Ricardo Giesen, BRT Workshop, Rio 2013
Fare Collection in the Broader
Payments Environment
Ricardo Giesen
Pontificia Unive...
Ricardo Giesen, BRT Workshop, Rio 2013
Motivation
• OD matrices reflect demand’s behavior for a particular
time period
– O...
Ricardo Giesen, BRT Workshop, Rio 2013
Transantiago AFC System
• bip! Transactions (card id and type, fare, vehicle id, ti...
Ricardo Giesen, BRT Workshop, Rio 2013
Outline
• Automated Fare Collection Systems (AFC) & Data
Collection Systems (ADCS)
...
Ricardo Giesen, BRT Workshop, Rio 2013
Automated Data Collection Systems (Buses)
• Automatic Vehicle Location Systems (AVL...
Ricardo Giesen, BRT Workshop, Rio 2013
Automated Data Collection Systems (Buses)
• Automatic Fare Collection Systems (AFC)...
Ricardo Giesen, BRT Workshop, Rio 2013 7
Manual
• low capital cost
• high marginal cost
• small sample sizes
• aggregate
•...
Ricardo Giesen, BRT Workshop, Rio 2013
Opportunities
• ADCS
– monitoring status at various levels of resolution
– measurin...
Ricardo Giesen, BRT Workshop, Rio 2013
ADCS - Potential
• Integrated ADCS database
• Models and software to support many a...
Ricardo Giesen, BRT Workshop, Rio 2013
ADCS - Reality
• Most ADCS systems are implemented independently
• Data collection ...
Ricardo Giesen, BRT Workshop, Rio 2013
Key Transit Agency/Operator Functions
A.Off-Line Functions
• Service and Operations...
Ricardo Giesen, BRT Workshop, Rio 2013
Key Operator Functions: Off-Line Functions
A.1) Service and Operations Planning (SO...
Ricardo Giesen, BRT Workshop, Rio 2013
A.2) Performance Measurement (PM)
• Measures of operator performance against SOP
• ...
Ricardo Giesen, BRT Workshop, Rio 2013
B1) Operations Control and Management
• Dealing with deviations from SOP, both mino...
Ricardo Giesen, BRT Workshop, Rio 2013
B2) Customer Information (CI)
• Information on routes, trip times, vehicle arrival ...
Ricardo Giesen, BRT Workshop, Rio 2013
Key Functions
Off-line Functions
Real-time Functions
Supply Demand
Customer
Informa...
Ricardo Giesen, BRT Workshop, Rio 2013
Real-Time Functions
Demand
CONTROL CENTER
Prediction
Estimation of current
conditio...
Ricardo Giesen, BRT Workshop, Rio 2013
Managing for uncertainty
Timing
Strategy
Operations Planning Real time
Preventive
R...
Ricardo Giesen, BRT Workshop, Rio 2013
Examples of ADCS in Decision Support
• Passenger Flow and System Capacity
• Public ...
Ricardo Giesen, BRT Workshop, Rio 2013
Passenger Flow and System Capacity
• Estimation of passenger flows at the route lev...
Ricardo Giesen, BRT Workshop, Rio 2013
Histogram of bip! transactions
Ricardo Giesen, BRT Workshop, Rio 2013
OD Matrix Estimation
Objective:
• Estimate passenger OD matrix at the network level...
Ricardo Giesen, BRT Workshop, Rio 2013
Transactions localized spatially
Ricardo Giesen, BRT Workshop, Rio 2013
Source: Munizaga
and Palma 2012
Transactions localized spatially
Ricardo Giesen, BRT Workshop, Rio 2013
Vanishing routeBoarding point
First route
Second route
Third route
User i
B
iks1
2
...
Ricardo Giesen, BRT Workshop, Rio 2013
• Three types of transactions are distinguish:
• Bus
• Metro
• Multi-service stop
A...
Ricardo Giesen, BRT Workshop, Rio 2013
• Identify service
• Position of the next transaction
• Closest stop to the next tr...
