2013-12-05 Webinar by Sebastian Raveau

1. Modelling Mode and Route Choices on
Public Transport Systems
Sebastián Raveau
Pontificia Universidad Católica de Chile
BRT Centre of Excellence Webinar
December 5, 2013

2. Modelling Mode and Route Choices on
Public Transport Systems
Sebastián Raveau
Pontificia Universidad Católica de Chile
with the collaboration of:
Juan Carlos Muñoz
Pontificia Universidad Católica de Chile
Juan de Dios Ortúzar
Pontificia Universidad Católica de Chile
Louis de Grange
Universidad Diego Portales
Zhan Guo
New York University
Nigel H.M. Wilson
Massachusetts Institute of Technology
Carlo Giacomo Prato
Technical University of Denmark

3. It’ is better to use the Yellow Line,
but 9 out of 10 use the Red Line!
The trip begins by heading
in the opposite direction…
Destination
Origin
Attribute
Red Line
Yellow Line
Transfers
1
1
Time
23:40
23:43
Density
5 pax/m2
3 pax/m2
First leg
90 %
50 %

4. How do we change these
travelers’ decision?

5. Study’s objectives
Understanding travellers is essential in Transportation Planning
and Design.
Identify and quantify the factors that affect the public transport
users’ behaviour.
Explore differences across modes, in multi-modal public
transport networks.
Compare the preferences of public transport users in different
systems and contexts.

6. Contents
Study Case 1
Metro Networks
Study Case 2
Multimodal Network
Results &
Analysis
Extensions &
Applications
Route Choice
Background
Conclusions

7. Route choice modelling
Route Choice
Background
Traditional route choice models usually consider just tangible
variables related to the level of service.
travel time
fare
number of transfers
These models are sometimes refined including socio-economic
variables of the travellers.

8. 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.

9. Pathfinding Criteria
Route Choice
Background

10. Pathfinding Criteria
Route Choice
Background

11. Pathfinding Criteria
Route Choice
Background
Some people follow different criteria when deciding how to get
from one point to another:
the fastest way
the cheapest way
avoid walking
avoid transferring
But most consider many factors at the same time, depending on
their preferences and information!

12. Pathfinding Criteria
Route Choice
Background

13. Analyzing travellers decisions on Metro Networks
Study Case 1
Metro Networks
Santiago
London
Survey date
2008
1998-2005
Length
78 Km
324 Km
Lines
5
11
Stations
85
255
Transfer stations
7
72
Daily trips
2,300,000
3,400,000
Survey size
28,961
16,300

14. What do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)
Number of transfers
Transfer stations layout
ascending
at level
descending
Study Case 1
Metro Networks
travel time
components

15. Study Case 1
Metro Networks
What do people take into account?
travel time
In-vehicle time
Waiting time
Walking time (when transferring)
Number of transfers
Transfer stations layout
Transfer stations infrastructure
components
assisted
or
semi-assisted
or
non-assisted
and

16. 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
Study Case 1
Metro Networks
travel time
components
transfer
experience
initial occupancy ≥ 75% in London
initial occupancy ≥ 85% in Santiago

17. 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
Study Case 1
Metro Networks
travel time
components
transfer
experience
initial occupancy ≤ 25% in London
initial occupancy ≤ 15% in Santiago

18. 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
Study Case 1
Metro Networks
travel time
components
transfer
experience
comfort and
crowding

19. Study Case 1
Metro Networks
What do people take into account?
T2
d2
T1
d1
1
2
d3
Destination
Origin
 1   d  sin  2 
Angular Cost = d1  sin  
 
2
2
2

20. What do people take into account?
Study Case 1
Metro Networks
travel time
In-vehicle time
components
Waiting time
Walking time (when transferring)
Transfer
Number of transfers
experience
Transfer stations layout
Easy to obtain!
Transfer stations infrastructure
comfort and
Mean occupancy
crowding
Possibility of not boarding
Possibility of getting a seat
Easy to obtain!
topological
Route distance
variables
Number of stations
Defined based on the schematic maps
Angular cost
Easy to obtain!
Reasonable route

21. Schematic map’s effect
Study Case 1
Metro Networks
We want to understand the impact of the Metro network
schematic map on the users’ behaviour

22. Schematic map’s effect
Study Case 1
Metro Networks

23. Set of alternative routes
Study Case 1
Metro Networks
A key element when dealing with probabilistic route choice
models is the definition of the alternatives for the OD pairs of
interest
Santiago
generated based on the actual choices
→
2 to 4 alternative routes
London
generated based on a labeling approach
→
2 to 6 alternative routes
C-Logit Model
for Route Choice

24. Study Case 1
Metro Networks
Estimation results
Attribute
London 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
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
- 6.83
n. 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
0.092
3.41
Not Boarding
- 0.430
- 6.06
- 0.380
- 2.97
Angular Cost
- 0.065
- 5.87
- 0.024
- 5.48
Map Distance
- 0.358
- 5.76
- 0.274
- 5.69
Number of Stations
- 0.316
- 5.52
- 0.147
- 3.10
Turning Back
- 0.725
- 8.12
- 0.141
- 9.76
Turning Away
- 0.968
- 8.00
- 0.226
- 7.11
Commonality Factor
- 0.146
- 3.92
- 0.548
- 3.33
Adjusted r 2

0.566
0.382

25. Marginal rates of substitution
Study Case 1
Metro Networks
Attribute
London
Santiago
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
Seating
0.52 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 back
3.86 min in-vehicle
1.48 min in-vehicle
Turning away
5.15 min in-vehicle
2.37 min in-vehicle

