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Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
Webinar: Making use of automated data collection to improve transit effectiveness
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Webinar: Making use of automated data collection to improve transit effectiveness

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  • 1. Making Use of Automated Data Collection to Improve Transit Effectiveness Presented by Nigel Wilson Professor of Civil and Environmental Engineering Massachusetts Institute of Technology Bus Rapid Transit Centre of Excellence Webinar January 25, 2013
  • 2. OUTLINE • Key Automated Data Collection Systems (ADCS) • Key Transit Agency/Operator Functions • Impact of ADCS on Functions • Traditional Relationships Between Functions • State of Research/Knowledge • Examples of Recent Research with ADCS in London • OD Matrix Estimation • Service Reliability Metrics • Train Load InferenceNigel Wilson MIT 2January 25, 2013
  • 3. Transit Agencies are at a Critical Transition in Data Collection Technology Manual Automatic • low capital cost • high capital cost • high marginal cost • low marginal cost • small sample sizes • large sample sizes • aggregate • more detailed, disaggregate • unreliable • errors and biases can be estimated and corrected • limited spatially and temporally • ubiquitous • not immediately available • available in real-time or quasi real- timeNigel Wilson MIT 3January 25, 2013
  • 4. Automated Data Collection Systems • 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 • 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-timeNigel Wilson MIT 4January 25, 2013
  • 5. ADCS - Potential and Reality Potential • Integrated ADCS database • Models and software to support many agency decisions using ADCS database • Providing insight into normal operations, special events, unusual weather, etc. • Provide large, long-time series disaggregate panel data for better understanding of travel behavior 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 resourcesNigel Wilson MIT 5January 25, 2013
  • 6. 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 service from customer viewpointNigel Wilson MIT 6January 25, 2013
  • 7. 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)Nigel Wilson MIT 7January 25, 2013
  • 8. Impact of ADCS on Functions IMPACT ON SOP • 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 PM • AVL: supports on-time performance assessment • AFC: supports passenger-oriented measures of travel time and reliability IMPACT ON SOCM • AVL: identifies current position of all vehicles, deviations from SOP IMPACT ON CI • AVL: supports dynamic CI • AFC: permits characterization of normal trip-making at the individual level, supports active dynamic CI functionNigel Wilson MIT 8January 25, 2013
  • 9. Relationships Between Functions • Real-time functions (SOCM and CI) based on • SOP • AVI data • Reasonable as long as SOP is sound and deviations from it are not very large • Fundamentally a static model in an increasingly dynamic worldNigel Wilson MIT 9January 25, 2013
  • 10. 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 judgement 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 supportNigel Wilson MIT 10January 25, 2013
  • 11. State of Research/Knowledge in CI • Next vehicle arrival times at stops/stations well developed and increasingly widely deployed • Pre-trip journey planner systems widely deployed but with limited functionality in terms of recognizing individual preferences • Strongly reliant on veracity of SOP • Ineffective in dealing with major disruptionsNigel Wilson MIT 11January 25, 2013
  • 12. Evolution of Customer Information • Operator view --> customer view • route-based --> OD-based • Static --> dynamic • based on SOP --> based on SOP modified by current system state and control actions • Pre-trip and at stop/station  en route • Generic customer  specific customer • Request-based systems  anticipatory systems • Agency/operator developed systems  "app" developers using real time data feeds from agencyNigel Wilson MIT 12January 25, 2013
  • 13. Key Functions Service and Operations Planning (SOP) Off-line Functions Performance Measurement (PM) ADCS System Monitoring, ADCS Supply Analysis, and Prediction Demand Service Management Customer (SOCM) Real-time Functions Information (CI)Nigel Wilson MIT 13January 25, 2013
  • 14. Real Time Functions Vehicle Locations ADCS Loads Incidents/Events Monitoring CONTROL CENTER Dynamic Estimation of current Information Supply rescheduling conditions - travel times Demand - paths PredictionNigel Wilson MIT 14January 25, 2013
  • 15. Examples of Recent Research with ADCS in London • OD Matrix Estimation • Reliability metrics • Individual train load inferenceNigel Wilson MIT 15January 25, 2013
  • 16. Public Transport OD Matrix Estimation Function: Service and Operations Planning Objective: Estimate passenger journey OD matrix at network level Network attributes: • multi-modal rail and bus systems • entry fare control only and/or entry+exit fare controlSource:"Intermodal Passenger Flows on London’s Public Transport Network: Automated Inference of Full PassengerJourneys Using Fare-Transaction and Vehicle-Location Data. Jason Gordon, MST Thesis, MIT (September 2012).Nigel Wilson MIT 16January 25, 2013
  • 17. Trip Chaining: Basic Idea Each AFC record includes: • AFC card ID • transaction type • transaction time • transaction location: rail station or bus route (time-matching with AVL data) B (locB, timeB) D (locD, timeD) A (locA, timeA) C (locC, timeC) The destination of many trip segments (TS) is close to the origin of the following trip segment.Nigel Wilson MIT 17January 25, 2013
  • 18. Trip-Chaining Method for OD Inference Key assumptions for destination inference to be correct: • No intermediate private transportation mode trip segment • Passengers will not walk a long distance • Last trip of a day ends at the origin of the first trip of the dayNigel Wilson MIT 18January 25, 2013
  • 19. Trip-Chaining Method for OD Inference • Infer start and end of each trip segment for individual AFC cards • Link trip segments into complete (one-way) journeys • Integrate individual journeys to form seed OD matrix by time period • Expand to full OD matrix using available control totals • station entries and/or exits for rail • passenger entries and/or exits by stop, trip, or period for bus • train load weight dataNigel Wilson MIT 19January 25, 2013
