brt workshop - the customer experience
DESCRIPTION
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).TRANSCRIPT
1
The Customer Experience
Nigel H.M. WilsonProfessor of Civil & Environmental Engineering
MIT
email: [email protected]
2
Outline
• The changing environment and customer expectations• Agency/Operator Functions• Customer Information Strategies• Recent Research
• Measuring Service Reliability• Role for Customer Surveys• Customer Classification
• Summary and Prospects
Nigel Wilson, MIT Rio de Janeiro, July 2013
3
The Changing Environment and Customer Expectations
• Many customers expect a personal relationship with service providers, e.g., Amazon
• Information technology advances provide raised expectations and new opportunities
• Wireless communications raise expectations for good real-time information
• Rising incomes result in more choice riders and fewer captive riders
• Finance for capital and operations remains a challenge
Nigel Wilson, MIT Rio de Janeiro, July 2013
Key Transit Agency/Operator Functions
A. Off-Line Functions
• Service and Operations Planning (SOP)• Network and route design• Frequency setting and timetable development• Vehicle and crew scheduling
• Performance Measurement (PM)• Measures of operator performance against SOP
• Measures of customer experience
4Nigel Wilson, MIT
Rio de Janeiro, July 2013
Key Transit Agency/Operator Functions
B. Real-Time Functions
• Service and Operations Control and Management (SOCM)• Dealing with deviations from SOP, both minor and major• Dealing with unexpected changes in demand
• Customer Information (CI)• Information on routes, trip times, vehicle arrival times, etc.
• Both static (based on SOP) and dynamic (based on SOP and SOCM)
5Nigel Wilson, MIT
Rio de Janeiro, July 2013
Key Functions
6
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
Nigel Wilson, MIT Rio de Janeiro, July 2013
Evolution of Customer Information
• Operator view Customer view
• Static Dynamic
• Pre-trip and at stop/station En route
• Generic customer Specific customer
• Information "pull" Information "push"
77
Nigel Wilson, MIT Rio de Janeiro, July 2013
Enabling Technologies
• AVL provides current vehicle locations
• Automated scheduling systems make service plan accessible
• Google Transit standard formats provide universal trip planning
• GPS- and WIFI cell phones provide current customer location
• AFC provides database on individual trip-making
• Wireless communication/Internet apps
88
Nigel Wilson, MIT Rio de Janeiro, July 2013
State of Research/Knowledge in CI
• Pre-trip journey planner systems widely deployed but with limited functionality in terms of recognizing individual preferences (e.g., Google Transit)
• Next vehicle arrival times at stops/stations well developed and increasingly widely deployed• both often strongly reliant on veracity of service schedules• ineffective in dealing with disrupted service
• Real-time mobile phone information• open data• many new apps, some great, some not so great• Google's entry may be game-changer in the long run
9Nigel Wilson, MIT
Rio de Janeiro, July 2013
Example of Well-Designed Mobile Web App: NextBus.com/webkit
• First finds your location
• Lists all services and nearest stops for each within 1/4 mile radius
• Scrolls to show next two vehicles for each service in each direction
• www.nextbus.com/webkit
10Nigel Wilson, MIT
Rio de Janeiro, July 2013
Emerging Possibilities
• Exception-based CI based on stated and revealed individual preferences, typical individual trip-making, and current AVL data
• Integration of AFC and CI functions through payment-capable cell phones
• Can CI actually attract more customers?• multi-modal trip planner/navigation systems
11Nigel Wilson, MIT
Rio de Janeiro, July 2013
Medium-term Vision
Transit becomes a virtual presence on mobile devices:• Transit is information-intensive mobility service
• Cell phone is a mobile information device, a perfect match
• People (will) have their lives on their smart phones• Single device for payment and information
• “Station in your pocket”: no need to restrict countdown clocks, status updates, trip guides to stations or fixed devices
• Lifestyle services: guaranteed connections, in-station navigation, bus stop finder, transit validation, rendezvous, …
12Nigel Wilson, MIT
Rio de Janeiro, July 2013
13
Recent Research
• Measuring Service Reliability
• Roles for Customer Surveys
• Customer Classification
Nigel Wilson, MIT Rio de Janeiro, July 2013
Reliability Metrics
• Goal: characterize transit service reliability from passenger's perspective
• Application: London rail services• entry and exit fare transactions• train tracking data
• Application: London bus services• typically high frequency• entry fare transactions only
14
Sources:
"Service Reliability Measurement Framework using Smart Card Data: Application to the London Underground." David Uniman, MST Thesis, MIT (2009)
"Automatic Data for Applied Railway Management: Passenger Demand, Service Quality Measurement, and Tactical Planning on the London Overground Network." Michael Frumin, MST Thesis, MIT (2010)
"Applications of Automatic Vehicle Location Systems Towards Improving Service Reliability and Operations Planning in London." Joseph Ehrlich, MST Thesis, MIT (2010)
Nigel Wilson, MIT Rio de Janeiro, July 2013
Excess Journey Time (EJT)
15Nigel Wilson, MIT
Rio de Janeiro, July 2013
Example: Reliability Metrics - Rail
High Frequency Service• use tap-in and tap-out times to measure actual station-station journey
times
• characterize journey time distribution measures such as Reliability Buffer Time, RBT (at O-D level):
16
RBT = Additional time a passenger must budget to arrive on time for most of their trips (≈ 95% of the time)
50th perc.