Ricardo Giesen, BRT Workshop, Rio 2013
i
min ti
+
di->xpost ypost
vcam
×(qtcam /qtvia)
s.a. dpost
£ d
boarding
Next tap
Cl...
Ricardo Giesen, BRT Workshop, Rio 2013
• Position of the next transaction
• Metro Station: minimum generalized time to the...
Ricardo Giesen, BRT Workshop, Rio 2013
• Identify services stopping at multi-service stop
• Position of the next transacti...
Ricardo Giesen, BRT Workshop, Rio 2013
Estimation of alighting stop 
• Compute travel time
• Time until the next transact...
Ricardo Giesen, BRT Workshop, Rio 2013
Route 1
Route 2
Route 3
Boarding stop
Alighting stop
Bus Route OD Inference: Closed...
Ricardo Giesen, BRT Workshop, Rio 2013
Journey 1
1. Enter East Croydon NR station, 7:46
2 & 3. Out-of-station interchange ...
Ricardo Giesen, BRT Workshop, Rio 2013
Example Transport for London
• Oyster fare transactions/day:
• Rail (Underground, O...
Ricardo Giesen, BRT Workshop, Rio 2013
Station Specific Analysis
36MIT, Transit Leaders
Ricardo Giesen, BRT Workshop, Rio 2013
Preliminary Results
• Sample size of 63.221 observations from the week first week o...
Ricardo Giesen, BRT Workshop, Rio 2013
Preliminary Results: Location of trips
destinations
Histogram of trips per day for ...
Ricardo Giesen, BRT Workshop, Rio 2013
Histograma de Etapas para la submuestra
Preliminary Results:
Histogram of trips per...
Ricardo Giesen, BRT Workshop, Rio 2013
Histograma de Etapas para la submuestra
Preliminary Results:
Histogram of Stages pe...
Ricardo Giesen, BRT Workshop, Rio 2013
O/D North West East Center South South-East Oi
North 552 145 195 410 115 125 1542
W...
Ricardo Giesen, BRT Workshop, Rio 2013
What can be obtained?
• Level of service for each “detected” user
- In Vehicle Trav...
Ricardo Giesen, BRT Workshop, Rio 2013
What can be achieved?
– Load Profiles per service per period
– Public Transport O/D...
Ricardo Giesen, BRT Workshop, Rio 2013
Conclusions
• ADCS provide information with a high level
of resolution never seen b...
Ricardo Giesen, BRT Workshop, Rio 2013
Performance Measurement (PM)
Ricardo Giesen, BRT Workshop, Rio 2013
• Filtering data errors
Data Pre-processing
Source: Cortés et al 2011
Ricardo Giesen, BRT Workshop, Rio 2013
Data Pre-processing
Source: Cortés et al 2011
Ricardo Giesen, BRT Workshop, Rio 2013
Projection of GPS point to the route
Data Pre-processing
Source: Cortés et al 2011
Ricardo Giesen, BRT Workshop, Rio 2013
Can we use GPS data to monitor speed?
• We have the position of each bus every 30 s...
Ricardo Giesen, BRT Workshop, Rio 2013
Time- Space Diagram for one Service
Ricardo Giesen, BRT Workshop, Rio 2013
Time-Space Diagram
D2
r
D1
r
Source: Cortés et al 2011
Ricardo Giesen, BRT Workshop, Rio 2013
Computing commercial speed
Source: Cortés et al 2011
Ricardo Giesen, BRT Workshop, Rio 2013
Average Speed per Service
GoodVery bad Bad Acceptable Excelent
Km/h
Ricardo Giesen, BRT Workshop, Rio 2013
Average Speed per service-segment
(Spatial desegregation)
Level of service
Ricardo Giesen, BRT Workshop, Rio 2013
Average Speed per service-segment
(Temporal desagregation)
Morning
Peak
Off-Peak
Ricardo Giesen, BRT Workshop, Rio 2013
• We can obtain a matrix sij per service
(i: segment; j: period)
• Global Indicator...
