11 Passenger Demand, Tactical Planning, and Service Quality Measurement for the London Overground Network Michael Frumin MIT June, 2010

2 Outline 2 Passenger Demand Tactical Planning Service Quality (Measurement) Automatic Data

3 Data Collection and OD Estimation Expensive Manual Infrequent Cheaper Automatic Constant 3 CalibrationEstimation

4 Loadweigh: Industry Experience Sensors in airbag suspension –Average of 20 samples/second between stations Demon Info Systems: “Accurate to within ± 20 95% for a 3 car train” → σ = 10 Southern Railways: “± 95%” → σ = 2.5% –±5% of 400 passengers = ± 20 –“automatic counts more trustworthy than manual” Nielsen, et al (2008) in Copenhagen: σ = 14 → ± 28 95% –Financial implications 4

5 Loadweigh: Exploratory Analysis Random 10% Sample Peak Load Point (Canonbury to Highbury) 8 new Bombardier 378’s with loadweigh sensors on NLL/WLL First Sample: 23 Nov, 2009 – 6 Dec,

6 Loadweigh: Calibration Model 6 Weight (kg) kg/ pass Count (pass) Tare (kg) Estimate of standard deviation of error (in pass)= All Data Terminals Only

7 Loadweigh: Calibration Results 7

8 Loadweigh: Residuals 8

9 Loadweigh: Implications Found: σ = 10.8 → ± 95% –average obs for ± 95% Assumptions: –No error in manual counts at terminals (σ↓) – Unlikely –No error in loadweigh data processing (σ ↓ ) – Maybe –No day-to-day variation (σ ↑ ) – Unlikely 9

10 Loadweigh: Recommendations To begin with, assume: –80kg/passenger –±10 95% confidence level –0 tare weight Controlled experiment/calibration (eg as did Southern) Better calibration – higher quality manual counts (and/or terminal counts), and processed/filtered loadweigh data Continue manual counts on non-loadweigh-enabled portions of LO network (1 year?) If possible, calibration of new stock

11 Next: Origin-Destination Matrix Estimation 11

12 Origin-Destination Matrix Estimation Counts of train loads on each link (now: manual future: automatic)‏ Entry/Exits counts from LO-exclusive, gated stations (automatic)‏ Additional platform counts as desired (manual)‏ Oyster Seed Matrix (automatic)‏ Fitting Process (Minimum Info)‏ Final Matrix Timebands Assignment of O/D flows to links Path Choices ‏ Network Structure Path choice independent of congestion Lots of assumptions! Boardings, Alightings, Total Pax

13 OD Result Determines Ridership Estimate 13 OD Matrix Boardings & Alightings Link Flows X X

14 OD Estimation Results 14

15 OD: Expansion by Line

16 OD Estimation: Validation Summary

17 OD Estimation: Validation 17

18 OD Estimation: Sensitivity to Loadweigh Applied to each individual measurement (i.e. onboard link count), then re-estimate the matrix Assume σ = 10, simulated 30 times, for 1 week and 8 weeks of measurements !

19 OD Estimation: Recommendations Worth doing for tactical planning at the OD level If platform counts are conducted (for direct boarding & alighting measurement), can be added to OD estimation: –11 largest stations (out of 56) have 52% of boardings & alightings (5 are LO-only and gated) –24 largest have 75% (9 are LO-only and gated) Extend to East London Line – all new loadweigh- enabled stock, many stations gated & exclusive

20 OD Estimation: Implementation In-house implementation by LU S&SD –Prototype uses RODS network data files –Completed updates for existing LO network –Forthcoming updates for ELL –Updates to RODS network assignment model –OD estimation algorithm is simple First step towards in-house London-wide Rail/Tube OD estimation S&SD (Gerry W., Geoffrey M.)? 20

21 Next: Service Quality Measurement and Tactical Planning 21

22 Service Quality Measurement and Tactical Planning for the North London Line 22 Summer, 2008: Oyster-based service quality and waiting time analysis April, 2009: Tactical “3 + 3” service plan revision Now: Service plan evaluation + Operations analysis (consultant) and operator input

23 NLL Service Plan: Before 23 Uneven AM Peak headways from SRA: 16,4,10,15,15,8,7,15,9,6,15,11,5,15,9,6,15

24 The Case for a New Service Plan Uneven headways on core segment between Stratford and Camden Road –Mismatch with “random” passenger arrivals –Contribute to overloading trains and extending dwell times Congestion from shuttle turns at Camden Road Freight interference on short intervals Complex service plan for both operators and passengers From OD Matrix: 25% Cross Willesden Jn on NLL 24

25 Oyster + Schedule = SWT & EJT (an Example) 25 One Oyster journey: Stratford → Camden Road Scheduled Waiting Time (SWT): Pax. Behavior –Tap in: 08:01 –Next scheduled departure: 08:06 –SWT = 08:06 – 08:01 = 5 minutes Excess Journey Time (EJT): Service Quality –08:06 train scheduled to arrive at Camden at 08:29 –Tap out: 08:36 –EJT = 08:36 – 08:29 = 7 minutes Fundamentally relative measures, each with respect to the published timetable

26 Oyster + Schedule = SWT & EJT (Visually) 26

27 Spring 2008: Arrival Behavior SWT/headway

28 Spring 2008: EJT by Scheduled Service 28 Total EJT = Avg. EJT x Market Size (Oyster)

29 New “3 + 3” Service Plan: 20 April, Even AM Peak headways from SRA (at new platform): 10,10,10,8,12,10,10,10,10,10,10,10,10,1 3,15,15, minutes extra running time en-route 1-2 minutes less running time

30 “3 + 3” Evaluation: North London Line 30 Shorter overall journey times Improved on-time terminal departures (SRA, RMD) Reduced dwell times (SRA → RMD) Observed Journey Times ↓ (good) + Scheduled Journey Times ↓↓ = EJT ↑ (bad?) + Scheduled Journey Times ↑ = EJT ↓↓ (better?)

31 EJT/3+3: Recommendation Maintain even intervals on NLL Use Oyster (via OXNR) to assess passenger arrival behavior (ie SWT) at National Rail stations EJT: Still a measure of relative performance – useful for improving schedules (a primary tactical planning activity), less so for longitudinal evaluation Implement EJT? –For the Overground? –For National Rail in London? –For Crossrail?

32 EJT: Open Source/Standards Implementation Perl script: MOIRA timetables → Google Transit Feed Spec (GTFS) (easy) GTFS → GraphServer open source trip-planner for efficient schedule-based routing (hard, free!) Perl script: Query GraphServer with Oyster data (easy) SQL: Link to assignment model to filter non-LO trips (easy) 32

33 Questions? Comments? (as of 6 July)

34 Appendix: “3 + 3” Comparative Evaluation 34 Shorter overall journey times Improved on-time terminal departures (SRA, RMD) Reduced dwell times (SRA → RMD) Fewer customer complaints of being “left behind” Decrease in observed journey times + increase in scheduled journey times = less EJT (good!) Decrease in observed journey times + greater decrease in scheduled journey times = more EJT (bad?)