Presentation by Tamas Nadudvari at UTSG 2015. www.its.leeds.ac.uk/people/t.nadudvari www.city.ac.uk/utsg-2015/programme

1. Modelling Passengers’ Route Choice Behaviour
on the London Underground: Application of Two
Choice Modelling Approaches
Tamas Nadudvari tstn@leeds.ac.uk
Dr Ronghui Liu R.Liu@its.leeds.ac.uk
Professor Stephane Hess S.Hess@its.leeds.ac.uk
Nadudvari, Liu, Hess
ITS Uni of Leeds
UTSG 2015,
City University London
06 January 2015

2. Contents
UTSG 2015,
City University London
06 January 2015
• Introduction
– Objectives
– Case study network
– Initial data
– Preliminary analysis
• Application of choice modelling approaches
– Bayesian Modelling Framework (BMF)
– Random Utility Maximisation (RUM)
• Conclusion
– Conclusion
– Further research
Nadudvari, Liu, Hess
ITS Uni of Leeds

3. Introduction
UTSG 2015,
City University London
06 January 2015
Source: TfL
Source: newtravelco.com
Where are the passengers in the network? How can I avoid the crowd?
How can I use Smartacard data
to answer these 2 questions?
Nadudvari, Liu, Hess
ITS Uni of Leeds

4. Objectives
UTSG 2015,
City University London
06 January 2015
Transit Assignment Model
Route Choice Model Path Generation Model
• Bayesian Modelling Framework (BMF) (Fu 2014)
FU, Q., LIU, R. & HESS, S. 2014. A Bayesian Modelling Framework for Individual
Passenger’s Probabilistic Route Choices: a Case Study on the London Underground.
Transportation Research Board (TRB) Annual Meeting.
oWhich route passengers have chosen?
oInfer route choice from Observed Journey Time (OJT)
Apply for case study network Compare results Apply in TAM
Nadudvari, Liu, Hess
ITS Uni of Leeds

5. Objectives
UTSG 2015,
City University London
06 January 2015
Transit Assignment Model
Route Choice Model Path Generation Model
• Random Utility Maximisation (RUM) (Raveau et al 2014)
RAVEAU, S., GUO, Z., MUÑOZ, J. C. & WILSON, N. H. M. 2014. A behavioural comparison
of route choice on metro networks: Time, transfers, crowding, topology and socio-
demographics. Transportation Research Part A: Policy and Practice, 66, 185-195.
oWhich route passengers would choose to maximise utlity?
oUtility from attributes (time, transfer, crowding, topology, socio-
demographics)
Apply for case study network Compare results Apply in TAM
Nadudvari, Liu, Hess
ITS Uni of Leeds

6. Case Study Network
UTSG 2015,
City University London
06 January 2015
Clapham
Junction
East
Pudney
LU
SC
Tooting
London Underground (LU)
Northern line
•Via Bank
•Via Charing Cross
Services
•A: From Morden, via Bank (2-4 min)
•B: From Morden, via CX (10-15 min, peak)
•C: From Kennington via CX (3 min)
Origin zone: Morden – Oval
Destination zone : Waterloo – Goodge Street
Route 1: Direct (Service B)
Route 2: Change at Kennigton (Service A+C)Morden
Kennington
Bank
Charing
Cross
Euston
C
C
C
B
B
B
B
A
A
A
A
Morden
South Wimbledon
Colliers Wood
Tooting Broadway
Tooting Bec
Clapham Common
Balham
Clapham South
Clapham North
Stockwell
Oval
Waterloo
Tottenham Court Road
Embankment
Charing Cross
Leicester Square
Goodge Street
Kennington
B
B
B
A
C
C
Nadudvari, Liu, Hess
ITS Uni of Leeds

7. Initial Data
UTSG 2015,
City University London
06 January 2015
• 5% Individual Oyster data, 4 week (06/02-05/03/2011) →
2676 transactions of 153 regular commuters (min 15 days)
Case study network (Northern line), weekday, AM peak
• Timetable data
https://www.whatdotheyknow.com/request/london_underground_timetables
• Access Egress Interchange (AEI) data (06/02-05/03/2011)
• Station layout, Direct Enquires (DE)
http://www.directenquiries.com/londonunderground.aspx
• Rolling Origin and Destination Survey (RODS) (1998-2010) →
6330 respondents for case study network
Nadudvari, Liu, Hess
ITS Uni of Leeds