Ricardo Giesen, BRT Workshop, Rio 2013
Definition of Speed Ranges sR=20[km/hr]
Condition Sijk [Km/h] Ijk Color
Very bad ≤ ...
Ricardo Giesen, BRT Workshop, Rio 2013
Global Results (All Services)
Septembre 2008
March 2009
April 2009
Source: Cortés e...
Ricardo Giesen, BRT Workshop, Rio 2013
Global
Results
(group of
services)
Ricardo Giesen, BRT Workshop, Rio 2013
Results for a Particular Service
Time-space
desegregated
Ricardo Giesen, BRT Workshop, Rio 2013
Results for a Particular Service (in the map)
Ricardo Giesen, BRT Workshop, Rio 2013
Allow
detecting
problems,
propose
solutions,
improve
management.
Results for a Part...
Ricardo Giesen, BRT Workshop, Rio 2013
Real Time Control
• ADCS enabler  measure reliability and its impact on individual...
Ricardo Giesen, BRT Workshop, Rio 2013
Traveler Information
• The role of information
• Real-time information
▫ Location
▫...
Ricardo Giesen, BRT Workshop, Rio 2013
Customer Information
• Traditional
– Static
– Customer service call centers
– Pathf...
Ricardo Giesen, BRT Workshop, Rio 2013
Customer Information
• State of practice
– Web-based applications
• trip planning s...
Ricardo Giesen, BRT Workshop, Rio 2013
Conclusion
• New automated data sources enable a range of
applications and services...
Ricardo Giesen, BRT Workshop, Rio 2013
Fare Collection in the Broader
Payments Environment
Ricardo Giesen
Pontificia Unive...
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BRT Workshop - Fare Collection in the Broader Payments Environment

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O Centro de Excelência em BRT Across Latitudes and Cultures (ALC-BRT CoE) promoveu o Bus Rapid Transit (BRT) Workshop: Experiences and Challenges (Workshop BRT: Experiências e Desafios) dia 12/07/2013, no Rio de Janeiro. O curso foi organizado pela EMBARQ Brasil, com patrocínio da Fetranspor e da VREF (Volvo Research and Education Foundations).

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  • Passenger Flow and System CapacityPublic Transport OD Matrix EstimationPerformance Measurement (PM)Real time demand estimation and control Customer information
  • Automatic de rutas
  • BRT Workshop - Fare Collection in the Broader Payments Environment

    1. 1. Ricardo Giesen, BRT Workshop, Rio 2013 Fare Collection in the Broader Payments Environment Ricardo Giesen Pontificia Universidad Católica de Chile BRT Workshop: Experiences and Challenges Rio de Janeiro, July 2013
    2. 2. Ricardo Giesen, BRT Workshop, Rio 2013 Motivation • OD matrices reflect demand’s behavior for a particular time period – Obtaining OD matrices is a long and expensive process • Mobility Surveys • Traffic Counts • In-vehicle Passenger counts (Passenger per vehicle counts) • New technologies allow to compile cheaper and higher quality information – Automated Fare Collection Systems (AFC)
    3. 3. Ricardo Giesen, BRT Workshop, Rio 2013 Transantiago AFC System • bip! Transactions (card id and type, fare, vehicle id, time) ~ 35M transactions per week > 3M bip! cards observed  10.000 stops • AVL GPS (vehicle id, time, position) ~ 80 a 100 M observations > 6.000 buses
    4. 4. Ricardo Giesen, BRT Workshop, Rio 2013 Outline • Automated Fare Collection Systems (AFC) & Data Collection Systems (ADCS) • ADCS Relationship to key agency functions • Role in Decision Support • Examples of applications and services • Passenger Flow and System Capacity • OD Matrix Estimation • Performance Measurement (PM) • Real-time demand estimation and control (reliability) • Traveler (Customer) information
    5. 5. Ricardo Giesen, BRT Workshop, Rio 2013 Automated Data Collection Systems (Buses) • Automatic Vehicle Location Systems (AVL) • bus location based on GPS • available in real time • Automatic Passenger Counting Systems (APC) • bus systems based on sensors in doors with channelized passenger movements • passenger boarding (alighting) counts for stops/stations with fare barriers • traditionally not available in real-time
