Exploring Transit Data for Service Improvement

 
Data Types & Sources
 
 
Unit 2: Describing Transit Systems with Data
 
Outline
 
Types of transit data
 
Manual data collection
 
Automated data collection
 
TYPES OF TRANSIT DATA
 
We need to quantify and characterize our systems with different
 
Service Is Only As Good As Your Data
 
We need accurate data to make appropriate
decisions for transit service.
 
What if we were considering adding a new
line to a transit system?
What information would we need to know to
make appropriate design decisions?
 
What Data is Needed to Maintain Good Service?
 
7 Demands of Useful Service
 
How Transit Services Them
 
Three Main Types of Data
 
System Level Data
Entire operational overview
Useful for long-range planning, comparing service to
similar regions
 
Route Level Data
Characteristics of specific routes
Useful for long-range planning, service planning, route
optimization
 
Trip Level Data
Specific details of trips made on transit system
Useful for service planning, route optimization
 
Service Quality Improvement Cycles
 
Real-
time
loop
 
Off-line
loop
 
System Level: National Transit Database (NTD)
 
 
Established by Congress (Title 49 USC 5335a)
Primary source for statistics on transit in US
FTA grantees required to submit data
>660 providers currently report to NTD
Data used to apportion > $5 billion FTA funds
Freely and publically available
 
 
2011 National Transit Profile
 
 
 
 
 
 
 
 
 
 
Does this look familiar?
 
What is in the NTD?
 
Modules include:
Safety and Security
Financials
Capital funds, operating expenses, operator wages
Assets
Stations and maintenance facilities, transit way
mileage, revenue vehicles
Resources
Number of employees, energy consumption
Transit Service (Supplied and Consumed)
Vehicle revenue miles, Vehicle revenue hours, unlinked
passenger trips, passenger miles traveled
 
NTD Glossary (Expenses)
 
Capital Expenses 
= purchase of equipment
Useful life > one year and Greater than $5,000
Includes Facilities (Guideway, Passenger Stations,
Administration Buildings, Maintenance), Rolling Stock
(vehicles), Other Equipment
Operating Expenses 
= operation of the agency
Function (Vehicle Operations, Vehicle Maintenance, Non-
Vehicle Maintenance, General Administration)
Object Class (Salary&Wages+Fringe= Compensation, Services ,
Materials and Supplies, Utilities, Casualty and Liability,
Purchased Transportation, Other)
 
NTD Glossary (Service Consumed)
 
Unlinked Passenger Trips 
= boardings
Passenger Miles 
= cumulative sum of the distances ridden by each
passenger
Average Trip Length 
= avg distance ridden for = passenger miles /
unlinked passenger trips
Average Passenger Load 
= avg # pass aboard a vehicle at any one
time
 
NTD Glossary (Service Supplied)
 
Average Speed 
= miles / hours in revenue service
Revenue Service 
= operation when passengers can board and ride
on the vehicle
Vehicle Total Miles 
= all miles from pull out to pull in, including
"deadhead"
Vehicle Revenue Miles 
= miles in revenue service
Vehicle Total Hours 
= hours from pull out to pull in, including
"deadhead"
Vehicle Revenue Hours 
= hours in revenue service
 
NTD Glossary (Service Supplied)
 
Revenue Vehicle  
= vehicle in fleet available to operate in revenue
service including spares and out for maintenance
Vehicles Available for Maximum Service = 
vehicles agency has
available to operate revenue
Vehicles Operated Maximum Service 
= largest # vehicles operated
at any one time
Base Period Requirement 
= vehicles needed to serve the base (all-
day) transit service
Peak-to-Base Ratio 
= Max Service / Base Period
Percent Spares 
= (Veh Available – Veh Operated) / Veh Operated
 
NTD Performance Measures
 
Service Efficiency
Operating Expense per Vehicle Revenue Mile
Operating Expense per Vehicle Revenue Hour
 
Service Effectiveness
Operating Expense per Passenger Mile
Operating Expense per Unlinked Passenger Trip
 
Service Effectiveness
Unlinked Passenger Trips per Vehicle Revenue Mile
Unlinked Passenger Trips per Vehicle Revenue Hour
 
