not all) trips by following specific ORCA cards Do not get actual origin and destination Need to estimate them Location of origin is the stop, not the zone Stops are often located on zonal boundaries

19 Step 5 Continued What fraction of trips can we estimate O/D? Summarize those patterns Geographic Time of Day Etc

20 Data Products ORCA Boardings by stop, by card (by date, time, and route including weekdays and weekends) Two months of data Four agencies (ST, KCM, PT, CT) maybe, could include more (WSF) O/D (by time of day) patterns from those data (estimated commute trips vs other trips) ORCA cards connected to subsidy levels So we know which cards are subsidized and by how much (at least for many cards)

21 Step 6: Connect to Other Data Sets Now we have ORCA payments by stop Total ridership by stop (ons and offs) Compare O/D table Compare to Rider Survey Both connected to CTR subsidy data All kinds of GIS based socio-economic data

22 Step 7: Start the Cool Analyses

23 Analyses Compare trip making behavior Monthly pass versus electronic purse Purchased monthly passes versus employer provided passes versus subsidized passes Pass use by (residential?) land use Pass use by (employer?) land use characteristics

24 Analyses: CTR Program Impacts After including the following variables Transportation network attributes Parking price Subsidy Land use / land form Estimate the effectiveness of different transit oriented CTR programs in different built environments

25 Analyses Examine ORCA ridership versus total ridership Compare differences by location in the region Compare differences versus demographics Compare differences versus land use Understand biases in the ORCA data

26 Analyses Comparison of actual (ORCA) trip patterns against those developed from rider / nonrider survey data (~2,000,000 trips versus ~10,000 trips) Compare actual use versus use reported via the CTR survey

27 Transit Operations Planning Are there specific O/D patterns which warrant direct service that currently require a transfer? What fraction of P&R patrons transfer to a feeder bus?

28 Analyses Data for transit ridership model calibration Off-peak as well as peak period Analysis of off-peak ridership patterns Weekend analyses are also possible

29 Future Analyses Compare ORCA use after transit marketing efforts Number of trips taken? Does free pass continue to be used? What type of use? Pass? Electronic purse? How effective was that transit marketing program?

30 Future Analyses Compare ORCA use after route/schedule changes Number of trips taken? Changes in O/D How effective was that change?

31 Questions? Thanks for listening!

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