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The customer experience: reliability, cost,
travel times, safety, comfort …
Juan Carlos Muñoz, Juan de Dios Ortúzar, Nigel Wilson
and Sebastián Raveau
Bus Rapid Transit Centre of Excellence
Pontificia Universidad Católica de Chile - MIT
September 20, 2013
Origin
Destination
The traveler starts
heading in the opposite
direction…
The map influences
the Baquedano trip
Only 11% travel through Santa Ana!!
Attribute Baquedano Santa Ana
Travel Time 23:40 23:43
Density 5 pax/m2 3 pax/m2
Occupancy 88% 49%
Transfers 1 1
13 min
10 min
Contents
Route Choice
Background
Modelling
Variables
Study
Cases
Results &
Analysis
Extensions &
Applications
Conclusions
Route choice modelling
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 travelers
Route Choice
Background
Route choice modelling
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
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
In a transit context, there are some additional factors
avoid walking
avoid transferring
But most consider many factors at the same time!
Pathfinding Criteria
Route Choice
Background
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
and Santiago
Modelling
Variables
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
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
assisted or
semi-assisted or and
non-assisted
travel time
components
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
Mean occupancy
Possibility of not boarding
in London initial occupancy ≥ 70%
in Santiago initial occupancy ≥ 85%
travel time
components
transfer
experience
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
Mean occupancy
Possibility of not boarding
Possibility of getting a seat
in London initial occupancy ≤ 20%
in Santiago initial occupancy ≤ 15%
travel time
components
transfer
experience
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
Mean occupancy
Possibility of not boarding
Possibility of getting a seat
Route distance
Number of stations
Angular cost
Modelling
Variables
comfort and
crowding
travel time
components
transfer
experience
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
comfort and
crowding
travel time
components
transfer
experience
   
 
 2
d sin
What do people take into account?
Modelling
Variables
Origin
Destination
1d
2d
3dT1
T2
Angular Cost =
What do people take into account?
Modelling
Variables
Origin
Destination
1d
2d
3d
1
         
   
1 2
1 2
2 2
d sin d sin
T1
T2
Angular Cost =
What do people take into account?
Modelling
Variables
Origin
Destination
1d
2d
3d
         
   
1 2
1 2
2 2
d sin d sin
T1
T2
Angular Cost =
What do people take into account?
Modelling
Variables
Origin
Destination
1d
2d
3d
         
   
1 2
1 2
2 2
d sin d sin
2
T1
T2
Angular Cost =
What do people take into account?
Modelling
Variables
Origin
Destination
1d
2d
3d
         
   
1 2
1 2
2 2
d sin d sin
T1
T2
Angular Cost =
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
travel time
components
transfer
experience
Modelling
Variables
comfort and
crowding
What do people take into account?
Modelling
Variables
turning away from the destination
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
travel time
components
transfer
experience
Modelling
Variables
comfort and
crowding
topological
variables
Comparing Santiago and London
Study
Cases
Santiago London
Survey date 2008 1998-2005
Lines 5 11
Stations 85 255
Transfer stations 7 72
Daily trips 2,300,000 3,400,000
Survey size 28,961 16,300
Comparing Santiago and London
Study
Cases
Santiago Metro
Comparing Santiago and London
Study
Cases
London Underground
Modelling approach
Results &
Analysis
We use a Multinomial Logit for modeling the route
choice
Probabilistic model for discrete choices
Every route has an utility level based on its
characteristics
People choose the route with maximum utility
level
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
Adjusted r 2 0.566 0.382
Results &
Analysis
Parameter’s signs  OK
Parameter’s significances  OK
Marginal rates of substitution
Results &
Analysis
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
Map impact
Results &
Analysis
The topological variables can be specified using the
real (geographic) information or the schematic maps
Statistic
London Santiago
Real Map Real Map
t-test Angular Cost - 1.60 - 5.87 - 4.28 - 5.48
wait / travel 2.44 1.65 1.98 1.46
walk / travel 1.90 1.15 1.94 1.62
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
the design of transfer
stations
What did you learn today?
Public transport users take into account a wide variety
of attributes 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 to users on a daily basis, is relevant
Conclusions
What did you learn today?
Due to bigger distortions in the schematic map, the
topological 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,
but care less about getting a seat
Conclusions
The customer experience: reliability, cost,
travel times, safety, comfort …
Juan Carlos Muñoz, Juan de Dios Ortúzar, Nigel Wilson
and Sebastián Raveau
Bus Rapid Transit Centre of Excellence
Pontificia Universidad Católica de Chile - MIT
September 20, 2013