IN-CLASS EXERCISE
 
Head to www.ntdprogram.gov to complete the
 
Route and Trip Levels Are Similar
 
We describe unlinked passenger trips with
Route Stop Locations
Route Scheduling & Efficiency
Volumes of Passengers
Access/ Egress Locations
Travel and Boarding Times
Trip Purposes
 
Two ways to collect this detailed data
Manually
Automatically
 
Exact
Characteristics
Depend on
Application
 
MANUAL DATA COLLECTION
 
Engineers can tailor route and trip analyses through
 
Manual Procedure Considerations
 
Instruments:
Pencil/paper
Hand-held units
 
Route Selection:
100 percent
Sample
 
Type of “Checks”:
Ride check: on-board the vehicle
Point checks: at a specific location
 
Surveys also used (at trip-level)
 
Ride Checks
 
Captures an entire route in detail
Data collection is done on-board
Checkers counts the number of boardings,
alightings, & thru-passengers at each stop
Distance between each stop may also be
recorded
 
Output: Route-level & Stop-level ridership
data
 
Ride Check Data Sheet - Example
 
 
Ride Check - Basic Calculations
 
1.
Total Unlinked Passenger Trips on the Route = Sum of all Boardings
 
2.
Load Profile for the Route = Cumulative Boardings @ Stop –
Cumulative Alightings @ Stop
Peak (maximum) load along the route
Average load along the route
 
3.
Passenger Miles Traveled on the Route = Sum of Stop-Level Load *
Distance between Stops
 
Example Load Profile
 
Peak Load Point: 47 Pax
 
Average Load: 24 Pax
 
Point Checks
 
Data collection is done at one (or more)
stops along a route
Number of passengers on the vehicle is
recorded
Often conducted at high (peak) load
points along a route
 
 
Surveys
 
Travel surveys are used to estimate complete
origin-destination patterns
 
Questions include boarding location, alighting
location, transfer location
 
More next lecture
 
 
AUTOMATED DATA COLLECTION
 
Transit operators are increasingly relying on new technologies…
 
Automated Data Sources
 
1.
Automated Fare Collection (AFC)
2.
Automated Passenger Counters (APC)
3.
Automated Vehicle Location (AVL)
 
 
Archived data from AFC and AVL systems is an
important byproduct of installing these systems.
 
Automated Fare Collection (AFC)
 
Magnetic stripe & smart cards
Smart card systems have unique
ID that provides entry (exit)
information at the individual
level
Data not available in real-time
(coming soon!)
Magnetic stripe and smart cards
together can produce
station/stop level data
Rail gateline counts
Bus fare boxes (operator often
punches a key indicating other
fare types, such as free passes)
 
Smart Card Example: OD Estimation
 
AM Inbound trip: tap-in at
“origin”
 
PM Outbound trip: tap-in
at “destination”
 
Infer that passenger is
traveling from Brookhaven
to Midtown and back
 
AM Tap at
Brookhaven
 
PM Tap at
Midtown
 
Automated Passenger Counters (APC)
 
Sensors near vehicle doors count passenger
on and offs, usually using infrared beams
Some vehicles also determine weight/steps
on-board vehicle
Typically data not available in real-time
Usually only on a sample (%) of bus fleet;
redistribute buses to determine counts
 
Automated Vehicle Location (AVL)
 
Originally, Signpost-beacon-based used to track
the location of buses
Post-2000, GPS-based technology
Provided in real-time
Usually matched with schedule through
Computer Aided Dispatch (CAD) systems
Also combined with other data sources (AFC &
APC) to capture detailed ridership
Valuable for service planning (i.e. determining
running times, schedule adherence)
 
Signpost-based AVL
 
GPS-based AVL
 
Combining AVL & APC
 
Detailed Route Level Analysis
 
DATA COLLECTION COMPARISON
 
Manual vs. Automated Data
 
Manual Data
 
Low capital costs
High marginal (labor) costs
Small sample sizes
Limited spatial and
temporal variation
Often unreliable (errors by
checkers)
Longer data
collection/processing times
 
Automated Data
 
High capital costs
Low marginal (labor) costs
Large sample sizes
Detailed temporal and spatial
data
Biases can (usually) be corrected
Available in (quasi) real-time
 
Passenger Counting Technologies (2008)
 
Future Trends
 
Increasing use of automated data
collection systems
Including mixed modes (manual &
automated)
 
More disaggregate data used for planning
and decision-making (refined units of
analysis)
 
 
DATA STANDARDS
 
Transit Data Consumption
The changing landscape
Schedule
Paper Schedules
 
10
9:36
 
Digitization
 
Interactivity
General Transit Feed Specification
routes.txt
stops.txt
trips.txt
stop_times.txt
calendar.txt
agency.txt
shapes.txt
 
How Does Open Data Help?
 
