1. “Understanding fare evasion in urban bus systems:
Evidence from Santiago, Chile”
BRT Centre of Excellence Webinar
Pablo Guarda
Chilean Centre for Sustainable Urban Development (CEDEUS)
January 28, 2016
Pablo Guarda (CEDEUS) Webinar January 28, 2016 1 / 27

2. Outline
1 Introduction
2 Framework
3 Results
4 Conclusions
5 Further Research
Pablo Guarda (CEDEUS) Webinar January 28, 2016 2 / 27

3. Introduction Previous Work
Previous Work
Guarda, P., Galilea, P., Paget-Seekins, L. and Ort´uzar, J. de D., 2016. What is
behind fare evasion in urban bus systems? An econometric approach.
Transportation Research Part A: Policy and Practice 20, 55-71.
http://dx.doi.org/10.1016/j.tra.2015.10.008
Guarda, P., Galilea, P., Handy, S., Mu˜noz, J.C. and Ort´uzar, J. de D., 2015.
Decreasing fare evasion without fines? A microeconomic analysis. Research in
Transportation Economics. Submitted for Initial Review.
Pablo Guarda (CEDEUS) Webinar January 28, 2016 3 / 27

4. Introduction Santiago’s Public Transport System
Santiago’s Public Transport System (Transantiago)
• Transportation Structure
– 10 zones
– 7 bus operators
– Feeder and trunk buses
– Metro (5 lines)
• Fare System
– Integrated fare between Metro
and Buses
– Payment only by smart cards
Pablo Guarda (CEDEUS) Webinar January 28, 2016 4 / 27

5. Introduction Bus Fare Collection System
Bus Fare Collection System
Bus
Stop
Door 3 Door 2 Door 1
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6. Introduction Bus Fare Collection System
Bus Fare Collection System
Door 3 Door 2 Door 1
Bus
Stop
Pablo Guarda (CEDEUS) Webinar January 28, 2016 5 / 27

7. Introduction Bus Fare Collection System
Bus Fare Collection System
Door 3 Door 2 Door 1
Bus
Stop
Pablo Guarda (CEDEUS) Webinar January 28, 2016 5 / 27

8. Introduction Fare Evasion is Increasing Over Time
Fare Evasion is Increasing Over Time
Source: Graph obtained using R studio (DTPM, 2015)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 6 / 27

9. Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to more dedicated fine enforcement.
Pablo Guarda (CEDEUS) Webinar January 28, 2016 7 / 27

10. Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to more dedicated fine enforcement.
• We evaluate if ticket inspection is cost-e↵ective when evaders are not
given a fine.
Pablo Guarda (CEDEUS) Webinar January 28, 2016 7 / 27

11. Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to more dedicated fine enforcement.
• We evaluate if ticket inspection is cost-e↵ective when evaders are not
given a fine.
• To determine where, when and how much to invest in ticket
inspection, we formulate a methodology of 4 steps:
Pablo Guarda (CEDEUS) Webinar January 28, 2016 7 / 27

12. Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to more dedicated fine enforcement.
• We evaluate if ticket inspection is cost-e↵ective when evaders are not
given a fine.
• To determine where, when and how much to invest in ticket
inspection, we formulate a methodology of 4 steps:
1 Step 1: Data collection
Pablo Guarda (CEDEUS) Webinar January 28, 2016 7 / 27

13. Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to more dedicated fine enforcement.
• We evaluate if ticket inspection is cost-e↵ective when evaders are not
given a fine.
• To determine where, when and how much to invest in ticket
inspection, we formulate a methodology of 4 steps:
1 Step 1: Data collection
2 Step 2: Regression model estimation
Pablo Guarda (CEDEUS) Webinar January 28, 2016 7 / 27

14. Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to more dedicated fine enforcement.
• We evaluate if ticket inspection is cost-e↵ective when evaders are not
given a fine.
• To determine where, when and how much to invest in ticket
inspection, we formulate a methodology of 4 steps:
1 Step 1: Data collection
2 Step 2: Regression model estimation
3 Step 3: Allocation of ticket inspectors
Pablo Guarda (CEDEUS) Webinar January 28, 2016 7 / 27

15. Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to more dedicated fine enforcement.
• We evaluate if ticket inspection is cost-e↵ective when evaders are not
given a fine.
• To determine where, when and how much to invest in ticket
inspection, we formulate a methodology of 4 steps:
1 Step 1: Data collection
2 Step 2: Regression model estimation
3 Step 3: Allocation of ticket inspectors
4 Step 4: Cost-benefit analysis
Pablo Guarda (CEDEUS) Webinar January 28, 2016 7 / 27

16. Framework Step I: Data Collection
Step 1: Data Collection
Pablo Guarda (CEDEUS) Webinar January 28, 2016 8 / 27

17. Framework Step I: Data Collection
Step 1: Data Collection
Anonymous observers
Pablo Guarda (CEDEUS) Webinar January 28, 2016 8 / 27

18. Framework Step I: Data Collection
Step 1: Data Collection
Anonymous observers
Pablo Guarda (CEDEUS) Webinar January 28, 2016 8 / 27

19. Framework Step I: Data Collection
Data Available
1 Which door (1,2,3,4)
2 Month, Day, Hour, Minute
3 Number of fare evaders
4 Number of passenger boardings and exitings
5 Ticket inspection
6 Stop Location (GIS code)
7 Bus route and bus size
Pablo Guarda (CEDEUS) Webinar January 28, 2016 9 / 27

20. Framework Step I: Data Collection
Data Available
1 Which door (1,2,3,4)
2 Month, Day, Hour, Minute
3 Number of fare evaders
4 Number of passenger boardings and exitings
5 Ticket inspection
6 Stop Location (GIS code)
7 Bus route and bus size
Information not collected on individual
characteristics
Pablo Guarda (CEDEUS) Webinar January 28, 2016 9 / 27

21. Framework Step I: Data Collection
Data
• Data Description
– Size: 107,247 observations, 115,163 passenger boardings, 32,084 fare
evaders
– 62 bus routes operated by one private bus company
– Level of Aggregation: by bus stop and time of the day
– Estimation Sample: 13 days of November 2013
– Heuristic Application: 19 days of December 2013
Pablo Guarda (CEDEUS) Webinar January 28, 2016 10 / 27

22. Framework Step I: Data Collection
Data
• Data Description
– Size: 107,247 observations, 115,163 passenger boardings, 32,084 fare
evaders
– 62 bus routes operated by one private bus company
– Level of Aggregation: by bus stop and time of the day
– Estimation Sample: 13 days of November 2013
– Heuristic Application: 19 days of December 2013
• Data cleaning
– Observations with missing data and other basic inconsistencies (1,108)
– Observations collected in o↵-board payment stations (641)
– Observations when nobody boards the bus (31,340) were not included
in the model estimation
Pablo Guarda (CEDEUS) Webinar January 28, 2016 10 / 27

23. Framework Step I: Data Collection
Data
• Data Description
– Size: 107,247 observations, 115,163 passenger boardings, 32,084 fare
evaders
– 62 bus routes operated by one private bus company
– Level of Aggregation: by bus stop and time of the day
– Estimation Sample: 13 days of November 2013
– Heuristic Application: 19 days of December 2013
• Data cleaning
– Observations with missing data and other basic inconsistencies (1,108)
– Observations collected in o↵-board payment stations (641)
– Observations when nobody boards the bus (31,340) were not included
in the model estimation
• Variables created
– Bus occupancy
– Socioeconomic Level (SEL)
– Theoretical Headway
Pablo Guarda (CEDEUS) Webinar January 28, 2016 10 / 27

