Analysing route choice decisions
on Metro networks
A comparison between Santiago and London
Sebastián Raveau
Pontificia Universidad Católica de Chile
Zhan Guo
New York University
Juan Carlos Muñoz
Pontificia Universidad Católica de Chile
Nigel Wilson
Massachusetts Institute of Technology

13 min10 minContents
Modelling
Variables

StudyCases

Results &Analysis

Extensions &Applications
Route Choice
BackgroundConclusions

Route choice modelling
Traditional route choice models usually consider justtangible variables related to the level of service

travel timefarenumber of transfers

These models are sometimes refined including socioeconomic variables of the travelers

Route Choice
BackgroundRoute choice modelling

Route Choice
Background

However, this approach ignores other relevant elements that
influence route choice as:

comfort and safety
transfers accessibility
network topology
aesthetics

These variables are subjective and hard to quantify

Pathfinding Criteria

Route Choice
BackgroundPathfinding Criteria

Route Choice
BackgroundPathfinding Criteria

Route Choice
BackgroundSome people follow different criteria when deciding how toget from one point to anotherthe fastest way

the cheapest way
In a transit context, there are some additional factors

avoid walking
avoid transferring

But most consider many factors at the same time!

Study’s objectives
Understanding travelers is essential in Transportation
Planning

Identify and quantify the factors that affect the transit
users’ behaviour

Compare the preferences of transit users in London andSantiago

Modelling
VariablesWhat do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)Number of transfers
Transfer stations layout
ascendingat leveldescending

Modelling
Variables

travel timecomponents

Modelling
Variables

What do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)Number of transfers
Transfer stations layout
Transfer stations infrastructure

travel timecomponents

assistedorsemi-assistedornon-assisted

andWhat do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)Number of transfers
Transfer stations layout
Transfer stations infrastructure
Mean occupancy
Possibility of not boarding
in Londonin Santiago

Modelling
Variables

travel timecomponents
transferexperience

initial occupancy ≥ 70%
initial occupancy ≥ 85%

What do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)Number of transfers
Transfer stations layout
Transfer stations infrastructure
Mean occupancy
Possibility of not boarding
Possibility of getting a seatin Londonin Santiago

Modelling
Variables

travel timecomponents
transferexperience

initial occupancy ≤ 20%
initial occupancy ≤ 15%

What do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)Number of transfers
Transfer stations layout
Transfer stations infrastructure
Mean occupancy
Possibility of not boarding
Possibility of getting a seat
Route distance
Number of stations
Angular cost

Modelling
Variables

travel timecomponents
transferexperience
comfort andcrowding

What do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)Number of transfers
Transfer stations layout
Transfer stations infrastructure
Mean occupancy
Possibility of not boarding
Possibility of getting a seat
Route distance
Number of stations
Angular cost  d  sin 2 

Modelling
Variables

travel timecomponents
transfer
experience
comfort andcrowding

What do people take into account?

d2
T1

Modelling
Variables

T2

d3

d1
DestinationOrigin
Angular Cost =Modelling
Variables

What do people take into account?

d2T1

T2d3

d1

1

Destinatio
nOrigin1 2 Angular Cost = d1  sin   d2  sin
 2
2

Modelling
Variables

What do people take into account?

d2
T1

T2

d3

d1
Destinatio
nOrigin

1 
2 

Angular Cost = d1  sin   d2  sin
 2
2

Modelling
Variables

What do people take into account?
T2d2
T1

2d3

d1
Destinatio
nOrigin1 2 Angular Cost = d1  sin   d2  sin
 2
2

Modelling
Variables

What do people take into account?

d2
T1

T2

d3

d1
DestinationOrigin

1 2 Angular Cost = d1  sin   d2  sin
 2
2

What do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)Number of transfers
Transfer stations layout
Transfer stations infrastructure
Mean occupancy
Possibility of not boarding
Possibility of getting a seat
Route distance
Number of stations
Angular cost
Reasonable routeModelling
Variables

travel timecomponents
transferexperience
comfort andcrowding

Modelling
Variables

What do people take into account?
turning away fromthe destination

1

O

turning back to
the origin

O22

D

1

DWhat do people take into account?

Modelling
Variables

In-vehicle time
travel timecomponents
Waiting time
Walking time (when transferring)Number of transfers
transfer
experience
Transfer stations layout
Transfer stations infrastructure
Mean occupancy
comfort andcrowding
Possibility of not boarding
Possibility of getting a seat
Route distance
topological variables
Number of stations
Angular costbased on schematic maps
Reasonable routeStudyCases

Comparing Santiago and London
SantiagoLondon

Survey date2008

1998-2005

Length78 KmLines

511Stations

85

255

Transfer stations

772

Daily trips

2,300,000

3,400,000

Survey size

28,961

16,300324 KmComparing Santiago and London

Santiago Metro

StudyCases

Comparing Santiago and London

StudyCases

London UndergroundSet of alternative routes

Results &Analysis

A key element when dealing with probabilistic route choicemodels is the definition of the alternatives for the OD pairs ofinterest
Santiago
generated based on the actual choices→2 to 4 alternative routes

Londongenerated based on a labeling approach→
2 to 6 alternative routes

Results &Analysis

Estimation results
AttributeLondon Underground

Santiago Metro

Travel Time- 0.188- 16.02- 0.095- 19.57

Waiting Time- 0.311- 7.39- 0.139- 5.07

Walking Time- 0.216- 6.14- 0.155- 8.23

Number of Transfers- 1.240- 4.37- 0.632- 4.06

Ascending Transfers- 0.138- 2.57- 0.323- 2.73

Even Transfers

Parameter’s signs

0.513

3.53

n. a. (2)n. a.