8. Preliminary analysis (RODS)
UTSG 2015,
City University London
06 January 2015
Waterloo LU Embankment
Charing
Cross LU
Leicester
Square
Tottenham
Court Rd
Goodge
Street Total
Morden 26 121 107 107 129 69 559
South Wimbledon 32 26 21 107 69 53 308
Colliers Wood 80 31 87 65 159 71 493
Tooting Broadway 77 71 120 198 158 73 697
Tooting Bec 71 94 44 161 49 71 490
Balham LU 75 147 165 312 233 100 1032
Clapham South 82 126 93 144 71 82 598
Clapham Common 53 148 100 103 203 49 656
Clapham North 48 48 23 40 126 211 496
Stockwell 43 99 70 148 135 72 567
Oval 24 42 43 97 126 102 434
Total 611 953 873 1482 1458 953 6330
Number of RODS Respondents
Few respondents
for station-to-
station OD pairs
Greater dataset
for zone-to-zone
OD pairs
Nadudvari, Liu, Hess
ITS Uni of Leeds

9. Preliminary analysis (RODS)
UTSG 2015,
City University London
06 January 2015
Waterloo LU Embankment
Charing
Cross LU
Leicester
Square
Tottenham
Court Rd
Goodge
Street Zonal
Morden 15% 11% 9% 8% 24% 62%
South Wimbledon 38% 23% 71% 87% 72% 91%
Colliers Wood 11% 35% 31% 58% 20% 73%
Tooting Broadway 25% 25% 30% 35% 27% 58%
Tooting Bec 14% 22% 55% 46% 18% 56%
Balham LU 16% 29% 56% 56% 21% 9%
Clapham South 38% 14% 43% 10% 23% 23%
Clapham Common 25% 10% 34% 48% 7% 16%
Clapham North 27% 15% 39% 23% 10% 26%
Stockwell 58% 35% 40% 17% 10% 25%
Oval 17% 38% 14% 59% 5% 30%
Zonal
Route choice probability of the direct route
Waterloo LU Embankment
Charing
Cross LU
Leicester
Square
Tottenham
Court Rd
Goodge
Street Zonal
Morden 15% 11% 9% 8% 24% 62% 20%
South Wimbledon 38% 23% 71% 87% 72% 91% 73%
Colliers Wood 11% 35% 31% 58% 20% 73% 34%
Tooting Broadway 25% 25% 30% 35% 27% 58% 32%
Tooting Bec 14% 22% 55% 46% 18% 56% 36%
Balham LU 16% 29% 56% 56% 21% 9% 37%
Clapham South 38% 14% 43% 10% 23% 23% 23%
Clapham Common 25% 10% 34% 48% 7% 16% 20%
Clapham North 27% 15% 39% 23% 10% 26% 21%
Stockwell 58% 35% 40% 17% 10% 25% 26%
Oval 17% 38% 14% 59% 5% 30% 28%
Zonal 25% 21% 37% 41% 19% 38%
Waterloo LU Embankment
Charing
Cross LU
Leicester
Square
Tottenham
Court Rd
Goodge
Street Zonal
Morden 15% 11% 9% 8% 24% 62% 20%
South Wimbledon 38% 23% 71% 87% 72% 91% 73%
Colliers Wood 11% 35% 31% 58% 20% 73% 34%
Tooting Broadway 25% 25% 30% 35% 27% 58% 32%
Tooting Bec 14% 22% 55% 46% 18% 56% 36%
Balham LU 16% 29% 56% 56% 21% 9% 37%
Clapham South 38% 14% 43% 10% 23% 23% 23%
Clapham Common 25% 10% 34% 48% 7% 16% 20%
Clapham North 27% 15% 39% 23% 10% 26% 21%
Stockwell 58% 35% 40% 17% 10% 25% 26%
Oval 17% 38% 14% 59% 5% 30% 28%
Zonal 25% 21% 37% 41% 19% 38% 31%
Station-to-Station
big difference
Station-to-zone
Zone-to-station
Smaller range
Zone to zone
31%
Nadudvari, Liu, Hess
ITS Uni of Leeds