24. Framework Step I: Data Collection
Descriptive Statistics
Table 2: Summary statistics for the non-categorical variables (N = 105,497 observations)
Variable Min Max Mean Standard
Deviation
Coe cient
of Variation
Evasion (pax) 0.000 76.000 0.301 1.309 4.345
Boarding (pax) 0.000 102.000 1.015 2.913 2.867
Exiting (pax) 0.000 123.000 1.036 2.795 2.698
Monthly Household Income (US$) 750.766 5797.781 1278.447 766.218 0.599
Doors 2.000 4.000 2.934 0.780 0.266
Occupancy (pax/bus) 0.000 153.000 16.862 16.833 0.998
Bus Size (pax) 50.000 161.000 89.054 42.217 0.474
Level of Occupancy (%) 0.000 100.000 19.905 17.668 0.888
Frequency (bus/hr) 3.000 22.000 6.000 2.979 0.497
Headway (min) 2.727 20.000 9.328 3.320 0.356
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25. Framework Step I: Data Collection
Descriptive Statistics
Table 2: Summary statistics for the non-categorical variables (N = 105,497 observations)
Variable Min Max Mean Standard
Deviation
Coe cient
of Variation
Evasion (pax) 0.000 76.000 0.301 1.309 4.345
Boarding (pax) 0.000 102.000 1.015 2.913 2.867
Exiting (pax) 0.000 123.000 1.036 2.795 2.698
Monthly Household Income (US$) 750.766 5797.781 1278.447 766.218 0.599
Doors 2.000 4.000 2.934 0.780 0.266
Occupancy (pax/bus) 0.000 153.000 16.862 16.833 0.998
Bus Size (pax) 50.000 161.000 89.054 42.217 0.474
Level of Occupancy (%) 0.000 100.000 19.905 17.668 0.888
Frequency (bus/hr) 3.000 22.000 6.000 2.979 0.497
Headway (min) 2.727 20.000 9.328 3.320 0.356
Pablo Guarda (CEDEUS) Webinar January 28, 2016 11 / 27

26. Framework Step I: Data Collection
Descriptive Statistics
Table 1: Summary statistics for the categorical variables (N = 105,497 observations)
Variable Proportion of
sample (%)
Proportion of total
boardings (%)
Proportion of
total evasions (%)
Evasion
Rate (%)
Month
November 42.9 51.0 50.1 29.1
December 57.1 49.0 49.9 30.2
Bus doors
2 33.9 19.8 18.5 27.6
3 38.7 39.3 38.3 28.9
4 27.4 40.9 43.2 31.3
Ticket Inspector
Yes 0.8 2.2 1.2 16.4
No 99.2 97.8 98.8 29.9
Intermodal station
Yes 0.3 3.0 0.6 5.6
No 99.7 97.0 99.4 30.4
Metro station
Yes 5.8 17.4 18.7 31.8
No 94.2 82.6 81.3 29.1
Type of bus route
Trunk 44.8 28.8 27.6 28.4
Feeder 55.2 71.2 72.4 30.1
Socioeconomic level (SEL)
Low (< US$ 1065) 53.1 49.5 56.4 33.7
Lower middle (US$ 1065-1674) 31.9 28.8 25.3 26.0
Upper middle (US$ 1674-5175) 14.0 20.0 17.2 25.4
High (> US$ 5175) 1.0 1.7 1.1 20.1
Pablo Guarda (CEDEUS) Webinar January 28, 2016 11 / 27

27. Framework Step I: Data Collection
Descriptive Statistics
Table 1: Summary statistics for the categorical variables (N = 105,497 observations)
Variable Proportion of
sample (%)
Proportion of total
boardings (%)
Proportion of
total evasions (%)
Evasion
Rate (%)
Month
November 42.9 51.0 50.1 29.1
December 57.1 49.0 49.9 30.2
Bus doors
2 33.9 19.8 18.5 27.6
3 38.7 39.3 38.3 28.9
4 27.4 40.9 43.2 31.3
Ticket Inspector
Yes 0.8 2.2 1.2 16.4
No 99.2 97.8 98.8 29.9
Intermodal station
Yes 0.3 3.0 0.6 5.6
No 99.7 97.0 99.4 30.4
Metro station
Yes 5.8 17.4 18.7 31.8
No 94.2 82.6 81.3 29.1
Type of bus route
Trunk 44.8 28.8 27.6 28.4
Feeder 55.2 71.2 72.4 30.1
Socioeconomic level (SEL)
Low (< US$ 1065) 53.1 49.5 56.4 33.7
Lower middle (US$ 1065-1674) 31.9 28.8 25.3 26.0
Upper middle (US$ 1674-5175) 14.0 20.0 17.2 25.4
High (> US$ 5175) 1.0 1.7 1.1 20.1
Pablo Guarda (CEDEUS) Webinar January 28, 2016 11 / 27

