webinar: modelling mode and route choices on public transport systems
DESCRIPTION
2013-12-05 Webinar by Sebastian RaveauTRANSCRIPT
Modelling Mode and Route Choices on
Public Transport Systems
Sebastián Raveau Pontificia Universidad Católica de Chile
BRT Centre of Excellence Webinar
December 5, 2013
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
Origin Destination
The trip begins by heading
in the opposite direction…
It’ is better to use the Yellow Line,
but 9 out of 10 use the Red Line!
Attribute Red Line Yellow Line
Transfers 1 1
Time 23:40 23:43
Density 5 pax/m2 3 pax/m2
First leg 90 % 50 %
How do we change these
travelers’ decision?
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.
Study’s objectives
Contents
Route Choice
Background
Study Case 1
Metro Networks
Study Case 2
Multimodal Network
Results &
Analysis
Extensions &
Applications
Conclusions
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.
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.
Route choice modelling
Route Choice
Background
Pathfinding Criteria
Route Choice
Background
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!
Route Choice
Background Pathfinding Criteria
Route Choice
Background Pathfinding Criteria
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
Study Case 1
Metro Networks Analyzing travellers decisions on Metro Networks
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
travel time
components
Study Case 1
Metro Networks
What do people take into account?
In-vehicle time
Waiting time
Walking time (when transferring)
Number of transfers
Transfer stations layout
Transfer stations infrastructure
assisted or
semi-assisted or and
non-assisted
travel time
components
Study Case 1
Metro Networks
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
initial occupancy ≥ 75% in London
initial occupancy ≥ 85% in Santiago
travel time
components
transfer
experience
Study Case 1
Metro Networks
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
initial occupancy ≤ 25% in London
initial occupancy ≤ 15% in Santiago
travel time
components
transfer
experience
Study Case 1
Metro Networks
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
comfort and
crowding
travel time
components
transfer
experience
2d sin
Study Case 1
Metro Networks
What do people take into account?
Origin
Destination 1d
2d
3d
1 21 2
2 2d sin d sin
T1
T2
Angular Cost =
Study Case 1
Metro Networks
2
1
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 route
comfort and
crowding
travel time
components
Transfer
experience
topological
variables
Easy to obtain!
Easy to obtain!
Easy to obtain!
Study Case 1
Metro Networks
Defined based on the schematic maps
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
Schematic map’s effect
Study Case 1
Metro Networks
Set of alternative routes
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
Study Case 1
Metro Networks
C-Logit Model
for Route Choice
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
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
Marginal rates of substitution
Study Case 1
Metro Networks
Transfer valuations in London
Transfer Type Getting
a seat Intermediate
Not
boarding
Ascending
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
At level 03.35 min 03.87 min 06.15 min
Descending
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
Marginal rates of substitution
Study Case 1
Metro Networks
Transfer valuations in Santiago
range in London 3.35 to 12.49 min
range in Santiago 5.67 to 16.76 min
Marginal rates of substitution
Transfer Type Getting
a seat Intermediate
Not
boarding
Ascending Assisted 09.05 min 10.02 min 14.01 min
Non-assisted 11.80 min 12.77 min 16.76 min
Descending Assisted 05.67 min 06.63 min 10.62 min
Non-assisted 08.41 min 09.38 min 13.37 min
Study Case 1
Metro Networks
Transantiago - Santiago, Chile
Study Case 2
Multimodal Network
34 communes
7 million people
700 sq Km
10 million daily trips
55% in public modes
Transantiago - Santiago, Chile
Study Case 2
Multimodal Network
10 zones
feeder bus lines
trunk bus lines
express bus lines
Metro
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
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.
Analyzing travellers decisions on Transantiago
Study Case 2
Multimodal Network
Choosing a itinerary
Choosing an hyper-path → considering common lines
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
Route choice strategies
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
r 2 0.525
Study Case 2
Multimodal Network
Mode/route choice results
Study Case 2
Multimodal Network
Consider
Common Lines
Do Not Consider
Common Lines
Variable Parameter t-value Parameter t-value
Fare (CLP) - 0.041 - 2.32 - 0.050 - 2.45
In-vehicle time (min) - 0.625 - 2.17 - 0.477 - 2.39
Waiting time (min) - 1.601 - 4.37 - 1.217 - 3.78
Walking time (min) - 1.856 - 2.11 - 1.353 - 2.43
Bus-bus transfer - 2.822 - 2.98 - 2.139 - 2.23
Bus-Metro transfer - 2.201 - 2.32 - 1.849 - 2.63
Metro-Metro transfer - 1.939 - 2.33 - 1.673 - 2.09
Travelling seated 1.886 2.88 1.652 2.33
Not boarding - 1.890 - 1.97 - 1.533 - 2.04
Log-Likelihood - 1,512
r 2 0.487
Marginal rates of substitution
Study Case 2
Multimodal Network
Variable Consider
Common Lines
Do Not Consider
Common Lines
In-vehicle time (min) € 1.35 per hour € 0.88 per hour
Waiting time (min) € 3.51 per hour € 2.25 per hour
Walking time (min) € 4.06 per hour € 2.50 per hour
Bus-bus transfer € 0.11 per transfer € 0.07 per transfer
Bus-Metro transfer € 0.08 per transfer € 0.06 per transfer
Metro-Metro transfer € 0.07 per transfer € 0.05 per transfer
Travelling seated € 0.07 per leg € 0.05 per transfer
Not boarding € 0.07 per vehicle € 0.05 per transfer
Those who consider common lines are more sensitive to the
different attributes.
Extensions &
Applications Using the model for policy
Change in the Santiago Metro Map
Apply the model to different cities and systems
Extensions &
Applications Some extensions to this work
Extensions &
Applications Some extensions to this work
Map design optimization
Application to journey planner
Extensions &
Applications Some extensions to this work
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.
Conclusions
What did we learn today?
Don’t forget that we are dealing
with individuals, whose behaviour is
hard to understand and model
Conclusions
What did we learn today?
Modelling Mode and Route Choices on
Public Transport Systems
Sebastián Raveau Pontificia Universidad Católica de Chile
BRT Centre of Excellence Webinar
December 5, 2013