11 passenger demand, tactical planning, and service quality measurement for the london overground...

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Passenger Demand, Tactical Planning, and Service Quality Measurement for

the London Overground Network

Michael FruminMIT

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

Calibration Estimation

4

Loadweigh: Industry Experience

• Sensors in airbag suspension– Average of 20 samples/second between stations

• Demon Info Systems: “Accurate to within ± 20 people @ 95% for a 3 car train” → σ = 10

• Southern Railways: “± 5% @ 95%” → σ = 2.5%– ±5% of 400 passengers = ± 20

– “automatic counts more trustworthy than manual”

• Nielsen, et al (2008) in Copenhagen: σ = 14 → ± 28 people @ 95%– Financial implications

4

5

Time of Day

We

igh

t (kg

)

0

10,000

20,000

30,000

40,000

04:00 09:00 14:00 19:00 00:00

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, 2009

5

Time of Day

We

igh

t (kg

)

0

10,000

20,000

30,000

40,000

04:00 09:00 14:00 19:00 00:00

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Loadweigh: Calibration Model

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Weight (kg)

kg/ pass

Count (pass)

Tare (kg)

Estimate of standard deviation of error (in pass)=

Count (pax)

We

igh

t (kg

)

5000

10000

15000

20000

25000

50 100 150 200 250 300Count (pax)

We

igh

t (kg

)

10000

20000

30000

40000

100 200 300 400

All Data Terminals Only

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Loadweigh: Calibration Results

7

8

Loadweigh: Residuals

8

Count (passengers)

Re

sid

ua

l (kg

)

-5,000

0

5,000

10,000

100 200 300 400

Model

All Data

Terminals Only

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Loadweigh: Implications

• Found: σ = 10.8 → ± 21.2 @ 95%– average 4 - 5 obs for ± 10 @ 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

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Loadweigh: Recommendations

• To begin with, assume:

– 80kg/passenger

– ±10 passengers/train @ 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

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Next: Origin-Destination Matrix Estimation

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1212

Origin-Destination Matrix Estimation

Counts of train loads on each link

(now: manualfuture: 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

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OD Result Determines Ridership Estimate

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OD Matrix

Boardings & Alightings

Link FlowsX X

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OD Estimation Results

0 50 100 150 200

050

100

150

200

flowOy ster

flow

estim

ate

d

0 200 400 600 800

020

040

060

080

0

flowOy ster

flow

estim

ate

d

14

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OD: Expansion by Line

flowOyster

flow

estim

ated

0

200

400

600

800NLL

0 200 400 600 800

GOB

0 200 400 600 800

WAT

0 200 400 600 800

WLL

0 200 400 600 800

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OD Estimation: Validation Summary% Error: Total Boardings

-30.0%

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

NLL WAT WLL GOB All

RailPlan

Oyster-Based

Mean Absolute % Error: Station Level Boardings

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

NLL WAT WLL GOB All

RailPlan

Oyster-Based

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OD Estimation: Validation

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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

Percent Absolute Error

De

nsi

ty

0.00 0.05 0.10 0.15 0.20

0.00 0.05 0.10 0.15 0.20

5 Days40 Days

Percent Error

De

nsi

ty

0.00 0.01 0.02 0.03 0.04 0.05

0.00 0.01 0.02 0.03 0.04 0.05

5 Days40 Days

!

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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

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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.)?

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Next: Service Quality Measurement and Tactical Planning

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Service Quality Measurement and Tactical Planning for the North London Line

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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

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NLL Service Plan: Before

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Uneven AM Peak headways from SRA: 16,4,10,15,15,8,7,15,9,6,15,11,5,15,9,6,15

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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

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Oyster + Schedule = SWT & EJT (an Example)

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• 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

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Oyster + Schedule = SWT & EJT (Visually)

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Spring 2008: Arrival Behavior

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1 - SWT/headway

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Spring 2008: EJT by Scheduled Service

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Time of Departure

Da

ily M

ea

n T

ota

l EJT

(m

in)

0

200

400

600

800

1000

1200

07:07

SRA/R

MD

07:12

SRA/C

LJ

07:22

SRA/R

MD

07:37

SRA/R

MD

07:52

SRA/R

MD

07:59

SRA/C

MD

08:06

SRA/R

MD

08:22

SRA/R

MD

08:30

SRA/C

LJ

08:37

SRA/R

MD

08:52

SRA/R

MD

09:03

SRA/R

MD

09:07

SRA/R

MD

09:22

SRA/R

MD

09:31

SRA/C

MD

09:37

SRA/R

MD

09:52

SRA/R

MD

Total EJT = Avg. EJT x Market Size (Oyster)

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New “3 + 3” Service Plan: 20 April, 2009

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Even AM Peak headways from SRA(at new platform): 10,10,10,8,12,10,10,10,10,10,10,10,10,13,15,15,15

5-6 minutes extra running time en-route

1-2 minutes less running time

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“3 + 3” Evaluation: North London Line

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• 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?)

Study Period PPM EJT OJT EJT OJTBefore "3+3" 79.7% 2.29 25.69 1.39 17.42After "3+3" 92.4% 1.68 25.51 1.75 17.06After - Before 12.7% -0.61 -0.18 0.36 -0.36

NLL NLL Core (SRA->CMD)

+ Scheduled

Journey Times ↑

= EJT ↓↓ (better?)

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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?

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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)

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3333

Questions? Comments?

mfrumin@mtahq.org (as of 6 July)

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Appendix: “3 + 3” Comparative Evaluation

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• 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?)