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

OUTLINE

2

• 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 Inference

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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

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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-time

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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 resources

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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 viewpoint

Key Transit Agency/Operator Functions

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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)

Key Transit Agency/Operator Functions

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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 function

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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 world

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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 support

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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 disruptions

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Evolution of Customer Information

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• 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 agency

Off-line Functions

Real-time Functions

Supply Demand

Customer

Information (CI)

Service

Management

(SOCM)

Service and Operations

Planning (SOP)

ADCS ADCS

Performance

Measurement (PM)

System Monitoring,

Analysis, and Prediction

Key Functions

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Demand

CONTROL CENTER

Prediction

Estimation of current

conditions Supply

ADCS

Incidents/Events

Real Time Functions

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Vehicle Locations Loads

Monitoring

Dynamic

rescheduling

Information

- travel times

- paths

Examples of Recent Research with ADCS in London

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• OD Matrix Estimation

• Reliability metrics

• Individual train load inference

Public Transport OD Matrix Estimation

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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 control

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).

Each AFC record includes:

• AFC card ID

• transaction type

• transaction time

• transaction location: rail station or bus route (time-matching with AVL data)

The destination of many trip segments (TS) is close to the origin of the following trip segment.

A (locA, timeA)

B (locB, timeB)

C (locC, timeC)

D (locD, timeD)

Trip Chaining: Basic Idea

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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 day

Trip-Chaining Method for OD Inference

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• 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 data

Trip-Chaining Method for OD Inference

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Summary Information on London Application

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• 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 RAM

Reliability Metrics

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Function: Performance Measurement

Objective: Characterize transit service reliability from passenger's 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 data

Excess Journey Time (EJT): Overground Example

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Example: Reliability Metrics - Rail

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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):

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

Victoria Line, AM Peak, 2007

Tra

ve

l T

ime

(m

in)

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 5.52 4.18 5.52

1.56

5.22

2.36

1.86

Period-Direction

Line Level ERBT: Underground Example

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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 time

Reliability Metrics: Bus

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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 stations

Individual Train Load Inference

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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-time

Individual Train Load Inference

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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 unknown

Individual Train Load Inference

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London Underground Network (Central Zone)

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Individual Train Load Inference

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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)

Proposed Methodology (cont'd)

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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 terminate

A (33% of passengers)

• no interchange • capacity not

binding • single path

C (1%)

• interchange • capacity not binding • single path

B (3%)

• no interchange • capacity may be

binding • single path

D (1%)

• interchange • capacity not binding • multiple paths

Model Exploration

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• Apply to Victoria and Jubilee Line OD pairs only – those with complete train tracking data

• Examine the following passenger OD pair groups:

Conclusions on LU Train Load Inference Application

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• 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 models

Future Research

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• 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 data

February Webinar

Regulatory Organization and Contractual Relations Between Agents

Professor Rosário M. R. Macário

Departamento de Engenharia Civil, Arquitectura e Georrecursos

Instituto Superior Técnico, Lisbon Portugal

Thursday, February 21st, 2013 at 1300 CLST (UTC-3)

The regulatory setting of urban transport is the background scene where economic and

institution relations occur. These are in turn regulated through contractual relation. The

last decades provided a strong development in this field establishing a clear cause-effect

relation between regulatory setting – contractual form – performance of operators and

system.

No universal solution exists and for each reality one must chose the type of contract that

provides the best fitting and the highest performance incentive to all intervening agents. In

the case of introduction of a new mode (e.g. BRT) these aspects gain a critical importance

since change is introduced in the system and agents behaviours do adjust to the new

circumstances.