% of Journeys
Travel Time95th perc.
RBT
Nigel Wilson, MIT Rio de Janeiro, July 2013
Line Level ERBT
17
Victoria Line, AM Peak, 2007
Trav
el T
ime
(min
)
February November
NB(5.74)
SB(10.74)
NB(6.54)
SB(7.38)
12.00
10.00
8.00
6.00
4.00
2.00
0.00
Excess RBT
Baseline RBT
4.18 5.524.185.52
1.56
5.22
2.36
1.86
Period-Direction
Nigel Wilson, MIT Rio de Janeiro, July 2013
Reliability Metrics: Bus
Challenge to measure passenger journey time because:• no tap-off, just tap-on• tap-on occurs after wait at stop, but wait is an important part of
journey time
Strategy:• trip-chaining to infer destination for all possible boardings• AVL to estimate:
• average passenger wait time (based on assumed passenger arrival process)
• actual in-vehicle time
18Nigel Wilson, MIT
Rio de Janeiro, July 2013
19
Role for Customer Surveys
• Agencies/operators have traditionally relied on customer surveys for data on:• multi-modal trip-making
• demographics
• attitudes and perceptions
• Surveys provide the base for travel demand modeling• Surveys will remain important, but can they be more cost-
effective and reliable?• Research in London compared Oyster records with LTDS
(Household survey) responses for approximately 4,000 individuals in 2011-2012
Nigel Wilson, MIT Rio de Janeiro, July 2013
20
Concerns with Household Surveys
• Expensive and usually conducted infrequently
• Public Transport trips may not be fully captured
• Gathering representative data is becoming more difficult
• Large journey sample over multiple days is desired for public transport planning purposes
• Relies on respondent’s memory
Nigel Wilson, MIT Rio de Janeiro, July 2013
21
Summary of Matching Specific LTDS and Oyster (OR) Journey Stages
• 46% of LTDS stages had matching OR Stages
• 51% of OR Stages had matching LTDS Stages
Source: "Utilizing Automatically Collected Smart Card Data to Enhance Travel Demand Surveys." Laura Riegel, MST Thesis, MIT (June 2013)
Nigel Wilson, MIT Rio de Janeiro, July 2013
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LTDS vs. Oyster Stages for People with Weekday Travel Days
Avg. OR on All Captured
Weekdays
Avg. OR on All Possible Weekdays
LTDS on Travel Day
OR on Travel Day
LTD
S or
Oys
ter S
tage
s
20
15
10
5
0
Nigel Wilson, MIT Rio de Janeiro, July 2013
23
Variability of PT Travel
• The surveyed travel day is not representative of all days: • the single day overestimates typical PT use overall
• underestimates the intensity of PT use on the days it is used
• People who used PT in the survey used it only about half the time (over a four week period), leading to an overestimate of typical PT use.
