O/D Applications from Smart Card Data

1. O/D Applications from Smart Card Data Jesse Simon, Ph.D. simonj@metro.net

2. The O/D we are targeting • Origin = the initial boarding stop of a linked transit trip. Destination = the final alighting stop of a linked transit trip. • Only boardings and alightings on transit vehicles are being measured • The transit O/D not the actual O/D. • O/D involves linked trips: while only 1 vehicle can be involved (a 1-link trip), 2 or more vehicles are often involved in the trip. • On/Off counts do not tell us this info since we need to know where an individual patron starts and ends his trip, which may involve transfers.

3. WE KNOW WHAT A SINGLE CARD USER DOES ON ANY GIVEN DAY. • Every Smart Card Transaction is date, time and geo-stamped • Includes the Card ID number, which is stored on the farebox computer and downloaded to a database • Smart Card transactions now constitute 45% of all Fare transactions, and is growing

4. Differs from BART & WMATA • BART & WMATA have entry/exit system • They know actual O/D • We have entry-only farecards (smart cards) • We must infer Destination • We know the origin: first boarding stop of initial link • We infer the final link destination by matching trips • On any given day, the card’s later trip’s initial boarding stop is the inferred destination of the earlier trip. • Vice-versa: later trip destination is earlier trip’s first stop

5. Hot Topic • NYC, Chicago, Ottawa, London, Sao Paulo all have pilot programs. • Benefits: • Much larger volumes than On Board Surveys • Low marginal cost because already collected • Available in weeks rather than a year • Allows ongoing Time Series comparisons • No problems of OBS self response • Accuracy and self-selection problems ameliorated

6. O/D Inference Efforts Compared Other Cities’ Efforts tied to Modeling • Assume Day’s First & Last Trip match • Use NYC finding of 90% accuracy as reasonable approximation for modeling Our #1 audience, schedulers and schedule planners, demand greater precision • Every matched pair tested (loss of quantity, gain in validity)

8. Preview: What O/D shows • Origin/Destination (O/D): Where patrons’ trips begin and where they end • The last map showed 3 O/D patterns from TAP card data: (text matches “desire line” colors) • 3rd/Vermont: Local catchment area • El Monte Station: Wide catchment area but mostly from SG Valley and Downtown LA • Metro Center: Extremely wide catchment area with heavy O/D along Rail and Harbor Freeway corridors

9. Study part of validation project Error Check, Troubleshoot, Correct: • Apply diagnostic reporting & liaison with Maintenance that made APC work better at LACMTA than elsewhere • Develop prototype applications that identify problems • Project now developing reports & liaison • Troubleshooting methods/reporting subject of another paper.

10. Two Tasks: • Form Linked Trips • Match Linked Trips to Infer O/D A successful Match: First/Last stops match • Outgoing 3-Link AM trip (black) • Incoming 2-Link PM trip (brown)

11. 1st Step: Defining a Linked Trip • Before Matching Linked Trips, form them • On any given date on any given card there may be one or many fare transactions • Each transaction represents a boarding which, in this context, we call a “link” • The question is: which links become part of a linked trip • The Solution:

12. Forming Linked Trips • What Other Agencies Do • Use “fixed temporal thresholds” between transactions • Less than 30 or 90 minute elapsed time between boardings, or less than 1 hour wait time at stops • Criticized for not accounting for variation in trip length and service levels • Use “Spatial-Temporal” path (Ottawa demo project) • Uses alighting time of first link destination stop and walk speed of 2.7 mph + 5 minutes to next stop. If boarding on the 2nd link can be made in that time period then link is assumed. (Chu & Chapleau)

13. Forming Linked Trips • What was done here • Link is part of linked trip if time elapsed between successive boardings greater than 3 mph • Retains Spatial-Temporal context where “miles” is spatial and “per hour” is temporal • Processing pragmatics: • Ottawa viewed thousands of trips using multiple data sources, including referring to other passenger arrival times; • We will be regularly looking at millions of trips so we want to keep it to one data source (TAP Cards), one user at a time • 3 mph is more a function of service provided than patron ability: • Very few instances where Metro service, including headways, was lower than 3 mph between any two connecting lines at any two stops (not even downtown LA) • Other agencies may need lower mph to capture links