    6. 6. Ricardo Giesen, BRT Workshop, Rio 2013 Automated Data Collection Systems (Buses) • Automatic Fare Collection Systems (AFC) • increasingly based on contactless smart cards with unique ID • provides entry (exit) information (spatially and temporally) for individual passengers • traditionally not available in real-time • XFCD (extended floating car data) • Maintenance • Monitoring
    7. 7. Ricardo Giesen, BRT Workshop, Rio 2013 7 Manual • low capital cost • high marginal cost • small sample sizes • aggregate • unreliable • limited spatially and temporally • not immediately available Automatic • high capital cost • low marginal cost • large sample sizes • more detailed, disaggregate • errors and biases can be estimated and corrected • ubiquitous • available in real-time or quasi real-time Transit Agencies are at a Critical Transition in Data Collection Technology We are in the era of BIG DATA!
    8. 8. Ricardo Giesen, BRT Workshop, Rio 2013 Opportunities • ADCS – monitoring status at various levels of resolution – measuring reliability – understanding customer behavior • Data + Computing – simulation-based performance models – robust scheduling – dynamic scheduling • Communications – real time information (demand) – Dynamic response (supply) • Systematic approaches for planning, operations, real time control • Maintenance
    9. 9. Ricardo Giesen, BRT Workshop, Rio 2013 ADCS - Potential • Integrated ADCS database • Models and software to support many agency decisions using ADCS database • Monitoring and insight into normal operations, special events, unusual weather, etc. • Large, long-time series disaggregate panel data for better understanding of travel behavior
    10. 10. Ricardo Giesen, BRT Workshop, Rio 2013 ADCS - Reality • Most ADCS systems are implemented independently • Data collection is ancillary to primary ADC function • AVL - emergency notification, stop announcements • AFC - fare collection and revenue protection • Many problems to overcome: • not easy to integrate data • requires substantial resources • lack of expertise
    11. 11. Ricardo Giesen, BRT Workshop, Rio 2013 Key Transit Agency/Operator Functions A.Off-Line Functions • Service and Operations Planning (SOP) • Performance Measurement (PM) B.Real-Time Functions • Service and Operations Control and Management (SOCM) • Customer Information (CI)
    12. 12. Ricardo Giesen, BRT Workshop, Rio 2013 Key Operator Functions: Off-Line Functions A.1) Service and Operations Planning (SOP) • Network and route design • Frequency setting and timetable development • Vehicle and crew scheduling • ADCS Impacts on SOP • AVL: Provide detailed characterization of route segment running times • APC: Provide detailed characterization of stop activity (boardings, alightings, and dwell time at each stop) • AFC: Give detailed characterization of fare transactions for individuals over time, supports better characterization of traveler behavior
    13. 13. Ricardo Giesen, BRT Workshop, Rio 2013 A.2) Performance Measurement (PM) • Measures of operator performance against SOP • Measures of service from customer viewpoint • ADCS Impacts on PM: • AVL: Supports on-time performance assessment • AFC: Supports passenger-oriented measures of travel time and reliability Key Operator Functions: Off-Line Functions
    14. 14. Ricardo Giesen, BRT Workshop, Rio 2013 B1) Operations Control and Management • Dealing with deviations from SOP, both minor and major • Dealing with unexpected changes in demand • ADCS Impacts on management and control • AVL: Identifies current position of all vehicles, deviations from SOP or desired operational strategy • AFC: Provide real-time information about demand Key Operator Functions: Real-Time Functions