• The reported frequency of use is much higher than actual PT use and may not be the most accurate way to scale up reported travel day responses
Nigel Wilson, MIT Rio de Janeiro, July 2013
24
Recommendations
• It is difficult to combine survey and AFC data after the survey • AFC records could be used during the interview with a card
reader and tablet to enhance the survey process • AFC records over two weeks (or other time period) could be
used to supplement questions regarding PT frequency of use • A customer panel could be created to understand variability in
travel behavior over time • OD matrix estimation and trip chaining could be used to
calculate exact trip attributes (start time, duration speeds)
Nigel Wilson, MIT Rio de Janeiro, July 2013
25
Online Customer Survey Strategy
• Aim was to demonstrate the potential of online surveys to gather detailed and representative information from public transport customers identified through Oyster records
• Application was to understand customer behavior in multi-route corridors
Source: "Bus Use Behavior in Multi-Route Corridors." Cecilia Viggiano, MST Thesis, MIT (2013)
Nigel Wilson, MIT Rio de Janeiro, July 2013
26
Online Customer Survey Strategy
• Survey e-mailed to about 52,000 registered Oyster Card holders who had used the routes of interest in the prior two weeks
• Incentive was an iPad awarded to a random respondent
• Response rate of 18% yielded over 9,400 responses
Source: "Bus Use Behavior in Multi-Route Corridors." Cecilia Viggiano, MST Thesis, MIT (2013)
Nigel Wilson, MIT Rio de Janeiro, July 2013
27
Customer Classification Research
Aims:• identify homogeneous groups of passengers through analysis
of Oyster records• investigate the representativeness of registered Oyster Card
holders• understand the attrition over time of individual Oyster cards
Source: "Classification of London’s Public Transport Users Using Smart Card Data." Meisy Ortega, MST Thesis, MIT (2013)
Nigel Wilson, MIT Rio de Janeiro, July 2013
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Methodology
• Identify Oyster Card clusters based on a number of explanatory variables:
Temporal characteristics• Travel Frequency No. travel days and trips per day• Journey Start Time First and last journeys of the day
Spatial characteristics• Origin Frequency No. of different first and last origins of
the day• Travel Distance Maximum and minimum distance
traveled
Activity Pattern characteristics • Activity Duration Main and shortest activity of the day
Mode Choices No. of bus-only and rail-only days
Sociodemographic Travelcard or Special Discount (Freedom, Student/Child, Staff)
• Clustering process based on identifying homogenous groups of travelersNigel Wilson, MIT Rio de Janeiro, July 2013
29
Travel Frequency
• London Oyster data for 1-7 October, 2012• Number of days a card was used over a week• Many cards are used only one day per week• Bimodal distribution:
• 1 day a week• 5 days a week
• Similar usage patterns in Santiago, Chile and Kochi City, Japan
Source: http://www.coordinaciontransantiago.cl
Number of Days
% o
f Oys
ter C
ards
Pay as You Go Period Pass242220181614121086420
1 2 3 4 5 6 7Number of Days
Number of Days
25
20
15
10
5
01 2 3 4 5 6 7
% o
f Oys
ter C
ards
Santiago, June 2010
Nigel Wilson, MIT Rio de Janeiro, July 2013
30
Activity Patterns
• London weekday activity direction
• Main activity: Activity of the day with the longest duration.• Two peaks: 1- 3 and 7-9 hours.
• Shortest activity: Activity of the day with the shortest duration (If user has only one activity, main and shortest activity are the same)• Clear peak at one hour.
Activity: Refers to actions users perform between journeys.
Activity duration: Time lapsed between a tap-out and the subsequent tap-in.
% o
f Oys
ter C
ards
bei
ng o
bser
ved
durin
g w
eekd
ays 12
10
8
6
4
2
0
Activity Duration (hours)
Main Activity Shortest Activity
0.5-
1.0
1.0-
1.5
1.5-
2.0
2.0-
2.5
2.5-
3.0
3.0-
3.5
3.5-
4.0
4.0-
4.5
4.5-
5.0
5.0-
5.5
5.5-
6.0
6.0-
6.5
6.5-
7.0
7.0-
7.5
7.5-
8.0
8.0-
8.5
8.5-
9.0
9.0-
9.5
9.5-
10.0
10.0
-10.
510
.5-1
1.0
11.0
-11.
511
.5-1
2.0
12.0
-12.
512
.5-1
3.0
13.0
-13.