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Theme 3b Users perspective of integrated transit systems

  • 1. The customer experience: reliability, cost, travel times, safety, comfort … Juan Carlos Muñoz, Juan de Dios Ortúzar, Nigel Wilson and Sebastián Raveau Bus Rapid Transit Centre of Excellence Pontificia Universidad Católica de Chile - MIT September 20, 2013
  • 2. Origin Destination The traveler starts heading in the opposite direction… The map influences the Baquedano trip Only 11% travel through Santa Ana!! Attribute Baquedano Santa Ana Travel Time 23:40 23:43 Density 5 pax/m2 3 pax/m2 Occupancy 88% 49% Transfers 1 1
  • 5. Route choice modelling 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 travelers Route Choice Background
  • 6. Route choice modelling 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 Background
  • 9. Some people follow different criteria when deciding how to get from one point to another the 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! Pathfinding Criteria Route Choice Background
  • 10. 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 and Santiago Modelling Variables
  • 11. 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 Modelling Variables
  • 12. 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 Modelling Variables
  • 13. 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 London initial occupancy ≥ 70% in Santiago initial occupancy ≥ 85% travel time components transfer experience Modelling Variables
  • 14. 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 in London initial occupancy ≤ 20% in Santiago initial occupancy ≤ 15% travel time components transfer experience Modelling Variables
  • 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 comfort and crowding travel time components transfer experience
  • 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 Possibility of getting a seat Route distance Number of stations Angular cost Modelling Variables comfort and crowding travel time components transfer experience        2 d sin
  • 17. What do people take into account? Modelling Variables Origin Destination 1d 2d 3dT1 T2 Angular Cost =
  • 18. What do people take into account? Modelling Variables Origin Destination 1d 2d 3d 1               1 2 1 2 2 2 d sin d sin T1 T2 Angular Cost =
  • 19. What do people take into account? Modelling Variables Origin Destination 1d 2d 3d               1 2 1 2 2 2 d sin d sin T1 T2 Angular Cost =
  • 20. What do people take into account? Modelling Variables Origin Destination 1d 2d 3d               1 2 1 2 2 2 d sin d sin 2 T1 T2 Angular Cost =
  • 21. What do people take into account? Modelling Variables Origin Destination 1d 2d 3d               1 2 1 2 2 2 d sin d sin T1 T2 Angular Cost =
  • 22. 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 travel time components transfer experience Modelling Variables comfort and crowding
  • 23. What do people take into account? Modelling Variables turning away from the destination
  • 24. 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 travel time components transfer experience Modelling Variables comfort and crowding topological variables
  • 25. Comparing Santiago and London Study Cases Santiago London Survey date 2008 1998-2005 Lines 5 11 Stations 85 255 Transfer stations 7 72 Daily trips 2,300,000 3,400,000 Survey size 28,961 16,300
  • 26. Comparing Santiago and London Study Cases Santiago Metro
  • 27. Comparing Santiago and London Study Cases London Underground
  • 28. Modelling approach Results & Analysis We use a Multinomial Logit for modeling the route choice Probabilistic model for discrete choices Every route has an utility level based on its characteristics People choose the route with maximum utility level
  • 29. 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 Adjusted r 2 0.566 0.382 Results & Analysis Parameter’s signs  OK Parameter’s significances  OK
  • 30. Marginal rates of substitution Results & Analysis 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
  • 31. Map impact Results & Analysis The topological variables can be specified using the real (geographic) information or the schematic maps Statistic London Santiago Real Map Real Map t-test Angular Cost - 1.60 - 5.87 - 4.28 - 5.48 wait / travel 2.44 1.65 1.98 1.46 walk / travel 1.90 1.15 1.94 1.62
  • 32. So… what can we do with this? Change in the Santiago Metro schematic map Extensions & Applications
  • 33. So… what can we do with this? Extensions & Applications Demand analysis for the design of transfer stations
  • 34. What did you learn today? Public transport users take into account a wide variety of attributes 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 to users on a daily basis, is relevant Conclusions
  • 35. What did you learn today? Due to bigger distortions in the schematic map, the topological 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, but care less about getting a seat Conclusions
  • 36. The customer experience: reliability, cost, travel times, safety, comfort … Juan Carlos Muñoz, Juan de Dios Ortúzar, Nigel Wilson and Sebastián Raveau Bus Rapid Transit Centre of Excellence Pontificia Universidad Católica de Chile - MIT September 20, 2013