10. Bayesian Modelling Framework (BMF)
UTSG 2015,
City University London
06 January 2015
Waterloo LU Embankment
Charing
Cross LU
Leicester
Square
Tottenham
Court Rd
Goodge
Street Total
Morden 0 71 68 92 100 18 349
South Wimbledon 18 36 0 15 18 15 102
Colliers Wood 19 37 20 18 96 38 228
Tooting Broadway 64 29 44 107 52 56 352
Tooting Bec 97 39 63 90 101 67 457
Balham LU 1 84 32 54 68 56 295
Clapham South 90 66 65 34 73 37 365
Clapham Common 21 35 16 0 28 18 118
Clapham North 31 39 94 7 33 30 234
Stockwell 40 8 4 13 3 35 103
Oval 37 15 0 0 18 3 73
Total 418 459 406 430 590 373 2676
Oyster transactions No dataFew data
FU, Q., LIU, R. & HESS, S. 2014. A Bayesian Modelling Framework for Individual
Passenger’s Probabilistic Route Choices: a Case Study on the London Underground.
Transportation Research Board (TRB) Annual Meeting.
Nadudvari, Liu, Hess
ITS Uni of Leeds

11. Oyster Journey Time
(OJT) frequencies
No data
Few data
Unable to fit
distribution
Zonal Journey Time
(ZJT) frequencies
Bigger dataset
better to fit
distribution

12. Bayesian Modelling Framework (BMF)
UTSG 2015,
City University London
06 January 2015
Zonal Journey Time (ZJT)
Tent Tex
tent-CO tex-CD
ZJT
ZJT: Journey time from zone centroid to zone centroid.
Tent /Tex: Entry/Exit time, Oyster data.
tent-CO / tex-CD: In veh. time bween entry/exit station and centroid, timetable
∆tacc/∆tegr: Correction due to diff. acc/egr times at stations, AEI/DE
(Tex+tex-CD +∆tegr)-(Tent )+tent-CO+∆tacc
Station
Zone centroid
ZJT=
Nadudvari, Liu, Hess
ITS Uni of Leeds

13. Bayesian Modelling Framework (BMF)
UTSG 2015,
City University London
06 January 2015
ZJT frequencies
ZJT [min]
Oyster
transactions[#]
Supposing two routes
Setting a Gaussian mixture
distribution of two components
(default case)
Calculating parameters
Mean SD Probability
[min] [min] [%]
Route 1 27.05 9.33 86%
Route 2 32.87 30.97 14%
Comparing with Scheduled
Journey Time (SJT)
Not realistic
Route In-vehicle Waiting Walking Total
[min] [min] [min]
Direct 20.5 5.60 3.60 29.70
Indirect 20.5 2.94 3.69 27.12
Nadudvari, Liu, Hess
ITS Uni of Leeds

14. Bayesian Modelling Framework (BMF)
UTSG 2015,
City University London
06 January 2015
ZJT frequencies
ZJT [min]
Oyster
transactions[#]
Supposing one route
Setting a Gaussian distribution
Calculating parameters
Comparing with Scheduled
Journey Time (SJT)
Mean = 27.84 min
SD = 4.03 min
Route In-vehicle Waiting Walking Total
[min] [min] [min]
Direct 20.5 5.60 3.60 29.70
Indirect 20.5 2.94 3.69 27.12
Nadudvari, Liu, Hess
ITS Uni of Leeds

15. Random Utility Maximisation (RUM)
UTSG 2015,
City University London
06 January 2015
RAVEAU, S., GUO, Z., MUÑOZ, J. C. & WILSON, N. H. M. 2014. A behavioural comparison of
route choice on metro networks: Time, transfers, crowding, topology and socio-
demographics. Transportation Research Part A: Policy and Practice, 66, 185-195.
Parameters already calibrated for the London Underground.
Time Transfer Crowding Topology Socio-demographics
In vehicle time is identical for two routes as it is a common line problem
In vehicle Wait Walk
Time
In vehicle
Nadudvari, Liu, Hess
ITS Uni of Leeds

