Outline
description
Transcript of Outline
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Passenger Demand, Tactical Planning, and Service Quality Measurement for
the London Overground Network
Michael FruminMIT
June, 2010
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Outline
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Passenger Demand
Tactical Planning
Service Quality (Measurement)
Automatic Data
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Data Collection and OD Estimation
Expensive Manual
Infrequent
Cheaper Automatic Constant
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Calibration Estimation
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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
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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
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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
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Loadweigh: Residuals
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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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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
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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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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?)