outline
DESCRIPTION
Passenger Demand, Tactical Planning, and Service Quality Measurement for the London Overground Network Michael Frumin MIT June, 2010. 1. Outline. Tactical Planning. Passenger Demand. Automatic Data. Service Quality (Measurement). 2. Data Collection and OD Estimation. Calibration. - PowerPoint PPT PresentationTRANSCRIPT
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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
6
Loadweigh: Calibration Model
6
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
7
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
9
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
9
10
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
11
Next: Origin-Destination Matrix Estimation
11
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
13
OD Result Determines Ridership Estimate
13
OD Matrix
Boardings & Alightings
Link FlowsX X
14
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
15
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
16
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
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
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
!
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
27
1 - SWT/headway
28
Spring 2008: EJT by Scheduled Service
28
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)
29
New “3 + 3” Service Plan: 20 April, 2009
29
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
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?)
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?)
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
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?)