BRT Workshop - Fare Collection in the Broader Payments Environment

Ricardo Giesen, BRT Workshop, Rio 2013

Fare Collection in the Broader Payments Environment

Ricardo GiesenPontificia Universidad Católica de Chile

BRT Workshop: Experiences and ChallengesRio de Janeiro, July 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)


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


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


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


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

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!


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


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


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


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)


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


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


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


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


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


Real-Time Functions

Demand

CONTROL CENTER

Prediction

Estimation of current conditionsSupply

ADCSIncidents/Events

ADCSIncidents/Events

Vehicle Locations Loads

Monitoring

Dynamic rescheduling

Information• travel times• paths


Managing for uncertainty

TimingStrategy 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


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


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)


Histogram of bip! transactions


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).


Transactions localized spatially


Source: Munizaga and Palma 2012

Transactions localized spatially


Boarding point

First route

Second route

Third route

User i

Biks1

21iks

11iks

41iks

51iks

jiks1

ikV1

jiks2

jiks3

Biks2

Biks3

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


• Three types of transactions are distinguish:

•Bus

•Metro

•Multi-service stop

Alighting Stop Estimation: Open AFC



• Identify service

• Position of the next transaction

• Closest stop to the next transaction

Alighting Stop Estimation: Bus



boarding

Next tap

Closest point ?

Alighting stop

estimation ?

Look for the point that minimizes

generalized travel time

i

d

. . . . . .

. . . . . . .

Alighting Stop Estimation: Bus




• Metro Station: minimum generalized time to the position to the next transaction

• Route Estimation: minimum time

Alighting Stop Estimation: Metro



• Identify services stopping at multi-service stop


• 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



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



Route 1

Route 2

Route 3

Boarding stop

Alighting stop

Bus Route OD Inference: Closed system


Journey 11. Enter East Croydon NR station, 7:462 & 3. Out-of-station interchange to Central Line at

Shepherds Bush, 8:304. Exit LU at White City, 8:355. Board 72 bus at Westway, 8:366. Alight 72 bus at Hammersmith Hospital, 8:42

Journey 27. Board bus 7 at Hammersmith Hospital, 16:178. Alight bus 7 at Latymer Upper School, 16:199. Board bus 220 at Cavell House, 16:2110. Alight bus 220 at White City Station, 16:2411. Enter LU at Wood Lane, 16:2512 & 13. Out-of-station interchange from Circle or

Hammersmith & City to District or Piccadilly, 16:40

14. Exit LU at Parsons Green, 16:56


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


Station Specific Analysis

36MIT, Transit Leaders Roundtable, Nov. 2012


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


Preliminary Results: Location of trips destinations

Histogram of trips per day for the subsample


Histograma de Etapas para la submuestra

Preliminary Results: Histogram of trips per day per bip!


Histograma de Etapas para la submuestra

Preliminary Results: Histogram of Stages per Trip


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


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


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


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 …


Performance Measurement (PM)

Transantiago Buses


• Filtering data errors

Data Pre-processing

Source: Cortés et al 2011


Data Pre-processing



Projection of GPS point to the route

Data Pre-processing



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


Time- Space Diagram for one Service


Time-Space Diagram



Computing commercial speed



Average Speed per Service

GoodVery bad Bad Acceptable

Excelent

Km/h


Average Speed per service-segment(Spatial desegregation)

Level of service


Average Speed per service-segment(Temporal desagregation)

Morning Peak

Off-Peak


• We can obtain a matrix sij per service (i: segment; j: period)

• Global Indicator aggregated per segment

sR = reference speed

Commercial Speed Computation



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



Global Results (All Services)Septembre 2008

March 2009

April 2009



Global Results (group of services)


Results for a Particular Service

Time-spacedesegregated


Results for a Particular Service (in the map)


Allow detecting problems, propose solutions, improve management.

Results for a Particular Service (in the map)


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


Traveler Information

• The role of information

• Real-time information

▫Location

▫Comprehensiveness

▫Type


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


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


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


Fare Collection in the Broader Payments Environment

Ricardo GiesenPontificia Universidad Católica de Chile

BRT Workshop: Experiences and ChallengesRio de Janeiro, July 2013

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BRT Workshop - Fare Collection in the Broader Payments Environment