513
.5-1
4.0
Nigel Wilson, MIT Rio de Janeiro, July 2013
31
Passenger Groups
Cluster Frequency Start Times Mode Type of Card
Regular Users
1. Everyday regular users 7 days w: 8:30 – 19:30
we: 9:30 – 18:15 Mixed Travelcard
2. All week regular users 6 days w:10:30 – 16:30
we: 13:30 – 17:00 Mixed Mix PAYG/Travelcard
3. Weekday rail regular users 5 weekdays 7:30 – 15:30 Rail Travelcard
4. Weekday bus regular users 5 weekdays 9:30 – 16:00 Bus Child bus
pass
Occasional Users
5. All week occasional users 3 days 15:30 – 18:00 Mixed PAYG
6. Weekday bus occasional users 2 weekdays 13:00 – 15:30 Bus PAYG
7. Weekend occasional users 2 weekend days 17:30 – 20:30 Mixed PAYG
8. Weekday rail occasional users 1 weekday 13:00 - 14:00 Rail PAYG
Exclusive Commuters
Non-Exclusive
Commuters
Non-Commuter Residents
LeisureTravelers
Nigel Wilson, MIT Rio de Janeiro, July 2013
Visitor Travel Patterns Cluster
Regular Users
1. Everyday regular users
Non-
Exclusiv
e Commuters
2. All week regular users
3. Weekday rail regular users
Exclusiv
e Commuters
4. Weekday bus regular users
Occasional Users
5. All week occasional users
Non-
Commuter
Resident
s
6. Weekday bus occasional users
7. Weekend occasional users Leis
ure Travelers8. Weekday rail
occasional users
• Visitor Oyster Card analysis (April 2012)• High number of short-to-medium duration activities• Trips start during off-peak periods• Activities focused in Central London• Long walking trips between public transport trips• High number of rail trips
• Leisure traveler groups similar behavior to Visitor Oyster Card holders
• Possible identification of visitors (not holding VOC)
Visitor Oyster Card Cluster Distribution
Type of ClusterOccasional Regular
Cluster 1
Cluster 5
Cluster 2
Cluster 6Cluster 3
Cluster 7
Cluster 4
Cluster 8
% o
f Vis
itor O
yste
r Car
ds
80
70
60
50
40
30
20
10
0
% o
f Eac
h Sa
mpl
e
Activity Duration (hours)
0.5-
1.0
1.0-
1.5
1.5-
2.0
2.0-
2.5
2.5-
3.0
3.0-
3.5
3.5-
4.0
4.0-
4.5
4.5-
5.0
5.0-
5.5
5.5-
6.0
6.0-
6.5
6.5-
7.0
7.0-
7.5
7.5-
8.0
8.0-
8.5
8.5-
9.0
9.0-
9.5
9.5-
10.0
10.0
-10.
510
.5-1
1.0
11.0
-11.
511
.5-1
2.0
12.0
-12.
512
.5-1
3.0
13.0
-13.
513
.5-1
4.0
12
10
8
6
4
2
0
Visitor Non-Visitor
04:0
0-04
:59
05:0
0-05
:59
06:0
0-06
:59
07:0
0-07
:59
08:0
0-08
:59
09:0
0-09
:59
10:0
0-10
:59
11:0
0-11
:59
12:0
0-12
:59
13:0
0-13
:59
14:0
0-14
:59
15:0
0-15
:59
16:0
0-16
:59
17:0
0-17
:59
18:0
0-18
:59
19:0
0-19
:59
20:0
0-20
:59
21:0
0-21
:59
22:0
0-22
:59
23:0
0-23
:59
Start Time%
of E
ach
Sam
ple
121110
9876543210
Visitor Non-Visitor
33
Registered Users
• Registered users are distributed differently among clusters
• Regular user clusters have higher percentage of registered cards
• Representative characteristics in each cluster, but more similarity with regular users behavior
Clus
ter 1
Clus
ter 2
Clus
ter 3
Clus
ter 4
Clus
ter 5
Clus
ter 6
Clus
ter 7
Clus
ter 8
% o
f eac
h cl
uste
r of O
yste
r Car
ds
70
60
50
40
30
20
10
0
Cluster 1 First and Last Journey Start Times
Start Time
Rela
tive
% o
f Oys
ter C
ards 14
12
10
8
6
4
2
0
04:0
0-04
:59
05:0
0-05
:59
06:0
0-06
:59
07:0
0-07
:59
08:0
0-08
:59
09:0
0-09
:59
10:0
0-10
:59
11:0
0-11
:59
12:0
0-12
:59
13:0
0-13
:59
14:0
0-14
:59
15:0
0-15
:59
16:0
0-16
:59
17:0
0-17
:59
18:0
0-18
:59
19:0
0-19
:59
20:0
0-20
:59
21:0
0-21
:59
22:0
0-22
:59
23:0
0-23
:59
Registered Total
Cluster 2 Activity Duration
Activity Duration (hours)
Rela
tive
% o
f Oys
ter C
ards
8
7
6
5
4
3
2
1
0
0.5-
1.0
1.0-
1.5
1.5-
2.0
2.0-
2.5
2.5-
3.0
3.0-
3.5
3.5-
4.0
4.0-
4.5
4.5-
5.0
5.0-
5.5
5.5-
6.0
6.0-
6.5
6.5-
7.0
7.0-
7.5
7.5-
8.0
8.0-
8.5
8.5-
9.0
9.0-
9.5
9.5-
10.0
10.0
-10.