    15. 15. Ricardo Giesen, BRT Workshop, Rio 2013 B2) Customer Information (CI) • Information on routes, trip times, vehicle arrival times, etc. • Both static (based on SOP) and dynamic (based on SOP and SOCM) • ADCS Impacts on Customer Information • AVL: Supports dynamic CI • AFC: Permits characterization of normal trip-making at the individual level, supports active dynamic CI function Key Operator Functions: Real-Time Functions
    16. 16. Ricardo Giesen, BRT Workshop, Rio 2013 Key Functions Off-line Functions Real-time Functions Supply Demand Customer Information (CI) Service Management (SOCM) Service and Operations Planning (SOP) ADCSADCS Performance Measurement (PM) System Monitoring, Analysis, and Prediction
    17. 17. Ricardo Giesen, BRT Workshop, Rio 2013 Real-Time Functions Demand CONTROL CENTER Prediction Estimation of current conditionsSupply ADCS Incidents/Events Vehicle Locations Loads Monitoring Dynamic rescheduling Information • travel times • paths
    18. 18. Ricardo Giesen, BRT Workshop, Rio 2013 Managing for uncertainty Timing Strategy Operations Planning Real time Preventive Run/cycle times Robust schedules Deployment of recovery resources (spare crews) temporarily and spatially Real time (minor) adjustments Supervision and dispatching Corrective Real time operations control Dynamic service plan adjustments and rescheduling Dynamic crew rescheduling Use of spare resources
    19. 19. Ricardo Giesen, BRT Workshop, Rio 2013 Examples of ADCS in Decision Support • Passenger Flow and System Capacity • Public Transport OD Matrix Estimation • Performance Measurement (PM) • Real time demand estimation and control • Customer information
    20. 20. Ricardo Giesen, BRT Workshop, Rio 2013 Passenger Flow and System Capacity • Estimation of passenger flows at the route level • Peak of the peak period and peak segment • Route choice in complex transit systems • Route choice in corridors (parallel routes) shipProfileofthePiccadillyLine25-March-2012 Ravichandran/MIT(harshavr@mit.edu)8 010002000300040005000600070008000900010000 ckfosters-Oakwood akwood-Southgate thgate-AmosGrove rove-BoundsGreen Green-WoodGreen reen-TurnpikeLane Lane-ManorHouse ouse-FinsburyPark nsburyPark-Arsenal enal-HollowayRoad ad-CaledonianRoad nRoad-King'sCross ross-RussellSquare sellSquare-Holborn orn-CoventGarden en-LeicesterSquare are-PiccadillyCircus lyCircus-GreenPark rk-HydeParkCorner orner-Knightsbridge e-SouthKensington on-GloucesterRoad erRoad-Earl'sCourt Court-BaronsCourt ourt-Hammersmith ith-TurnhamGreen Green-ActonTown wn-EalingCommon mmon-NorthEaling thEaling-ParkRoyal ParkRoyal-Alperton rton-SudburyTown yTown-SudburyHill ryHill-SouthHarrow arrow-RaynersLane ynersLane-Eastcote tcote-RuislipManor uislipManor-Ruislip Ruislip-Ickenham ckenham-Hillingdon Hillingdon-Uxbridge Town-SouthEaling hEaling-Northfields fields-BostonManor tonManor-Osterley rley-HounslowEast st-HounslowCentral tral-HounslowWest West-HattonCross nCross-Heathrow4 ross-Heathrow123 ow123-Heathrow5 Figure5:WBRidership--Peak30minutes CapacityRidership 12 trains 11 trains
    21. 21. Ricardo Giesen, BRT Workshop, Rio 2013 Histogram of bip! transactions
    22. 22. Ricardo Giesen, BRT Workshop, Rio 2013 OD Matrix Estimation Objective: • Estimate passenger OD matrix at the network level using AFC and AVL data • Multimodal passenger flows • AFC characteristics • Open (entry fare control only) • Closed (entry+exit fare control) • Hybrid Source: "Intermodal Passenger Flows on London’s Public Transport Network: Automated Inference of Full Passenger Journeys Using Fare- Transaction and Vehicle-Location Data. Jason Gordon, MST Thesis, MIT (September 2012).
