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“Understanding fare evasion in urban bus systems:
Evidence from Santiago, Chile”
BRT Centre of Excellence Webinar
Pablo Gu...
Outline
1 Introduction
2 Framework
3 Results
4 Conclusions
5 Further Research
Pablo Guarda (CEDEUS) Webinar January 28, 20...
Introduction Previous Work
Previous Work
Guarda, P., Galilea, P., Paget-Seekins, L. and Ort´uzar, J. de D., 2016. What is
...
Introduction Santiago’s Public Transport System
Santiago’s Public Transport System (Transantiago)
• Transportation Structu...
Introduction Bus Fare Collection System
Bus Fare Collection System
Bus
Stop
Door 3 Door 2 Door 1
Pablo Guarda (CEDEUS) Web...
Introduction Bus Fare Collection System
Bus Fare Collection System
Door 3 Door 2 Door 1
Bus
Stop
Pablo Guarda (CEDEUS) Web...
Introduction Bus Fare Collection System
Bus Fare Collection System
Door 3 Door 2 Door 1
Bus
Stop
Pablo Guarda (CEDEUS) Web...
Introduction Fare Evasion is Increasing Over Time
Fare Evasion is Increasing Over Time
Source: Graph obtained using R stud...
Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to...
Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to...
Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to...
Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to...
Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to...
Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to...
Introduction Study Overview
Study Overview
• Our main goal is to propose new methods to address evasion as
alternatives to...
Framework Step I: Data Collection
Step 1: Data Collection
Pablo Guarda (CEDEUS) Webinar January 28, 2016 8 / 27
Framework Step I: Data Collection
Step 1: Data Collection
Anonymous observers
Pablo Guarda (CEDEUS) Webinar January 28, 20...
Framework Step I: Data Collection
Step 1: Data Collection
Anonymous observers
Pablo Guarda (CEDEUS) Webinar January 28, 20...
Framework Step I: Data Collection
Data Available
1 Which door (1,2,3,4)
2 Month, Day, Hour, Minute
3 Number of fare evader...
Framework Step I: Data Collection
Data Available
1 Which door (1,2,3,4)
2 Month, Day, Hour, Minute
3 Number of fare evader...
Framework Step I: Data Collection
Data
• Data Description
– Size: 107,247 observations, 115,163 passenger boardings, 32,08...
Framework Step I: Data Collection
Data
• Data Description
– Size: 107,247 observations, 115,163 passenger boardings, 32,08...
Framework Step I: Data Collection
Data
• Data Description
– Size: 107,247 observations, 115,163 passenger boardings, 32,08...
Framework Step I: Data Collection
Descriptive Statistics
Table 2: Summary statistics for the non-categorical variables (N ...
Framework Step I: Data Collection
Descriptive Statistics
Table 2: Summary statistics for the non-categorical variables (N ...
Framework Step I: Data Collection
Descriptive Statistics
Table 1: Summary statistics for the categorical variables (N = 10...
Framework Step I: Data Collection
Descriptive Statistics
Table 1: Summary statistics for the categorical variables (N = 10...
Framework Step II: Regression Model Estimation
Step II: Regression Model Estimation
• Modelling
– Multiple linear regressi...
Framework Step II: Regression Model Estimation
Step II: Regression Model Estimation
• Modelling
– Multiple linear regressi...
Framework Step II: Regression Model Estimation
Step II: Regression Model Estimation
• Modelling
– Multiple linear regressi...
Framework Step II: Regression Model Estimation
Regression Model Results
Table 3: Regression model estimation results (N = ...
Framework Step II: Regression Model Estimation
Negative Binomial Regression Model (NB2)
• The NB2 model is a generalized l...
Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix...
Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix...
Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix...
Framework Step III: Allocation of Ticket Inspectors
Step III: Allocation of ticket inspectors
1 Construction of the matrix...
Framework Step III: Allocation of Ticket Inspectors
Matrix of slots for inspection
Information Slot
1 2 3 4 5
Bus route
Bu...
Framework Step III: Allocation of Ticket Inspectors
Minimum number of ticket inspectors
Information Slot
1 2 3 4 5
Bus rou...
Framework Step III: Allocation of Ticket Inspectors
Productivity of each inspector
Information Slot
1 2 3 4 5
Bus route
Bu...
Framework Step III: Allocation of Ticket Inspectors
Allocation of ticket inspectors
Information Slot
1 2 3 4 5
Bus route
B...
Framework Step III: Allocation of Ticket Inspectors
Marginal Productivity of Inspectors by SEL
µ = 60 [pax/hr]
1020304050
...
Framework Step IV: Cost-Benefit evaluation
Step IV: Cost-benefit evaluation
µ = 60 [pax/hr], cw = 3.8 [US$/hr]
cw = 3.8
Π*
=...
Results
Main Results
Regression model assumptions and goodness of fit
• All parameters obtained in the regression models we...
Results
Main Results
The impact of ticket inspectors depends on the SEL of the
municipality and the period of the day
• Th...
Conclusions
Conclusions
1 Fare evasion levels are the product of a combination of factors
including the level of the servi...
Conclusions
Conclusions
1 Fare evasion levels are the product of a combination of factors
including the level of the servi...
Conclusions
Conclusions
1 Fare evasion levels are the product of a combination of factors
including the level of the servi...
Further Research
Further Research
1 Formulation of a cost-benefit analysis model to help authorities
determine the budget d...
Further Research
Further Research
1 Formulation of a cost-benefit analysis model to help authorities
determine the budget d...
Acknowledgements
Acknowledgements
This research was benefited from the support of:
• Center for Sustainable Urban Developme...
“Understanding fare evasion in urban bus systems:
Evidence from Santiago, Chile”
BRT Centre of Excellence Webinar
Pablo Gu...
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Webinar Sesion: "Understanding fare evasion in urban bus systems: Evidence from Santiago, Chile"