16. Calibrated parameters
in Raveau 2014
RUM

17. Random Utility Maximisation (RUM)
UTSG 2015,
City University London
06 January 2015
Time Transfer Crowding Topology Socio-demographics
In vehicle Wait Walk
Further research:
• “A”: Headway: Two service, same platform?
• “B”: Wait time for infrequent service?
• “C”: Arrival from “A”. Services on time?
Now we consider: wait time = headway/2
• “A”: 2.88/2=1.44 min
• “B”: 11.20/2=5.60 min →
• “C”: 3.00/2=1.50 min
Parameter: θwait=-0.269-0.208=-0.477
Morden
South Wimbledon
Colliers Wood
Tooting Broadway
Tooting Bec
Clapham Common
Balham
Clapham South
Clapham North
Stockwell
Oval
Waterloo
Tottenham Court Road
Embankment
Charing Cross
Leicester Square
Goodge Street
B
B
B
A
C
C
Route 1: 5.60 min
Route 2: 2.98 min
Default
value
Adjustment for
AM peak
Applied
value
Nadudvari, Liu, Hess
ITS Uni of Leeds

18. Random Utility Maximisation (RUM)
UTSG 2015,
City University London
06 January 2015
Time Transfer Crowding Topology Socio-demographics
In vehicle Wait Walk
Departure/ Arrival same platform → Access and Egress times identical
Interchange: adjacent platforms → Short interchange time: 0.09 min.
Parameter: θwalk=-0.299-0.048*50%=-0.323
Default
value
Adjustment
for women
Percentage of
women
Applied
value
www.trainweb.org www.directenquiries.com commons.wikimedia.org
Nadudvari, Liu, Hess
ITS Uni of Leeds

19. Random Utility Maximisation (RUM)
UTSG 2015,
City University London
06 January 2015
Time Transfer Crowding Topology Socio-demographics
Passengers’ perception on transfer depends:
• Gradient: Ascending/Descending/Level
• Assistance: Yes/Semi/No (elevator, escalator)
Default
value
Adjustment for
level transfer
Adjustment for
assisted transfer
Applied
value
Parameter: θTR=-1.321+0.613+0.000=-0.708
www.trainweb.org www.directenquiries.com commons.wikimedia.org
Nadudvari, Liu, Hess
ITS Uni of Leeds

20. Random Utility Maximisation (RUM)
UTSG 2015,
City University London
06 January 2015
Time Transfer Crowding Topology Socio-demographics
Crowding not known →
Depends on the RC of other OD pairs →
RCs are not independent of each other →
Not only single RC problems for OD pairs →
Transit Assignment model for a network.
Common line →
Identical topological perceptions
Crowding Topology
Nadudvari, Liu, Hess
ITS Uni of Leeds

21. Random Utility Maximisation (RUM)
UTSG 2015,
City University London
06 January 2015
Utility
𝑃𝑖 =
𝑒 𝑈1
𝑒 𝑈1+𝑒 𝑈2
=
𝑒− 2.671
𝑒− 2.671+𝑒− 2.138 =37%
𝑈1 = 𝑇 𝑤𝑎𝑖𝑡,1 ∙ 𝜃 𝑤𝑎𝑖𝑡 + 𝑇 𝑤𝑎𝑙𝑘,1 ∙ 𝜃 𝑤𝑎𝑙𝑘 +∙ 𝜃 𝑇𝑅,1 =
5.60 ∙ −0.477 + 0 + 0 = − 2.671
𝑈2 = 𝑇 𝑤𝑎𝑖𝑡,2 ∙ 𝜃 𝑤𝑎𝑖𝑡 + 𝑇 𝑤𝑎𝑙𝑘,2 ∙ 𝜃 𝑤𝑎𝑙𝑘 +∙ 𝜃 𝑇𝑅,2 =
2.98∙ −0.477 +0.09∙ −0.323 + (−0.708) = − 2.138
Route Choice Probability
Direct route
Indirect route
Direct route
31 % from RODS
Nadudvari, Liu, Hess
ITS Uni of Leeds