    23. 23. Ricardo Giesen, BRT Workshop, Rio 2013 Transactions localized spatially
    24. 24. Ricardo Giesen, BRT Workshop, Rio 2013 Source: Munizaga and Palma 2012 Transactions localized spatially
    25. 25. Ricardo Giesen, BRT Workshop, Rio 2013 Vanishing routeBoarding point First route Second route Third route User i B iks1 2 1iks 1 1iks 4 1iks 5 1iks j iks1 ikV1 j iks2 j iks3 B iks2 B iks3 ikV2 ikV3 ikikikik VVVJ 321 ,, d(a,b) d(a,b) < M ikd1 ikd2 ikd3 Estimated alighting stop Alighting Stop Estimation: Open AFC Source: Chapleau et al 2008
    26. 26. Ricardo Giesen, BRT Workshop, Rio 2013 • Three types of transactions are distinguish: • Bus • Metro • Multi-service stop Alighting Stop Estimation: Open AFC Source: Munizaga and Palma 2012
    27. 27. Ricardo Giesen, BRT Workshop, Rio 2013 • Identify service • Position of the next transaction • Closest stop to the next transaction Alighting Stop Estimation: Bus Source: Munizaga and Palma 2012
    28. 28. Ricardo Giesen, BRT Workshop, Rio 2013 i min ti + di->xpost ypost vcam ×(qtcam /qtvia) s.a. dpost £ d boarding Next tap Closest point ? Alighting stop estimation ? Look for the point that minimizes generalized travel time i d . . . . . . . . . . . . . Alighting Stop Estimation: Bus Source: Munizaga and Palma 2012
    29. 29. Ricardo Giesen, BRT Workshop, Rio 2013 • Position of the next transaction • Metro Station: minimum generalized time to the position to the next transaction • Route Estimation: minimum time Alighting Stop Estimation: Metro Source: Munizaga and Palma 2012
    30. 30. Ricardo Giesen, BRT Workshop, Rio 2013 • Identify services stopping at multi-service stop • Position of the next transaction • Identify common lines: minimum expected generalized time to the position of the next transaction • Assign service: the first of the common lines that passes at that stop Alighting Estimation: Multi-service Stop Source: Munizaga and Palma 2012
    31. 31. Ricardo Giesen, BRT Workshop, Rio 2013 Estimation of alighting stop  • Compute travel time • Time until the next transaction: Transshipment or activity (destination)? Simple rule: t > 45min  Activity Alighting Estimation: Multi-service Stop Source: Munizaga and Palma 2012
    32. 32. Ricardo Giesen, BRT Workshop, Rio 2013 Route 1 Route 2 Route 3 Boarding stop Alighting stop Bus Route OD Inference: Closed system
    33. 33. Ricardo Giesen, BRT Workshop, Rio 2013 Journey 1 1. Enter East Croydon NR station, 7:46 2 & 3. Out-of-station interchange to Central Line at Shepherds Bush, 8:30 4. Exit LU at White City, 8:35 5. Board 72 bus at Westway, 8:36 6. Alight 72 bus at Hammersmith Hospital, 8:42 Journey 2 7. Board bus 7 at Hammersmith Hospital, 16:17 8. Alight bus 7 at Latymer Upper School, 16:19 9. Board bus 220 at Cavell House, 16:21 10. Alight bus 220 at White City Station, 16:24 11. Enter LU at Wood Lane, 16:25 12 & 13. Out-of-station interchange from Circle or Hammersmith & City to District or Piccadilly, 16:40 14. Exit LU at Parsons Green, 16:56
    34. 34. Ricardo Giesen, BRT Workshop, Rio 2013 Example Transport for London • Oyster fare transactions/day: • Rail (Underground, Overground, National Rail): 6 million (entry & exit) • Bus: 6 million (entry only) • For bus: • Origin inference rate: 96% • Destination inference rate: 77% • For full public transport network: • 76% of all fare transactions are included in the seed matrix • Computation time for full London OD Matrix (including both seed matrix and scaling): • 30 mins on 2.8 GHz Intel 7 machine with 8 GB of RAM
    35. 35. Ricardo Giesen, BRT Workshop, Rio 2013 Station Specific Analysis 36MIT, Transit Leaders
    36. 36. Ricardo Giesen, BRT Workshop, Rio 2013 Preliminary Results • Sample size of 63.221 observations from the week first week of September 2008. • 80% of the cases were estimated • 77% of the bip! Cards have complete information
    37. 37. Ricardo Giesen, BRT Workshop, Rio 2013 Preliminary Results: Location of trips destinations Histogram of trips per day for the subsample
    38. 38. Ricardo Giesen, BRT Workshop, Rio 2013 Histograma de Etapas para la submuestra Preliminary Results: Histogram of trips per day per bip!