26. Study Case 1
Metro Networks
Marginal rates of substitution
Transfer valuations in London
Getting
a seat
Intermediate
Not
boarding
Assisted
06.81 min
07.33 min
09.62 min
Semi-assisted
08.56 min
09.07 min
11.36 min
Non-assisted
09.69 min
10.21 min
12.49 min
03.35 min
03.87 min
06.15 min
Assisted
06.08 min
06.60 min
08.88 min
Semi-assisted
07.82 min
08.34 min
10.63 min
Non-assisted
08.95 min
09.47 min
11.76 min
Transfer Type
Ascending
At level
Descending

27. Study Case 1
Metro Networks
Marginal rates of substitution
Transfer valuations in Santiago
Getting
a seat
Intermediate
Not
boarding
Assisted
09.05 min
10.02 min
14.01 min
Non-assisted
11.80 min
12.77 min
16.76 min
Assisted
05.67 min
06.63 min
10.62 min
Non-assisted
08.41 min
09.38 min
13.37 min
Transfer Type
Ascending
Descending
range in London
3.35 to 12.49 min
range in Santiago
5.67 to 16.76 min

28. Transantiago - Santiago, Chile
Study Case 2
Multimodal Network
34 communes
7 million people
700 sq Km
10 million daily trips
55% in public modes

29. Study Case 2
Multimodal Network
Transantiago - Santiago, Chile
10 zones
feeder bus lines
trunk bus lines
express bus lines
Metro

30. Transantiago - Santiago, Chile
Study Case 2
Multimodal Network
30,000 daily trips
(7am to 12 pm)
1% of all the city trips
1,892 respondents
access to all modes

31. Analyzing travellers decisions on Transantiago
Study Case 2
Multimodal Network
The objective is to expand the behavioural models obtained
form Metro, to the entire public transport system.
Some new explanatory variables are:
fare
distinguish travel time by mode
distinguish transfers by modes involved
variability of in-vehicle and waiting times
When travelling in frequency-based networks, the travellers
might follow different route choice strategies.

32. Study Case 2
Multimodal Network
Route choice strategies
Choosing a itinerary
Choosing an hyper-path
→
considering common lines

33. Route choice strategies
Study Case 2
Multimodal Network
We found that 66.6% of the travellers that could choose their
routes considering common lines, didn’t do so...
One might argue that considering common lines is a personal
characteristic, rather than the behaviour of everyone.
We propose modelling two types of individuals:
Those who consider common lines
Those who don’t consider common lines

34. Study Case 2
Multimodal Network
Logit probability of considering common lines
Attribute
Parameter
t-Value
Income – More than 1,000€/month
- 0.940
3.22
Income – 500€/month to 1,000€/month
- 0.327
3.45
Income – Less than 500€/month
- 0.000
base
Frequency - Al least once a week
- 1.322
4.98
Frequency - Al least once a month
- 0.766
3.71
Frequency – Rarely/Never
- 0.000
base
Age – Less than 30 years old
- 0.399
2.90
Age – More than 30 years old
- 0.000
base
Constant
- 2.051
- 5.76
Log-Likelihood
- 800.66
r2
0.525

35. Study Case 2
Multimodal Network
Mode/route choice results
Consider
Common Lines
Variable
Fare (CLP)
In-vehicle time (min)
Waiting time (min)
Walking time (min)
Bus-bus transfer
Bus-Metro transfer
Metro-Metro transfer
Travelling seated
Not boarding
Log-Likelihood
r2
Parameter
- 0.041
- 0.625
- 1.601
- 1.856
- 2.822
- 2.201
- 1.939
1.886
- 1.890
Do Not Consider
Common Lines
t-value
Parameter
- 2.32
- 0.050
- 2.17
- 0.477
- 4.37
- 1.217
- 2.11
- 1.353
- 2.98
- 2.139
- 2.32
- 1.849
- 2.33
- 1.673
2.88
1.652
- 1.97
- 1.533
- 1,512
0.487
t-value
- 2.45
- 2.39
- 3.78
- 2.43
- 2.23
- 2.63
- 2.09
2.33
- 2.04

36. Marginal rates of substitution
Study Case 2
Multimodal Network
Variable
Consider
Common Lines
Do Not Consider
Common Lines
In-vehicle time (min)
Waiting time (min)
Walking time (min)
Bus-bus transfer
Bus-Metro transfer
Metro-Metro transfer
Travelling seated
Not boarding
€ 1.35 per hour
€ 3.51 per hour
€ 4.06 per hour
€ 0.11 per transfer
€ 0.08 per transfer
€ 0.07 per transfer
€ 0.07 per leg
€ 0.07 per vehicle
€ 0.88 per hour
€ 2.25 per hour
€ 2.50 per hour
€ 0.07 per transfer
€ 0.06 per transfer
€ 0.05 per transfer
€ 0.05 per transfer
€ 0.05 per transfer
Those who consider common lines are more sensitive to the
different attributes.

37. Using the model for policy
Change in the Santiago Metro Map
Extensions &
Applications

38. Some extensions to this work
Apply the model to different cities and systems
Extensions &
Applications

39. Some extensions to this work
Map design optimization
Extensions &
Applications

40. Some extensions to this work
Application to journey planner
Extensions &
Applications

41. What did we learn today?
Conclusions
Public transport users take into account a wide variety of
attributes when choosing routes.
The modelling effort should be on what we can explain, rather
than in what we can’t explain.
Network’s topology, and specially the way it’s presented to users
on a daily basis, is relevant.
Different individuals follow different strategies when choosing
routes.

42. What did we learn today?
Conclusions
Don’t forget that we are dealing
with individuals, whose behaviour is
hard to understand and model

43. Modelling Mode and Route Choices on
Public Transport Systems
Sebastián Raveau
Pontificia Universidad Católica de Chile
BRT Centre of Excellence Webinar
December 5, 2013