Descending Transfers

0.000 (1)n. a.

0.000 (1)n. a.

Assisted Transfers

0.000 (1)n. a.

0.000 (1)n. a.

Semi-Assisted Transfers- 0.328- 6.83n. a. (2)n. a.

Non-Assisted Transfers- 0.541- 6.79- 0.262- 6.23

Mean Occupancy- 2.911- 3.48- 1.018- 5.60

Getting a Seat

0.098

2.08

 OK

0.092

3.41

- 6.06- 0.380- 2.97- 5.87- 0.024- 5.48 OK

Not Boarding- 0.430
Parameter’ssignificances
Angular Cost
- 0.065

Map Distance- 0.358- 5.76- 0.274- 5.69Number of Stations- 0.316- 5.52- 0.147- 3.10Turning Back- 0.725- 8.12- 0.141- 9.76

Turning Away

- 0.968- 8.00- 0.226- 7.11

Adjusted r 2

0.566

0.382

Results &Analysis

Marginal rates of substitution
AttributeLondonSantiago

1 min waiting

1.65 min in-vehicle

1.46 min in-vehicle

1 min walking

1.15 min in-vehicle

1.62 min in-vehicle

1 (basic) transfer

6.60 min in-vehicle

6.63 min in-vehicle

1 % of occupancy

0.16 min in-vehicle

0.11 min in-vehicle

Seating0.52 min in-vehicle

0.97 min in-vehicle

Not boarding

2.29 min in-vehicle

3.99 min in-vehicle

1 station

1.68 min in-vehicle

1.54 min in-vehicle

Turning back3.86 min in-vehicle

1.48 min in-vehicle

Turning away

5.15 min in-vehicle

2.37 min in-vehicle

Marginal rates of substitution

Results &Analysis

Transferring valuations in London
Gettinga seatIntermediate

Notboarding

Assisted

06.81 min07.33 min09.62 minSemi-assisted

08.56 min09.07 min11.36 minNon-assisted

09.69 min10.21 min12.49 min03.35 min03.87 min06.15 min

Assisted

06.08 min06.60 min

08.88 minSemi-assisted

07.82 min08.34 min10.63 minNon-assisted

08.95 min09.47 min11.76 minTransfer Type

AscendingAt level

Descending

Marginal rates of substitution

Results &Analysis

Transferring valuations in Santiago
Gettinga seatIntermediateNotboarding

Assisted

09.05 min10.02 min14.01 minNon-assisted

11.80 min12.77 min16.76 min

Assisted

05.67 min

06.63 min10.62 minNon-assisted

08.41 min09.38 min13.37 minTransfer Type

Ascending

Descending

range in London

3.35 to 12.49 min

range in Santiago

5.67 to 16.76 min

Results &AnalysisInformation omission bias

The omission of relevant information produces bias in theresults

Model Specification

LondonSantiago

wait / travel

walk/ travel

wait / travel

walk/ travel

Complete Model

1.65

1.15

1.46

1.62

without topological

0.61

0.46

2.36

2.09

without occupancy

1.67

1.17

2.48

1.81

without transfers

2.18

1.94

4.32

2.47

without all three

0.80

0.75

4.48

2.64

So… what can we do with this?
Change in the Santiago Metro schematic map

Extensions &Applications

So… what can we do with this?

Extensions &Applications

Demand analysis for thedesign of transferstations

So… what can we do with this?
Analyze and apply to any kind of transit network

Extensions &Applications

So… what can we do with this?

Extensions &Applications

Create a trip planning that not only considers travel time,
fare and/or transfers, but much more!

Conclusions

What did you learn today?
Public transport users take into account a wide variety ofattributes when choosing routes

An incomplete model specification can result in biased
results, such as attributes valuations

Network’s topology, and specially the way it’s presented tousers on a daily basis, is relevant

Conclusions

What did you learn today?
Due to bigger distortions in the schematic map, thetopological variables are more important in London

Londoners are more willing to transfer, as it is more common
to them (bigger and denser network)

Londoners are less willing to travel in crowded trains, butcare less about getting a seat

Analysing route choice decisions
on Metro networks
A comparison between Santiago and London
Sebastián Raveau
Pontificia Universidad Católica de ChileZhan Guo
New York University
Juan Carlos Muñoz
Pontificia Universidad Católica de ChileNigel Wilson
Massachusetts Institute of Technology