O/D Applications from Smart Card Data

Jesse Simon, Ph.D.simonj@metro.net

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.

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

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

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

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)

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

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.

A successful Match: First/Last stops match

• Outgoing 3-Link AM trip (black)

• Incoming 2-Link PM trip (brown)

Two Tasks:

• Form Linked Trips

• Match Linked Trips to Infer O/D

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:

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)

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

2nd Step: O/D Matching

Conceptual Version Line to Area Match Direction of Travel Line to Area Match Outgoing Trip:

First Link’s Boarding Stop Area’s Last Link’s Line Associated Line Numbers Incoming Trip: Last Link’s Line First Link’s Boarding Stop Area’s Associated Line Numbers The red and orange must each match to make an O/D pair. Both Members of the Matched Pair get an Origin and Destination The Outgoing First Link’s Boarding Stop Area will be named the Origin and Incoming First Link’s Boarding Stop Area will be named the Destination of the Outgoing Trip.

Vice-Versa for the Incoming Trip’s Origin and Destination. Stop Areas are used in matching rather than matching Line-to-Line because alternative Lines from a Stop Area can be used on the return trip.

Refined, Version: Stop Areas Matched Stop Area Match Direction of Travel Stop Area Match Outgoing Trip: First Link’s Boarding Stop Area’s Last Link’s Boarding Stop Area’s Associated Line Numbers Associated Line Numbers Incoming Trip: Last Link’s Boarding Stop Area’s First Link’s Boarding Stop Area’s Associated Line Numbers Associated Line Numbers

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

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

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.

Line Destiny Report for Sept. 7-13, 2010(Only destinations with GT 2.5% of origin boardings)

Original Line Boarded

Final Line Boarded Frequency Percent

Cumulative Percent

2 2 17,532 65.8 65.8

Total 26,658 100.0  

4 4 20,023 67.6 67.6

802 789 2.7 70.2

Total 29,632 100.0  

10 10 10,506 61.5 61.5

Total 17,077 100.0  

14 14 13,288 57.9 57.9

204 815 3.6 61.5

207 710 3.1 64.6

754 604 2.6 67.2

Total 22,939 100.0  

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

What a Map Application Looks Like

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.

See accompanying Map

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

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 tract for 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

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.

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

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