14. 2nd Step: O/D Matching

16. Matching Stop Areas Matching Lines connected to Stop Areas based on geostamp of boarding Location • Actually where the origin and destination is • Eliminates database Line attribution error • Foreign Line recording • Trunk vs. Branch Line designation

17. Line Destiny Report Linked Trip generation is important in itself • Even without inferring final destination • Product: Report that rank orders transfers to Lines from any given Line • Much more complete dataset than O/D because unmatched trips are included • 75% of trips put into Linked trips (will be much higher in future); compares to 38% O/D matches. • Fewer inferences involved

18. Selection Bias • The 38.3% matching among Fare Card users points to selection bias • Appeared dramatically in study of a commuter college area, where home-school-work-home tours could not be matched. • But generally, the core group of multi-use fare card users are those who use Metro 5 days a week. • 5 day a week users are 82% of all users, and 82% of their trip productions are either home-work or home-school • Conclusion: biased sample of total transit travel but representative of core travel.

20. General Findings • The basic travel pattern is the 1-link trip: 57.3% are 1-link • Linkage from any given Line is widely distributed among transfer points • Median highest % of trips destined to another Line is 4%. • Only 6 Lines have 10% of patrons destined to another specific Line • No Metro Rapid Line has 10% of its patrons transfer to the Local Line on the same corridor • Contradicts Original assumptions

22. What a Map Application Looks Like See accompanying Map Destinations of Trips Originating on Line 901 • The Orange Line distributes people to: • Other Orange Line Stations (most frequent) • The Red & Purple Line corridors • Downtown LA • Hollywood • Small concentration in Westwood • Many N/S Corridors throughout the SFV, especially Van Nuys Blvd. • It does not distribute to the Blue or Gold Lines • This map does not show trips where Orange Line is an intermediate link on a 3 link trip.

23. Application: Travel to a specific area Background: Original research was on shortening Line 761 route. • Question arose as to O/D for people in Westwood area generally. • In this case “Westwood” was defined as areas in or near Westwood that people on Van Nuys corridor traveled to • All origins, including those outside SFV, included in the accompanying map

24. Use of Census Tracts in Map • Census Tracts are color coded to show intensity of travel (number of trips) to each of them. • By coloring tractfor destinations and tract interiors for origins, origins and destinations can be compared. • Census Tracts have demographics attached to them. Travel behavior can then be tied to demographics. • Census Blocks, Block Groups, TAZs or any other geographic area can be used outlines

27. Findings • There are two destination tracts in Westwood that dwarf all the others: UCLA (238 trips) and the tract along and south of Wilshire by Westwood Boulevard (128 trips) • An optimal stop on the subway to the sea would be on Wilshire between these two census tracts. • Most of the origin tracts lie on three main corridors: Van Nuys (with a short jog on Ventura), Wilshire/Whittier, and Sunset • The heavy origins are as far north as Nordhoff on Van Nuys • The heavy origins stretch very far to the east on both Wilshire/Whittier and Sunset • They trace out a strong path for potential corridors of the subway to the sea. • All three corridors represent some long trip-making. • The UCLA tract has the most origins, which indicates travel within Westwood. • These are short trips.

28. Next Steps: at LACMTA • Work with UFS on data errata and, if needed, structure requirements • Rewrite programs to retain information on intermediate trips • This will allow additional kinds of maps, such as O/D of trips where Line 901 is an intermediate link • Also important to Modeling as potential data client: critical paths and links per trip • Create data structures and routines for regular processing of O/D datasets for queries and mapping

29. Next Steps: Research Community • Coordinating with On/Off data • Coordinating data from automatic sources (Fare Card Data, On/Off counts) with intentionally developed survey data • Recognizing, exploiting and combining their strengths and weaknesses • Calibrating or supplementing massive datasets with survey data