    39. 39. Ricardo Giesen, BRT Workshop, Rio 2013 Histograma de Etapas para la submuestra Preliminary Results: Histogram of Stages per Trip
    40. 40. Ricardo Giesen, BRT Workshop, Rio 2013 O/D North West East Center South South-East Oi North 552 145 195 410 115 125 1542 West 122 1093 660 983 125 196 3179 East 208 562 1557 1126 404 912 4769 Center 374 824 961 889 509 748 4305 South 124 150 428 612 476 264 2054 South-East 117 177 972 754 217 1261 3498 Dj 1497 2951 4773 4774 1846 3506 19347 Matrix Preliminary Results Origen-Destination Trip Matrix
    41. 41. Ricardo Giesen, BRT Workshop, Rio 2013 What can be obtained? • Level of service for each “detected” user - In Vehicle Travel Time - Transshipment Time including wait - Estimation of Waiting Time for the Initial Trip • Desegregated by – Residential zone – Destination Zone (work, study) – Operator
    42. 42. Ricardo Giesen, BRT Workshop, Rio 2013 What can be achieved? – Load Profiles per service per period – Public Transport O/D Trip Matrix – Passenger Flows at each Stop – Passenger Arrival Pattern at each Stop
    43. 43. Ricardo Giesen, BRT Workshop, Rio 2013 Conclusions • ADCS provide information with a high level of resolution never seen before. • Big data can change the way we do public transport planning and management. • Analysis possibilities are endless …
    44. 44. Ricardo Giesen, BRT Workshop, Rio 2013 Performance Measurement (PM)
    45. 45. Ricardo Giesen, BRT Workshop, Rio 2013 • Filtering data errors Data Pre-processing Source: Cortés et al 2011
    46. 46. Ricardo Giesen, BRT Workshop, Rio 2013 Data Pre-processing Source: Cortés et al 2011
    47. 47. Ricardo Giesen, BRT Workshop, Rio 2013 Projection of GPS point to the route Data Pre-processing Source: Cortés et al 2011
    48. 48. Ricardo Giesen, BRT Workshop, Rio 2013 Can we use GPS data to monitor speed? • We have the position of each bus every 30 secs. • We need to assign buses to services and distinguish between stopping and moving time  Monitor the speed of each route
    49. 49. Ricardo Giesen, BRT Workshop, Rio 2013 Time- Space Diagram for one Service
    50. 50. Ricardo Giesen, BRT Workshop, Rio 2013 Time-Space Diagram D2 r D1 r Source: Cortés et al 2011
    51. 51. Ricardo Giesen, BRT Workshop, Rio 2013 Computing commercial speed Source: Cortés et al 2011
    52. 52. Ricardo Giesen, BRT Workshop, Rio 2013 Average Speed per Service GoodVery bad Bad Acceptable Excelent Km/h
    53. 53. Ricardo Giesen, BRT Workshop, Rio 2013 Average Speed per service-segment (Spatial desegregation) Level of service
    54. 54. Ricardo Giesen, BRT Workshop, Rio 2013 Average Speed per service-segment (Temporal desagregation) Morning Peak Off-Peak
    55. 55. Ricardo Giesen, BRT Workshop, Rio 2013 • We can obtain a matrix sij per service (i: segment; j: period) • Global Indicator  aggregated per segment sR = reference speed Commercial Speed Computation I jk = sR × 1 sijk iå N jk for sijk ¹ 0 Source: Cortés et al 2011