28. Framework Step II: Regression Model Estimation
Step II: Regression Model Estimation
• Modelling
– Multiple linear regression
– Binomial regression model
– Count regression models
1. Poisson
2. Negative Binomial (NB2)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 12 / 27

29. Framework Step II: Regression Model Estimation
Step II: Regression Model Estimation
• Modelling
– Multiple linear regression
– Binomial regression model
– Count regression models
1. Poisson
2. Negative Binomial (NB2)
• Dependent Variable
– The amount of evasion at a bus stop and given time of the day
Pablo Guarda (CEDEUS) Webinar January 28, 2016 12 / 27

30. Framework Step II: Regression Model Estimation
Step II: Regression Model Estimation
• Modelling
– Multiple linear regression
– Binomial regression model
– Count regression models
1. Poisson
2. Negative Binomial (NB2)
• Dependent Variable
– The amount of evasion at a bus stop and given time of the day
• Explanatory Variables
– Bus operation (boarding, alighting, doors, bus occupancy)
– Temporal variables (time period)
– Geographical area (comuna, Metro station, intermodal station)
– Ticket inspection
– No significant parameters (headway, type of bus route)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 12 / 27

31. Framework Step II: Regression Model Estimation
Regression Model Results
Table 3: Regression model estimation results (N = 13,912 observations)
Variable (t-test) Binomial Poisson
(O↵set)
NB2
(O↵set)
NB2
Bus operation
Log(Boarding) – – – 1.090 (75.2)
Exiting 0.012 (5.7) 0.003 (2.3) 0.016 (6.0) 0.016 (5.9)
Doors 0.147 (8.1) 0.100 (6.7) 0.097 (4.9) 0.075 (3.7)
Bus Occupancy (%) 0.015 (25.9) 0.010 (21.5) 0.009 (15.1) 0.008 (12.8)
Temporal Variables
Morning Weekday 0.448 ( 15.5) 0.317 ( 13.0) 0.360 ( 11.2) 0.363 ( 11.2)
Afternoon Weekday 0.283 (11.1) 0.180 (8.7) 0.164 (5.8) 0.168 (5.9)
Geographical Area
Metro Station 0.142 ( 4.9) 0.089 ( 3.7) 0.124 ( 3.5) 0.170 ( 4.7)
Intermodal Station 2.733 ( 15.5) 2.402 ( 13.9) 2.421 ( 12.1) 2.601 ( 12.9)
Income II (US$ 1065-1674) 0.312 ( 12.3) 0.208 ( 9.9) 0.184 ( 6.7) 0.175 ( 6.3)
Income III (>US$ 1674) 0.560 ( 20.5) 0.376 ( 16.5) 0.348 ( 11.3) 0.354 ( 11.5)
Ticket Inspection
Inspectors 0.524 ( 6.8) 0.354 ( 5.3) 0.426 ( 4.3) 0.458 ( 4.6)
Inspectors x Income III 0.657 ( 2.8) 0.672 ( 3.0) 0.589 ( 2.3) 0.593 ( 2.3)
Inspector x Weekend 1.234 ( 5.1) 1.105 ( 4.8) 1.069 ( 4.1) 1.077 ( 4.2)
Constant 1.598 ( 24.8) 1.707 ( 32.1) 1.722 ( 25.1) 1.746 ( 25.3)
Observations 11,752 11,752 11,752 11,752
Log Likelihood 15,788.05 14,577.94 13,920.25 13,901.23
Akaike Inf. Crit. 31,602.09 29,181.89 27,866.50 27,830.45
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32. Framework Step II: Regression Model Estimation
Negative Binomial Regression Model (NB2)
• The NB2 model is a generalized linear model that can be estimated by
maximum-likelihood estimation or Bayesian methods (Hilbe, 2012):
P(Yi = yi |µi ) =
✓
yi + 1
↵ 1
1
↵ 1
◆✓
1
1 + ↵µi
◆ 1
↵
✓
↵µi
1 + ↵µi
◆yi
• Link function that relates the expected value of the PDF (µi ) to a set
of predictors (xik) and parameters ( k)
ln(µi ) =
KX
k=0
ˆkxik
yi : Observed outcome of counts for the observation i
µi : Mean of the observed outcome of counts for the observation i
↵: Heterogeneity parameter (overdispersion)
ˆk : Set of estimated parameters
xik : Set of values taken by the explanatory variables for the observation i
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33. Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix with the slots in which ticket inspectors
can be allocated
Pablo Guarda (CEDEUS) Webinar January 28, 2016 15 / 27

34. Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix with the slots in which ticket inspectors
can be allocated
2 Calculation of the minimum number of ticket of inspectors to satisfy
the demand of passengers at each slot (I)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 15 / 27

35. Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix with the slots in which ticket inspectors
can be allocated
2 Calculation of the minimum number of ticket of inspectors to satisfy
the demand of passengers at each slot (I)
3 Calculation of the number of evaders that each inspector reduce per
hour (productivity) at a given slot ( E)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 15 / 27

36. Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix with the slots in which ticket inspectors
can be allocated
2 Calculation of the minimum number of ticket of inspectors to satisfy
the demand of passengers at each slot (I)
3 Calculation of the number of evaders that each inspector reduce per
hour (productivity) at a given slot ( E)
4 Allocation of ticket inspectors based on the level of productivity of
each slot and the time horizon. The process is perform iteratively
until no more money is available (C)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 15 / 27

37. Framework Step III: Allocation of Ticket Inspectors
Matrix of slots for inspection
Information Slot
1 2 3 4 5
Bus route
Bus stop GIS
Time Period
Length of the slot (hr)
Rate of arrival (pax/hr)
Capacity of inspectors (µ)
Number of inspectors (I)
Evasion rate (%)
Percentage change factor (%)
Productivity per inspector (fares/hr)
Hours of inspection (hr)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 16 / 27

38. Framework Step III: Allocation of Ticket Inspectors
Minimum number of ticket inspectors
Information Slot
1 2 3 4 5
Bus route
Bus stop GIS
Time Period
Length of the slot (hr)
Rate of arrival (pax/hr)
Capacity of inspectors (µ)
Number of inspectors (I)
Evasion rate (%)
Percentage change factor (%)
Productivity per inspector (fares/hr)
Hours of inspection (hr)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 17 / 27

39. Framework Step III: Allocation of Ticket Inspectors
Productivity of each inspector
Information Slot
1 2 3 4 5
Bus route
Bus stop GIS
Time Period
Length of the slot (hr)
Rate of arrival (pax/hr)
Capacity of inspectors (µ)
Number of inspectors (I)
Evasion rate (%)
Percentage change factor (%)
Productivity per inspector (fares/hr)
Hours of inspection (hr)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 18 / 27

40. Framework Step III: Allocation of Ticket Inspectors
Allocation of ticket inspectors
Information Slot
1 2 3 4 5
Bus route
Bus stop GIS
Time Period
Length of the slot (hr)
Rate of arrival (pax/hr)
Capacity of inspectors (µ)
Number of inspectors (I)
Evasion rate (%)
Percentage change factor (%)
Productivity per inspector (fares/hr)
Hours of inspection (hr)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 19 / 27

41. Framework Step III: Allocation of Ticket Inspectors
Marginal Productivity of Inspectors by SEL
µ = 60 [pax/hr]
1020304050
5000 10000
Total Time of Inspection [hr]
RateofReductionofFareEvasion[Fares/hr]
Income I Income II Income III
Pablo Guarda (CEDEUS) Webinar January 28, 2016 20 / 27

42. Framework Step IV: Cost-Benefit evaluation
Step IV: Cost-benefit evaluation
µ = 60 [pax/hr], cw = 3.8 [US$/hr]
cw = 3.8
Π*
= 91505 US$
A B
CD
010203040
0 5000 10000 15000 20000 25000
Total Time of Inspection [hr]
DollarsperHour[US$/hr]
Average Revenue Marginal Revenue Marginal Cost
Pablo Guarda (CEDEUS) Webinar January 28, 2016 21 / 27

