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An Algorithm to Measure Daily Bus Passenger Miles Using Electronic Farebox Data

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An Algorithm to Measure Daily Bus Passenger Miles Using Electronic Farebox Data An Algorithm to Measure Daily Bus Passenger Miles Using Electronic Farebox Data Alex Lu, Alla Reddy Operations Planning New York City Transit Authority Presented at the 90th Annual Meeting of the Transportation Research Board Washington D.C. (2011) T R A N S I T New York City Transit Notice: Opinions expressed in this presentation are those of the authors and do not necessarily reflect the official New York City Transit policy or position of Metropolitan Transportation Authority or MTA New YorkTRB City Transit. Paper #11-0368 Slide 1 Purpose and Need • Implement 100% electronic data reporting – Monthly “safety module” – Eliminates surveying, data entry, manual checking – More consistent & accurate • Algorithm requirements – Zero manual intervention – Fast: running time of a few minutes per day of data – Rely on schedules and AFC data (no GPS/AVL/APC) Photo: Adam E. Moreira New York City Transit TRB Paper #11-0368 Slide 2 NYCT’s MetroCard AFC Data • “Trip” file 73 bytes per record × about 8,000,000 bus and subway records per weekday = approximately 550 MB per weekday (3am to 2.59am next day) – partial trip records Hypothetical card with bus-only records shown: ....x....1....x....2....x....3....x....4....x....5....x....6....x....7. – no timestamps for 2653058017 20080416 55400 157 027 F02569 1 R482 0 362 2653058017 20080416 63000 157 027 F0027F 1 R480 0 494 cash transactions 2653058017 20080416 73600 157 027 F01E70 2 R494 0 153 2653058017 20080416 160000 157 027 F01E72 2 R494 0 152 2653058017 20080416 161800 157 027 F00214 1 R480 0 494 – evaders/half fares 2653058017 20080416 163600 157 027 F00129 1 R480 0 495 inconsistently logged 2653058017 20080416 184800 157 027 F020B0 3 R515 0 645 – can’t be automatically Fare Media Class (Unlimited, Pay-per-Ride) matched to schedule Transcation Code (Value Deduct, Transfer) Transaction Date and Time (Nearest Six Minutes) • “Transaction” file MetroCard ID (Serial Number) Bus Number in Hex (Subtract 0xF00000) – MetroCards only, Carrier (1 = TA, 2 = OA, 3 = MTABC) no cash data Value Deducted (0 for Unlimited Pass) Location Code (Bus Route and Direction) – no trip information – no geographic information (not ‘location-stamped’) – transaction time rounded to nearest six minutes – headsign route and directions not always correct New York City Transit TRB Paper #11-0368 Slide 3 Assumptions • Localizations anchored relative to trip origin – no en-route put-ins • Constant average speed (origin to destination) • Symmetric daily activity – Navick & Furth (2002) • Unbalanced ridership doesn’t affect pattern • End-of-trip enforced after maximum trip time – Service continues in opposing direction • Uniform non-AFC rider correction factors New York City Transit TRB Paper #11-0368 Slide 4 Typical Local Bus Constant Mileages underestimated for psgrs boarding here, Average Speeds and overestimated for those disembarking here. • Local Bus Distance Distance – Uniform speed profiles Mileages overestimated for psgrs boarding here, underestimated – Equally likely over- or for those disembarking here. under- estimation Time • Express Bus Inbound Express Bus – Many boardings in Suburban Express Drop suburban segment Boarding Segment Off – Speeds over-estimated – Mileages under-estimated Mileage under- estimation occurs DistanceDistance for all boardings. • Future Work No boardings – Use average speeds occur in express or drop-off between timepoints segments. Time New York City Transit TRB Paper #11-0368 Slide 5 3,500 Northbound Ons/Southbound Offs 3,000 Symmetric Daily 2,500 N/B Ons S/B Offs 2,000 RSQ = 0.9256 1,500 Passengers) 1,000 Activity Patterns Activity Level (Daily 500 0 3,500 Southbound Ons/Northbound Offs • Conservation of 3,000 2,500 S/B Ons N/B Of f s RSQ = 0.9670 Passengers 2,000 1,500 – Those disembarking here, Passengers) 1,000 Activity Level (Daily 500 reboard here later 0 E 204 St E 205 St • Equal & Opposite E 157 St E 163 St E 166 St E 169 St E 174 St E 180 St E 184 St Bedford Pk Bl 3 Av-E 149 St E Tremont Av Claremont Pkwy Passenger Activities 3 Av-E Fordham Rd Bathgate Av-E 172 St 172 Av-E Bathgate White Plains Rd (Subway) Webster Av-E Fordham Rd – Return passengers reboard Willis Av-E St 148 (Subway) Stops 12,000 an opposing number Cumulative and Reverse Cumulative/Ons and Offs – Minimal and balanced 10,000 triangulation 8,000 • High Correlation (BX55): 6,000 4,000 S/B Reverse Cumulative Ons – Northbound Ons versus Passengers Daily Total N/B Cumulative Ons Southbound Offs 2,000 S/B Reverse Cumulative Offs N/B Cumulative Offs 0 – R-squared > 0.90 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Stops Sequence New York City Transit TRB Paper #11-0368 Slide 6 Symmetry Analysis Results Boarding Boarding Route Type Direction R-squared Direction R-squared • Supports earlier