  • 20. Summary Information on London Application • Off-line tool developed • Oyster fare transactions/day: • Rail (Underground, Overground, National Rail): 6 million (entry & exit) • Bus: 6 million (entry only) • For bus: • Origin inference rate: 94% • Destination inference rate: 74% • For full public transport network: • 73% 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 RAMNigel Wilson MIT 20January 25, 2013
  • 21. Reliability Metrics Function: Performance Measurement Objective: Characterize transit service reliability from passengers perspective, consistently across modes Applications: • London rail services • Underground and Overground • entry and exit fare transactions • train tracking data • London bus services • typically high frequency • entry fare transactions only • AVL dataNigel Wilson MIT 21January 25, 2013
  • 22. Excess Journey Time (EJT): Overground ExampleNigel Wilson MIT 22January 25, 2013
  • 23. Example: Reliability Metrics - Rail High Frequency Service • use tap-in and tap-out times to measure actual station-station journey times • characterize journey time distributions measures such as Reliability Buffer Time, RBT (at O-D level): % of Journeys RBT Travel Time 50th perc. 95th perc. RBT = Additional time a passenger must budget to arrive on time for most of their trips (≈ 95% of the time)Nigel Wilson MIT 23January 25, 2013
  • 24. Line Level ERBT: Underground Example 12.00 Excess RBT 10.00 Baseline RBTTravel Time (min) 8.00 5.22 1.86 6.00 1.56 2.36 4.00 4.18 5.52 4.18 5.52 2.00 0.00 NB SB NB SB (5.74) (10.74) (6.54) (7.38) February November Period-Direction Victoria Line, AM Peak, 2007 Nigel Wilson MIT 24 January 25, 2013
  • 25. 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 to use: • 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 timeNigel Wilson MIT 25January 25, 2013
  • 26. Individual Train Load Inference Function: Off-line (SOP and PM) Objective: Estimate the passenger load for each train at each station Application: London Underground Data: • Oyster: entry station and time, exit station and time for each passenger • NetMIS: train arrival and departure times at stationsNigel Wilson MIT 26January 25, 2013
  • 27. Individual Train Load Inference Passenger Load-based Measures Passenger loading on a vehicle can affect perceptions of time: • seated vs standing • density for standees • ability/willingness to board a vehicle But not generally available from most ADCS systems. Potentially valuable addition to Customer Information and Operations Control Functions, if load estimation is feasible in real-timeNigel Wilson MIT 27January 25, 2013
  • 28. Individual Train Load Inference Challenges: • Oyster Times are truncated to the minute, so entry and exit times have errors of up to 59 seconds • Train tracking (NetMIS) data is incomplete • In-station access, egress, and interchange times are variable • across stations, individuals, and instances • Capacity constraints can be binding and are variable • Many OD pairs have multiple paths available and actual path chosen is unknownNigel Wilson MIT 28January 25, 2013
  • 29. London Underground Network (Central Zone)Nigel Wilson MIT 29January 25, 2013
  • 30. Individual Train Load Inference Definition: "Itinerary" refers to a feasible (sequence of) train(s) for a specific passenger journey. Proposed Methodology: 1. Categorize each observed passenger journey (OD pair, entry, and exit times) as: • Single path or multiple paths (based on OD pair and path choice models) • Single itinerary or multiple itineraries (based on NETMIS data and Oyster trip times)Nigel Wilson MIT 30January 25, 2013
  • 31. Proposed Methodology (contd) 2. Select most likely path for each multiple-paths case 3. Select most likely itinerary for each multiple itinerary case 4. Assign passengers 5. Compare assigned flows with train capacities. If assignment is infeasible, return to Step 2, otherwise terminateNigel Wilson MIT 31January 25, 2013
  • 32. Model Exploration • Apply to Victoria and Jubilee Line OD pairs only – those with complete train tracking data • Examine the following passenger OD pair groups: A • no interchange C • interchange • capacity not • capacity not binding (33% of (1%) binding • single path passengers) • single path B • no interchange D • interchange • capacity may be • capacity not binding (3%) (1%) binding • multiple paths • single pathNigel Wilson MIT 32January 25, 2013
  • 33. Conclusions on LU Train Load Inference Application • Inferring train loads on LU not yet feasible (off-line): • Missing NetMIS data (only 3 of 11 LU lines have complete train- tracking data) • Oyster Timestamp Truncation • Clock Misalignment • Basic groundwork for tackling the off-line problem has been laid • Generation of itineraries works well • Keys to improving results are: • improved modeling of access and egress times • improved path choice modelsNigel Wilson MIT 33January 25, 2013
  • 34. Future Research • Develop better models to support SOCM and CI functions • descriptive • predictive • normative • Evaluate marginal costs and benefits for additional data availability in real time • AFC data • APC dataNigel Wilson MIT 34January 25, 2013
  • 35. February WebinarRegulatory Organization and Contractual Relations Between AgentsProfessor Rosário M. R. Macário Departamento de Engenharia Civil, Arquitectura e Georrecursos Instituto Superior Técnico, Lisbon PortugalThursday, February 21st, 2013 at 1300 CLST (UTC-3)The regulatory setting of urban transport is the background scene where economic andinstitution relations occur. These are in turn regulated through contractual relation. Thelast decades provided a strong development in this field establishing a clear cause-effectrelation between regulatory setting – contractual form – performance of operators andsystem.No universal solution exists and for each reality one must chose the type of contract thatprovides the best fitting and the highest performance incentive to all intervening agents. Inthe case of introduction of a new mode (e.g. BRT) these aspects gain a critical importancesince change is introduced in the system and agents behaviours do adjust to the newcircumstances.

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