    56. 56. Ricardo Giesen, BRT Workshop, Rio 2013 Definition of Speed Ranges sR=20[km/hr] Condition Sijk [Km/h] Ijk Color Very bad ≤ 15 ≥ 1.333 Red Bad >15 a ≤19 < 1.333 to ≥ 1.053 Orange Barely Acceptable >19 a ≤20 < 1.053 to ≥ 1.0 Yellow Fair >20 to ≤25 < 1.0 to ≥ 0.80 Light Green Good >25 to ≤30 < 0.80 to ≥ 0.667 Dark Green Excellent >30 < 0.667 Blue Source: Cortés et al 2011
    57. 57. Ricardo Giesen, BRT Workshop, Rio 2013 Global Results (All Services) Septembre 2008 March 2009 April 2009 Source: Cortés et al 2011
    58. 58. Ricardo Giesen, BRT Workshop, Rio 2013 Global Results (group of services)
    59. 59. Ricardo Giesen, BRT Workshop, Rio 2013 Results for a Particular Service Time-space desegregated
    60. 60. Ricardo Giesen, BRT Workshop, Rio 2013 Results for a Particular Service (in the map)
    61. 61. Ricardo Giesen, BRT Workshop, Rio 2013 Allow detecting problems, propose solutions, improve management. Results for a Particular Service (in the map)
    62. 62. Ricardo Giesen, BRT Workshop, Rio 2013 Real Time Control • ADCS enabler  measure reliability and its impact on individual pax • Reliability metrics • Contractor performance • Performance from passenger’s point of view • High frequency services • Extensive vehicle interactions • Most customers do not time their arrival to schedules • On-time performance may not be as critical • Schedules can be revised in real time • Fleet management • Communications
    63. 63. Ricardo Giesen, BRT Workshop, Rio 2013 Traveler Information • The role of information • Real-time information ▫ Location ▫ Comprehensiveness ▫ Type
    64. 64. Ricardo Giesen, BRT Workshop, Rio 2013 Customer Information • Traditional – Static – Customer service call centers – Pathfinding at stops / stations/ pedestrian access • Initial ITS – Displays at bus stops (scheduled arrivals / real-time ETA) – Monitors at terminals – Next stop information on-board vehicles (AVA) Source: B. Hemily & A. Rizos
    65. 65. Ricardo Giesen, BRT Workshop, Rio 2013 Customer Information • State of practice – Web-based applications • trip planning systems • Google Transit trip planning – Smart Phone Applications • Static and dynamic information • State of the art – Social Media • Facebook, Twitter – Real-Time Information – Open-Source Traveler Information Software Development • Forthcoming – Special Mobile Applications (e.g. customers with special needs) – “Augmented Reality” and implementation in apps • Combine compass, visual recognition, other tools – Crowdsourcing (Waze for Buses?) Source: B. Hemily & A. Rizos
    66. 66. Ricardo Giesen, BRT Workshop, Rio 2013 Conclusion • New automated data sources enable a range of applications and services for improved level of service and more efficient utilization of resources • Lack of integration – Databases • Legacy systems • Challenge going from data to information • Level of “know – how” Source: B. Hemily & A. Rizos
    67. 67. Ricardo Giesen, BRT Workshop, Rio 2013 Fare Collection in the Broader Payments Environment Ricardo Giesen Pontificia Universidad Católica de Chile BRT Workshop: Experiences and Challenges Rio de Janeiro, July 2013

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