results BX12 Crosstown-feeder EB 0.992 WB 0.996 hybrid – Pittsburgh light rail* BX29 Double-ended EB 0.983 WB 0.981 – Los Angeles bus (5 routes)** feeder BX55 Limited stop trunk NB 0.997 SB 0.999 – New York City bus B31 Low density feeder NB 0.992 SB 0.994 (sixteen routes) B42 Subway extension NB 0.978 SB 0.985 B51 Bridge route NB 0.833 SB 0.907 M15 High volume trunk NB 0.987 SB 0.993 • Two notable exceptions: route – S60 Grymes Hill M18 Branch shuttle NB 0.924 SB 0.978 Q46 Suburban trunk EB 0.972 WB 0.999 (neighbourhood circulator) Q74 Campus NB 0.904 SB 0.962 feeder/circulator – B51 Manhattan Bridge Q79 Suburban NB 0.982 SB 0.981 crosstown – Both lightly used routes Q83 Suburban feeder EB 0.995 WB 0.996 eliminated in MTA’s 2010 S4090 Suburban trunk EB 0.994 WB 0.997 S52 Suburban feeder NB 0.973 SB 0.986 Service Reduction Plan S60 Neighbourhood NB 0.698 SB 0.833 circulator – Both operate highly S79 Suburban trunk NB 0.999 SB 0.986 asymmetrical route paths * Furth, Peter G. Innovative Sampling Plans for Estimating Transit Passenger Kilometers. In Transportation Research Record 1618, TRB, Washington D.C., 1998, pp. 87–95. ** Navick, David S. and P.G. Furth. Estimating Passenger Miles, Origin-Destination Patterns, and Loads with Location-Stamped Farebox Data. In Transportation Research Record 1799, Paper No. 02-2466, TRB, Washington D.C., 2002, pp. 107–113. New York City Transit TRB Paper #11-0368 Slide 7 Asymmetrical ‘Loopy’ Routes • S60 moves 210 daily psgrs mostly ‘up the hill’ – Fewer downhill riders – Dogbone-shaped path – More stops and longer path uphill than downhill • B51 drives 900 daily psgrs ‘over the bridge’ – All stops Manhattan bound; limited stop Brooklyn bound – Baseball-cap shaped path • Substantially violate symmetry assumptions New York City Transit TRB Paper #11-0368 Slide 8 Unbalanced Route Assumption • “Unbalanced” routes Unbalanced – Significantly different daily Ridership uuinbound/outbound ridership • Reasons: – Afternoon/night travel pattern EB and modal bias differences CumulativeCumulative Ons/Offs Ons/Offs WEStops – Carpool/drop-off – Triangulation due to + ≈ + afternoon activities u u – Express bus competition • Algorithm tolerant of WB small route imbalances CumulativeCumulative Ons/Offs Ons/Offs WEStops – Integration errors are small – Correlations remains high: Area under curve is M15, B42, B31, Q46, Q83 total psgr miles New York City Transit TRB Paper #11-0368 Slide 9 Passenger miles from Empty seat miles from Direct Numerical boarding location beginning of trip to Integration to end of trip boarding location • Simple algebraic transformations – – Greatly speeds up algorithm execution – Sequential one-pass AFC file evaluation + – 600 MB of daily data analyzed in about 3 minutes u u – New York City Transit TRB Paper #11-0368 Slide 10 All Correction Factors Factor Factor Service Bus Passengers Description Req’d? Value 1 Standard In EU65 MetroCard Base “raw” passenger boarding data No — Bus Transaction File from the MetroCard AFC system. Cash Passengers Passengers not using electronic fare 2 Yes 15.4% media. Non-Farebox Passengers Passengers not interacting with farebox due to broken farebox, fare evasion, Yes 12.1%3 paper tickets, flash passes, etc. Farebox Data Passengers paying fare normally but Transmission Errors data not in EU65 file due to farebox Yes 5.4%4 data transmission malfunction. Total Adjustment Factor (Standard Bus) 32.9% Select Revenue Passenger Data Number of receipts issued by the Proof- Bus from Select Bus Service of-Payment wayside fare collection No — Service Fare Validation Machines machines (Cash and MetroCard) on the BX12 Select Bus Service. Non-Receipt Passengers Passengers without POP receipts due to fare evasion, paper tickets, and flash Yes 14.9%3 passes, etc. Fare Validation Machine Passengers paying fare normally, but Data Transmission Errors not recorded because of fare payment Yes 2.0%3 machine malfunction. Total Adjustment Factor (Select Bus) 16.9% Notes: 1. Correction factor values are expressed as a percentage of raw AFC counts and are current as of December, 2008; 2. Updated monthly; 3. Updated as required; 4. Updated every two months. New York City Transit TRB Paper #11-0368 Slide 11 Non-Farebox Ridership Survey Passengers Category Observed Percentage Notes Valid MetroCard Fare 19,630 85.4% 1. Transfers between Valid Split Fare 294 1.3% Train and B42 Bus to Invalid MetroCard Fare 449 2.0% Canarsie Beach at Canarsie Subway station Invalid Split Fare 130 0.6% occurs within fare control. Subtotal AFC Counted Passengers 20,503 89.2% Transferring passengers Front Door Non-Paying Passenger 644 2.8% are not required to swipe Rear Door Non-Paying Passenger 110 0.5% upon boarding, thus no AFC record is generated. Child Over 44" Travelling Without Fare 413 1.8% Paper Ticket 47 0.2% 2. BX12 Select Bus utilizes Child Under 44" Travelling Without Fare 557 2.4% a proof-of-payment (POP) Flash Pass, Uniform, or Official Badge 268 1.2% fare collection system.
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