43. Results
Main Results
Regression model assumptions and goodness of fit
• All parameters obtained in the regression models were statistically
significant and their signs were consistent with our expectations.
• After trying di↵erent specifications and validating the statistical
model assumptions we selected the NB2 model.
Fare evasion rates increase:
• As increase the number of boardings, alightings, doors and
occupancy
• As decrease the socioeconomic level (SEL) of the municipality
• If the bus stop is not located near to a Metro station or
intermodal station
• During the weekend and the afternoon (weekdays)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 22 / 27

44. Results
Main Results
The impact of ticket inspectors depends on the SEL of the
municipality and the period of the day
• The highest productivity is in low income municipalities and
during the weekdays
• The highest e↵ectiveness is in high income municipalities and
during the weekend
Policy implication
• Fare evasion can be tackled without necessarily more dedicated
fine enforcement
• Ticket inspection was cost-e↵ective for a large range of
investment levels and a short time horizon (1 month)
Pablo Guarda (CEDEUS) Webinar January 28, 2016 23 / 27

45. Conclusions
Conclusions
1 Fare evasion levels are the product of a combination of factors
including the level of the service (e.g. bus occupancy), the
characteristics of the geographical area where bus stops are located
(e.g. monthly household income) and the level of enforcement.
Pablo Guarda (CEDEUS) Webinar January 28, 2016 24 / 27

46. Conclusions
Conclusions
1 Fare evasion levels are the product of a combination of factors
including the level of the service (e.g. bus occupancy), the
characteristics of the geographical area where bus stops are located
(e.g. monthly household income) and the level of enforcement.
2 The use of regression models and the microeconomic analysis
provided a powerful and simple tool to increase the cost-e↵ectiveness
of ticket inspectors
Pablo Guarda (CEDEUS) Webinar January 28, 2016 24 / 27

47. Conclusions
Conclusions
1 Fare evasion levels are the product of a combination of factors
including the level of the service (e.g. bus occupancy), the
characteristics of the geographical area where bus stops are located
(e.g. monthly household income) and the level of enforcement.
2 The use of regression models and the microeconomic analysis
provided a powerful and simple tool to increase the cost-e↵ectiveness
of ticket inspectors
3 This study contributes with new evidence that indicates that
inspection strategies can be cost-e↵ective even when evaders are not
given a fine
Pablo Guarda (CEDEUS) Webinar January 28, 2016 24 / 27

48. Further Research
Further Research
1 Formulation of a cost-benefit analysis model to help authorities
determine the budget distribution among a set of strategies for
dealing with fare evasion.
Increase bus frequency
Increase bus size
Implement o↵-board payment stations
Increase fare inspection.
Pablo Guarda (CEDEUS) Webinar January 28, 2016 25 / 27

49. Further Research
Further Research
1 Formulation of a cost-benefit analysis model to help authorities
determine the budget distribution among a set of strategies for
dealing with fare evasion.
Increase bus frequency
Increase bus size
Implement o↵-board payment stations
Increase fare inspection.
2 Formulation of a model to predict fare evasion using smartcard data.
Prediction of fare evasion rates in each inspection slot (Step III)
Calculation of changes on fare evasion due to improvements in the level
of service (reduction of bus occupancy)
Estimation of bus load profiles (correcting for fare evasion).
Pablo Guarda (CEDEUS) Webinar January 28, 2016 25 / 27

50. Acknowledgements
Acknowledgements
This research was benefited from the support of:
• Center for Sustainable Urban Development (CEDEUS), Chile
• Bus Rapid Transit Centre of Excellence, funded by the Volvo Research
and Educational Foundations (VREF)
• The Enforcement Commission of the Chilean Transport Ministry
• SUBUS Chile
Pablo Guarda (CEDEUS) Webinar January 28, 2016 26 / 27

51. “Understanding fare evasion in urban bus systems:
Evidence from Santiago, Chile”
BRT Centre of Excellence Webinar
Pablo Guarda
Chilean Centre for Sustainable Urban Development (CEDEUS)
January 28, 2016
Pablo Guarda (CEDEUS) Webinar January 28, 2016 27 / 27