AUTOMATIC DATA COLLECTION:
A New Foundation for Analysis and Management
Nigel H.M. Wilson
Professor of Civil & Environment...
OUTLINE
• Automated Data Collection Systems (ADCS)
• Key Transit Agency/Operator Functions
• Impact of ADCS on Functions
•...
Automated Data Collection Systems
• Automatic Fare Collection Systems (AFC)
• increasingly based on contactless smart card...
Manual
• low capital cost
• high marginal cost
• small sample sizes
• "hard and soft"
• unreliable
• limited spatially and...
ADCS - Potential
• Integrated ADCS database
• Models and software to support many agency decisions
using ADCS database
• M...
ADCS - Reality
• Most ADCS systems are implemented independently
• Data collection is ancillary to primary function
• AVL ...
Key Transit Agency/Operator Functions
Off-Line Functions
• Service and Operations Planning (SOP)
• Network and route desig...
Key Transit Agency/Operator Functions
Real-Time Functions
• Service and Operations Control and Management (SOCM)
• Dealing...
Impact of ADCS on Functions
IMPACT ON Service Planning
• AVL: detailed characterization of route segment running times
• A...
Impact of ADCS on Functions
IMPACT ON Management and Control
• AVL: identifies current position of all vehicles, deviation...
Opportunities
• ADCS
• monitoring status at various levels of resolution
• measuring reliability
• understanding customer ...
Relationships Between Functions
• Real-time functions (SOCM and CI) based on
• SOP
• AVL data
• Reasonable as long as SOP ...
State of Research/Knowledge in SOCM
• Advances in train control systems help minimize impacts of
routine events
• Major di...
Key Functions
Off-line Functions
Real-time Functions
Supply Demand
Customer
Information (CI)
Service
Management
(SOCM)
Ser...
Real-Time Functions
15
Vehicle Locations Loads
Monitoring
Information
- travel times
- paths
Dynamic
rescheduling
Demand
C...
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Theme 2 Automated data collection - a new foundation for analysis and management

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Theme 2 Automated data collection - a new foundation for analysis and management

  1. 1. AUTOMATIC DATA COLLECTION: A New Foundation for Analysis and Management Nigel H.M. Wilson Professor of Civil & Environmental Engineering MIT email: nhmw@mit.edu Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013 1
  2. 2. OUTLINE • Automated Data Collection Systems (ADCS) • Key Transit Agency/Operator Functions • Impact of ADCS on Functions • Traditional Relationships Between Functions • State of Research/Knowledge 2 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  3. 3. Automated Data Collection Systems • 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 • Automatic Vehicle Location Systems (AVL) • bus location based on GPS • train tracking based on track circuit occupancy • 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 • train weighing systems can be used to estimate number of passengers on board • traditionally not available in real-time 3 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  4. 4. Manual • low capital cost • high marginal cost • small sample sizes • "hard and soft" • unreliable • limited spatially and temporally • not available immediately Automatic • high capital cost • low marginal cost • large sample sizes • "hard" • 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 4 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  5. 5. 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 customer experience and travel behavior 5 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  6. 6. ADCS - Reality • Most ADCS systems are implemented independently • Data collection is ancillary to primary function • AVL - emergency notification, stop announcements • AFC - fare collection and revenue protection • Many problems to overcome: • not easy to integrate data • requires resources and expertise 6 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  7. 7. Key Transit Agency/Operator Functions 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 service from customer viewpoint 7 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  8. 8. Key Transit Agency/Operator Functions 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) 8 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  9. 9. Impact of ADCS on Functions IMPACT ON Service Planning • AVL: detailed characterization of route segment running times • APC: detailed characterization of stop activity (boardings, alightings, and dwell time at each stop) • AFC: detailed characterization of fare transactions for individuals over time, supports better characterization of traveler behavior IMPACT ON Performance Monitoring • AVL: supports on-time performance assessment • AFC: supports passenger-oriented measures of travel time and reliability 9 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  10. 10. Impact of ADCS on Functions IMPACT ON Management and Control • AVL: identifies current position of all vehicles, deviations from SOP IMPACT ON Customer Information • AVL: supports dynamic CI • AFC: permits characterization of normal trip-making at the individual level, supports active dynamic CI function 10 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  11. 11. Opportunities • ADCS • monitoring status at various levels of resolution • measuring reliability • understanding customer behavior • Data + Computing • simulation-based performance models • Communications • real time information (demand) • operations management (supply) • Systematic approaches for planning, operations, real- time control 11 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  12. 12. Relationships Between Functions • Real-time functions (SOCM and CI) based on • SOP • AVL data • Reasonable as long as SOP is sound and deviations from it are not very large • Fundamentally a static model in an increasingly dynamic world 12 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  13. 13. State of Research/Knowledge in SOCM • Advances in train control systems help minimize impacts of routine events • Major disruptions still handled in individual manner based on judgment and experience of the controller • Little effective decision support for controllers • Models are often deterministic formulations of highly stochastic systems • Simplistic view of objectives and constraints in model formulation • Substantial opportunities remain for better decision support 13 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  14. 14. 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 14 Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013
  15. 15. Real-Time Functions 15 Vehicle Locations Loads Monitoring Information - travel times - paths Dynamic rescheduling Demand CONTROL CENTER Prediction Estimation of current conditionsSupply ADCS Incidents/Events Nigel Wilson, BRT Workshop: Experiences and Challenges September 2013

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