BRT Workshop - The Customer Experience
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BRT Workshop - The Customer Experience

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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 ...

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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  • These are the variables that we defined to model travel patterns
  • Non Exclusive  34% --- 53%Exclusive  19%Non-Commuter  27% ----- 47%Leisure Travelers  21%Regular users 53% of total cards- 81% of week journeys.
  • The most visible and easy-to-understand mode
  • CONSIDER NUMBER OF CARDS THAT REQUIRE REGISTRATIONRegular users 53% of total cards- 81% of week journeys. Register users  47% of total cards- 51% of week journeys. From these registered cards:61% are regular users who make 86% of the registered user journeys.

BRT Workshop - The Customer Experience BRT Workshop - The Customer Experience Presentation Transcript

  • The Customer Experience Nigel H.M. Wilson Professor of Civil & Environmental Engineering MIT email: nhmw@mit.edu 1
  • 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 2 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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 3 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 4 Nigel 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) 5 Nigel 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" 7 7 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 8 8 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 9 Nigel 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 10 Nigel 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 11 Nigel 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, … 12 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • Recent Research • Measuring Service Reliability • Roles for Customer Surveys • Customer Classification 13 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) 15 Nigel 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 Time 95th perc. RBT Nigel Wilson, MIT Rio de Janeiro, July 2013
  • Line Level ERBT 17 Victoria Line, AM Peak, 2007 TravelTime(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 18 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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 19 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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 20 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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) 21 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • LTDS vs. Oyster Stages for People with Weekday Travel Days 22 Avg. OR on All Captured Weekdays Avg. OR on All Possible Weekdays LTDS on Travel Day OR on Travel Day LTDSorOysterStages 20 15 10 5 0 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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 23 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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) 24 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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) 25 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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) 26 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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 27 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
  • 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 travelers 28 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • Travel Frequency 29 • 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 %ofOysterCards Pay as You Go Period Pass 24 22 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 Number of Days Number of Days 25 20 15 10 5 0 1 2 3 4 5 6 7 %ofOysterCards Santiago, June 2010 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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. 30 Activity: Refers to actions users perform between journeys. Activity duration: Time lapsed between a tap-out and the subsequent tap-in. %ofOysterCardsbeingobservedduringweekdays 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 10.0-10.5 10.5-11.0 11.0-11.5 11.5-12.0 12.0-12.5 12.5-13.0 13.0-13.5 13.5-14.0 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • Passenger Groups 31 Cluster Frequency Start Times Mode Type of Card RegularUsers 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 OccasionalUsers 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 Leisure Travelers Nigel Wilson, MIT Rio de Janeiro, July 2013
  • Visitor Travel Patterns Cluster RegularUsers 1. Everyday regular users Non-Exclusive Commuters 2. All week regular users 3. Weekday rail regular users Exclusive Commuters 4. Weekday bus regular users OccasionalUsers 5. All week occasional users Non-Commuter Residents 6. Weekday bus occasional users 7. Weekend occasional users Leisure Travelers 8. 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 Cluster Occasional Regular Cluster 1 Cluster 5 Cluster 2 Cluster 6 Cluster 3 Cluster 7 Cluster 4 Cluster 8 %ofVisitorOysterCards 80 70 60 50 40 30 20 10 0 %ofEachSample 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 10.0-10.5 10.5-11.0 11.0-11.5 11.5-12.0 12.0-12.5 12.5-13.0 13.0-13.5 13.5-14.0 12 10 8 6 4 2 0 Visitor Non-Visitor 04:00-04:59 05:00-05:59 06:00-06:59 07:00-07:59 08:00-08:59 09:00-09:59 10:00-10:59 11:00-11:59 12:00-12:59 13:00-13:59 14:00-14:59 15:00-15:59 16:00-16:59 17:00-17:59 18:00-18:59 19:00-19:59 20:00-20:59 21:00-21:59 22:00-22:59 23:00-23:59 Start Time %ofEachSample 12 11 10 9 8 7 6 5 4 3 2 1 0 Visitor Non-Visitor
  • Registered Users 33 • 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 Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Cluster8 %ofeachclusterofOysterCards 70 60 50 40 30 20 10 0 Cluster 1 First and Last Journey Start Times Start Time Relative%ofOysterCards 14 12 10 8 6 4 2 0 04:00-04:59 05:00-05:59 06:00-06:59 07:00-07:59 08:00-08:59 09:00-09:59 10:00-10:59 11:00-11:59 12:00-12:59 13:00-13:59 14:00-14:59 15:00-15:59 16:00-16:59 17:00-17:59 18:00-18:59 19:00-19:59 20:00-20:59 21:00-21:59 22:00-22:59 23:00-23:59 Registered Total Cluster 2 Activity Duration Activity Duration (hours) Relative%ofOysterCards 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 10.0-10.5 10.5-11.0 11.0-11.5 11.5-12.0 12.0-12.5 12.5-13.0 13.0-13.5 13.5-14.0 Registered Total Cluster RegularUsers 1. Everyday regular users Non-Exclusive Commuters 2. All week regular users 3. Weekday rail regular users Exclusive Commuters 4. Weekday bus regular users OccasionalUsers 5. All week occasional users Non-Commuter Residents 6. Weekday bus occasional users 7. Weekend occasional users LeisureTravelers 8. Weekday rail occasional users
  • Oyster Card Attrition 34 Cluster RegularUsers 1. Everyday regular users Non-Exclusive Commuters 2. All week regular users 3. Weekday rail regular users Exclusive Commuters 4. Weekday bus regular users OccasionalUsers 5. All week occasional users Non-Commuter 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) + 0.8632 R2 = 0.9685 Apr-10 Jun-10 Sep-10 Oct-10 --- Log Regression %ofActiveOysterCards 100 90 80 70 60 50 40 30 20 10 0 Number of months after observed week 0 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- - - - 100 90 80 70 60 50 40 30 20 10 0 %ofActiveOysterCards Months Oct-2011 Nov-2011 Dec-2011 Jan-2012 Feb-2012 Mar-2012 Apr-2012 May-2012 Jun-2012 Jul-2012 Aug-2012 Sep-2012 Oct-2012
  • 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 35 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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 36 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • 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 37 Nigel Wilson, MIT Rio de Janeiro, July 2013
  • Appendix MIT 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) 38 Nigel Wilson, MIT Rio de Janeiro, July 2013