510
.5-1
1.0
11.0
-11.
511
.5-1
2.0
12.0
-12.
512
.5-1
3.0
13.0
-13.
513
.5-1
4.0
Registered Total
Cluster
Regular Users
1. Everyday regular users
Non-Exclusiv
e Commuters
2. All week regular users
3. Weekday rail regular users
Exclusiv
e Commuters
4. Weekday bus regular users
Occasional Users
5. All week occasional users
Non-Commuter
Resident
s
6. Weekday bus occasional users
7. Weekend occasional users Leis
ure Travelers8. Weekday rail
occasional users
34
Oyster Card Attrition Cluster
Regular Users
1. Everyday regular users
Non-
Exclusive Com
muters
2. All week regular users
3. Weekday rail regular users
Exclusive Com
muters
4. Weekday bus regular users
Occasional Users
5. All week occasional users
Non-Com
muter Residents
6. Weekday bus occasional users
7. Weekend occasional users
Leisure
Travelers
8. Weekday rail occasional users
• Oyster Card attrition estimated as a function of active cards in each month
• 2010/2011 Oyster Card data analysis active cards decreased logarithmically.
• Similar attrition rate for 2011/2012 period
• Occasional users have higher attrition
y = -0.1576 ln(x) + 0.8632R2 = 0.9685
Apr-10 Jun-10 Sep-10 Oct-10 --- Log Regression
% o
f Acti
ve O
yste
r Car
ds
100
90
80
70
60
50
40
30
20
10
0
Number of months after observed week0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Cluster 8
Total Sample
Log Regression- - - -
100908070605040302010
0
% o
f Acti
ve O
yste
r Car
ds
Months
Oct
-201
1
Nov
-201
1
Dec
-201
1
Jan-
2012
Feb-
2012
Mar
-201
2
Apr-
2012
May
-201
2
Jun-
2012
Jul-2
012
Aug-
2012
Sep-
2012
Oct
-201
2
35
Findings
• 8 homogenous groups of users with distinctive travel behavior were found logical aggregation in 4 groups:
• Exclusive commuters, non exclusive commuters, leisure travelers, and non-commuter residents
• Visitors similar to occasional user clusters business and leisure
• Different % of registered card users per cluster. Registered users travel behavior more similar to regular users behavior.
• Attrition rates decrease over time. Large drop in number of active cards explained by occasional users behavior
• First step in understanding user attrition
Nigel Wilson, MIT Rio de Janeiro, July 2013
36
Summary
• Realistic to assess service reliability for individuals and journeys• most critical aspect of customer experience
• Home interview surveys can be enhanced with AFC records• Targeted on-line surveys an efficient alternative to other survey
methods• Customer classification is critical in understanding the customer
experience
Nigel Wilson, MIT Rio de Janeiro, July 2013
37
Prospects
Panel data combined with full journey OD estimation and journey time provides the basis for extensive customer experience and behavior analysis including:
• understanding impacts of changes in service and price
• understanding customer attraction, retention, and attrition
• informing "information push" customer information strategies
• documenting the impacts of marketing and promotional strategies
Nigel Wilson, MIT Rio de Janeiro, July 2013
38
AppendixMIT theses used in this presentation
"Service Reliability Measurement Framework using Smart Card Data: Application to the London Underground." David Uniman, MST Thesis (2009)
"Automatic Data for Applied Railway Management: Passenger Demand, Service Quality Measurement, and Tactical Planning on the London Overground Network." Michael Frumin, MST Thesis (2010)
"Applications of Automatic Vehicle Location Systems Towards Improving Service Reliability and Operations Planning in London." Joseph Ehrlich, MST Thesis (2010)
"Utilizing Automatically Collected Smart Card Data to Enhance Travel Demand Surveys." Laura Riegel, MST Thesis (2013)
"Bus Use Behavior in Multi-Route Corridors." Cecilia Viggiano, MST Thesis (2013)
"Classification of London’s Public Transport Users Using Smart Card Data." Meisy Ortega, MST Thesis (2013)
Nigel Wilson, MIT Rio de Janeiro, July 2013