Modelling passengers’ route choice behaviour on the london underground

Modelling Passengers’ Route Choice Behaviour on the London Underground: Application of Two

Choice Modelling Approaches

Tamas Nadudvari [email protected]

Dr Ronghui Liu [email protected]

Professor Stephane Hess [email protected]

Nadudvari, Liu, HessITS Uni of Leeds

UTSG 2015, City University London

06 January 2015

mailto:[email protected]



Contents


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


Introduction


06 January 2015Source: TfL

Source: newtravelco.com

Where are the passengers in the network? How can I avoid the crowd?

How can I use Smartacard datato answer these 2 questions?


Objectives


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


Objectives


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


Case Study Network


06 January 2015

Clapham Junction

EastPudney

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 – OvalDestination zone : Waterloo – Goodge Street

Route 1: Direct (Service B)Route 2: Change at Kennigton (Service A+C)Morden

Kennington

Bank

CharingCross

Euston

C

C

C

B

B

B

B

A

A

AA

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


Initial Data


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


https://www.whatdotheyknow.com/request/london_underground_timetables

http://www.directenquiries.com/londonunderground.aspx

Preliminary analysis (RODS)


06 January 2015

Waterloo LU EmbankmentCharing 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


Preliminary analysis (RODS)


06 January 2015


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


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%


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-zoneZone-to-stationSmaller range

Zone to zone 31%


Bayesian Modelling Framework (BMF)


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.


Oyster Journey Time (OJT) frequencies

No data

Few dataUnable to fit distribution

Zonal Journey Time (ZJT) frequencies

Bigger dataset better to fit distribution



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=




06 January 2015

ZJT frequencies

ZJT [min]

Oys

ter

tran

sact

ion

s [#

]

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




06 January 2015

ZJT frequencies

ZJT [min]

Oys

ter

tran

sact

ion

s [#

]

Supposing one route

Setting a Gaussian distribution

Calculating parameters

Comparing with Scheduled Journey Time (SJT)

Mean = 27.84 minSD = 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


Random Utility Maximisation (RUM)


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


Calibrated parameters in Raveau 2014

RUM



06 January 2015



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


Embankment

Charing Cross

Leicester Square

Goodge Street

B

B

B

A

C

C

Route 1: 5.60 min

Route 2: 2.98 min

Defaultvalue

Adjustment for AM peak

Applied value




06 January 2015



Departure/ Arrival same platform → Access and Egress times identicalInterchange: adjacent platforms → Short interchange time: 0.09 min.

Parameter: θwalk=-0.299-0.048*50%=-0.323

Defaultvalue

Adjustment for women

Percentage of women

Applied value

www.trainweb.org www.directenquiries.com commons.wikimedia.org


http://www.trainweb.org/


http://commons.wikimedia.org/



06 January 2015


Passengers’ perception on transfer depends:• Gradient: Ascending/Descending/Level• Assistance: Yes/Semi/No (elevator, escalator)

Defaultvalue

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


http://www.trainweb.org/


http://commons.wikimedia.org/



06 January 2015


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




06 January 2015

Utility

𝑃𝑖 =𝑒𝑈1

𝑒𝑈1+𝑒𝑈2=

𝑒−2.671

𝑒−2.671+𝑒−2.138=37%

𝑈1 = 𝑇𝑤𝑎𝑖𝑡,1 ∙ 𝜃𝑤𝑎𝑖𝑡 + 𝑇𝑤𝑎𝑙𝑘,1 ∙ 𝜃𝑤𝑎𝑙𝑘 +∙ 𝜃𝑇𝑅,1 =

5.60 ∙ −0.477 + 0 + 0 = − 2.671

𝑈2 = 𝑇𝑤𝑎𝑖𝑡,2 ∙ 𝜃𝑤𝑎𝑖𝑡 + 𝑇𝑤𝑎𝑙𝑘,2 ∙ 𝜃𝑤𝑎𝑙𝑘 +∙ 𝜃𝑇𝑅,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



06 January 2015


Morden

South Wimbledon

Colliers Wood

Tooting Broadway

Tooting Bec

Clapham Common

Balham

Clapham South

Clapham North

Stockwell

Oval

Waterloo


Embankment

Charing Cross

Leicester Square

Goodge Street

Indirect route: Save 2.6 min

𝑃𝑖 =𝑒𝑈1

𝑒𝑈1 + 𝑒𝑈2=

1

1 + 𝑒𝑈2−𝑈1

Morden – Goodge Street: 41,4 minOval – Waterloo: 14,9 minPerceived same to save 2.6 min for 2 cases?

Cost dampingDALY, A. 2010. Cost Damping in Travel Demand Models - Report of a study for the Department for Transport. RAND Europe.

Probability from utility difference

𝑇𝑤𝑎𝑖𝑡,1 − (𝑇𝑤𝑎𝑖𝑡,2−𝑇𝑤𝑎𝑙𝑘,2) =

5.60-(2.98-0.09)=2.6 min


Conclusions and further research


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


Conclusions and further research


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?


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.



06 January 2015

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06 January 2015

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Modelling passengers’ route choice behaviour on the london underground

Economy & Finance