22. UTSG 2015,
City University London
06 January 2015
Random Utility Maximisation (RUM)
Morden
South Wimbledon
Colliers Wood
Tooting Broadway
Tooting Bec
Clapham Common
Balham
Clapham South
Clapham North
Stockwell
Oval
Waterloo
Tottenham Court Road
Embankment
Charing Cross
Leicester Square
Goodge Street
Indirect route: Save 2.6 min
𝑃𝑖 =
𝑒 𝑈1
𝑒 𝑈1 + 𝑒 𝑈2
=
1
1 + 𝑒 𝑈2−𝑈1
Morden – Goodge Street: 41,4 min
Oval – Waterloo: 14,9 min
Perceived same to save 2.6 min for 2 cases?
Cost damping
DALY, A. 2010. Cost Damping in Travel Demand
Models - Report of a study for the Department for
Transport. RAND Europe.
Probability from utility difference
𝑇 𝑤𝑎𝑖𝑡,1 − (𝑇 𝑤𝑎𝑖𝑡,2−𝑇 𝑤𝑎𝑙𝑘,2) =
5.60-(2.98-0.09)=2.6 min
Nadudvari, Liu, Hess
ITS Uni of Leeds

23. Conclusions and further research
UTSG 2015,
City University London
06 January 2015
• Zone to zone OD pairs → Larger dataset, better for analysis
• Bayesian Modelling Framework (BMF)
• Observed data to infer route choice
• If 2 routes similar OJT, mixture of 2 comp. not fit well, 1 fits better
• Random Utility Maximisation (RUM)
• Scheduled data to estimate route choice
• Interdependence of crowding →
Not only RC model for OD pairs, but TAM for network
• Considers only the utility difference → Cost damping
Nadudvari, Liu, Hess
ITS Uni of Leeds

24. Conclusions and further research
UTSG 2015,
City University London
06 January 2015
• Combination of BMF and RUM
• Observed AND scheduled data to have a better picture of route choice
• Infer service taken from entry/exit time and departure/arrival time
• Passenger arrival and preference behaviour
• Arrive randomly or before the departure of service?
• Wait for preselected service or board first arriving service?
Nadudvari, Liu, Hess
ITS Uni of Leeds

25. References
• CHAN, J. 2007. Rail Transit OD Matrix Estimation and Journey Time Reliability Metrics Using
Automated Fare Data. Master of Science in Transportation, Massachusetts Institute of
Technology.
• FU, Q., LIU, R. & HESS, S. 2014. A Bayesian Modelling Framework for Individual Passenger’s
Probabilistic Route Choices: a Case Study on the London Underground. Transportation
Research Board (TRB) Annual Meeting.
• DALY, A. 2010. Cost Damping in Travel Demand Models - Report of a study for the
Department for Transport. RAND Europe.
• RAVEAU, S., GUO, Z., MUÑOZ, J. C. & WILSON, N. H. M. 2014. A behavioural comparison of
route choice on metro networks: Time, transfers, crowding, topology and socio-
demographics. Transportation Research Part A: Policy and Practice, 66, 185-195.
• SCHMÖCKER, J.-D., FONZONE, A., SHIMAMOTO, H., KURAUCHI, F. & BELL, M. G. H. 2011.
Frequency-based transit assignment considering seat capacities. Transportation Research
Part B: Methodological, 45, 392-408.
• SUN, L. 2014. Characterizing Travel Time Reliability and Passenger Path Choice in a Metro
Network. Paper presented at the hEART (European Association for Research in Transportation
) Conference, 10-12 September 2014,. Leeds.
Nadudvari, Liu, Hess
ITS Uni of Leeds
UTSG 2015,
City University London
06 January 2015

26. Thank you for your attention!
Any questions?
Nadudvari, Liu, Hess
ITS Uni of Leeds
UTSG 2015,
City University London
06 January 2015