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Webinar Session presented by Pablo Guarda (CEDEUS), on January 28th, 2016.
BRT Centre of Excellence

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Webinar Sesion: "Understanding fare evasion in urban bus systems: Evidence from Santiago, Chile"

  1. 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. 2. Outline 1 Introduction 2 Framework 3 Results 4 Conclusions 5 Further Research Pablo Guarda (CEDEUS) Webinar January 28, 2016 2 / 27
  3. 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. 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. 5. Introduction Bus Fare Collection System Bus Fare Collection System Bus Stop Door 3 Door 2 Door 1 Pablo Guarda (CEDEUS) Webinar January 28, 2016 5 / 27
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 16. Framework Step I: Data Collection Step 1: Data Collection Pablo Guarda (CEDEUS) Webinar January 28, 2016 8 / 27
  17. 17. Framework Step I: Data Collection Step 1: Data Collection Anonymous observers Pablo Guarda (CEDEUS) Webinar January 28, 2016 8 / 27
  18. 18. Framework Step I: Data Collection Step 1: Data Collection Anonymous observers Pablo Guarda (CEDEUS) Webinar January 28, 2016 8 / 27
  19. 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. 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. 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. 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. 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. 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$) 750.766 5797.781 1278.447 766.218 0.599 Doors 2.000 4.000 2.934 0.780 0.266 Occupancy (pax/bus) 0.000 153.000 16.862 16.833 0.998 Bus Size (pax) 50.000 161.000 89.054 42.217 0.474 Level of Occupancy (%) 0.000 100.000 19.905 17.668 0.888 Frequency (bus/hr) 3.000 22.000 6.000 2.979 0.497 Headway (min) 2.727 20.000 9.328 3.320 0.356 Pablo Guarda (CEDEUS) Webinar January 28, 2016 11 / 27
  25. 25. Framework Step I: Data Collection Descriptive Statistics Table 2: Summary statistics for the non-categorical variables (N = 105,497 observations) Variable Min Max Mean Standard Deviation Coe cient of Variation Evasion (pax) 0.000 76.000 0.301 1.309 4.345 Boarding (pax) 0.000 102.000 1.015 2.913 2.867 Exiting (pax) 0.000 123.000 1.036 2.795 2.698 Monthly Household Income (US$) 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. 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. 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. 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. 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. 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. 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 Pablo Guarda (CEDEUS) Webinar January 28, 2016 13 / 27
  32. 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 Pablo Guarda (CEDEUS) Webinar January 28, 2016 14 / 27
  33. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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