Data access models
 
Agency responds to
special requests by
developers
 
Small subset
of riders find
this specific
tool useful.
 
Transit
Agency
 
App
Developers
 
Riders
 
DATA
 
DATA
 
Anyone can
access data
 
Many riders access a diverse market
of tools powered by GTFS.
 
Agency
produces data
and opens it
once.
 
Transit Open Data Timeline
 
Source: Rojas, Francisca (2012) Transit Transparency: Effective Disclosure through Open Data
 
Open Schedule Data (GTFS) Adoption
 
44
 
Source: Wong, James. (2013). Leveraging the General Transit Feed Specification (GTFS) for Efficient Transit Analysis. Proceedings
of the 2013 Transportation Research Board Annual Meeting.
 
Conclusions
 
We need accurate data to make appropriate decisions
for transit service.
 
The National Transit Database (NTD) is the largest
source of transit data in the US.
 
Manual data collection consists in recording transit
information in person.
 
Automatic data collection uses captors in buses to
record vehicle location, vehicle loads, etc.
 
Future trend of opening up data based on standardized
formats.
 
Reference
 
Materials in this lecture were taken from:
Walker, J. (2011). 
Human transit: How clearer thinking about
public transit can enrich our communities and our lives
.
Island Press.
Furth, Hemily, Muller, Strathman (2006). “Using Archived
AVL-APC Data to Improve Transit Performance and
Management. TCRP Report 113." 
Transportation Research
Board
.
National Transit Database Sampling Manual (2009)
"Sampling Tests Automatic Passenger Counters." 
The Inside
Lane
. 26 Sept. 2011.
Boyle, D (2008). "TCRP Synthesis 77: Passenger Counting
Systems.” TRB, National Research Council, Washington, D. C.
 
 
 
 
 
 
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Dive into the world of transit data, where understanding different types such as system-level, route-level, and trip-level data can help in optimizing service quality. Discover the importance of accurate data in making informed decisions for transit systems, from service planning to operational control. Developed by K. Watkins, J. LaMondia, and C. Brakewood.

  • Transit data
  • Service improvement
  • Data types
  • Decision-making
  • Service planning

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  1. Unit 2: Describing Transit Systems with Data Data Types & Sources Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  2. Outline Types of transit data Manual data collection Automated data collection Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  3. We need to quantify and characterize our systems with different TYPES OF TRANSIT DATA Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  4. Service Is Only As Good As Your Data We need accurate data to make appropriate decisions for transit service. What if we were considering adding a new line to a transit system? What information would we need to know to make appropriate design decisions? Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  5. What Data is Needed to Maintain Good Service? 7 Demands of Useful Service It gives me freedom (to change my plans). It takes me where I want to go. It takes me when I want to go. It is a good use of my time. It is a good use of my money. It I can trust it. respects me. Stops/ Stations Speed or Delay Frequency Span Fares Civility Reliability Simplicity /Presentation Connectivity How Transit Services Them Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  6. Three Main Types of Data System Level Data Entire operational overview Useful for long-range planning, comparing service to similar regions Route Level Data Characteristics of specific routes Useful for long-range planning, service planning, route optimization Trip Level Data Specific details of trips made on transit system Useful for service planning, route optimization Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  7. Service Quality Improvement Cycles Service Plan Analyze Performance and Demand Operational Control & Passenger Info Transit Operation Archive Data Real- time loop Automated Data Gathering Off-line loop Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  8. System Level: National Transit Database (NTD) Established by Congress (Title 49 USC 5335a) Primary source for statistics on transit in US FTA grantees required to submit data >660 providers currently report to NTD Data used to apportion > $5 billion FTA funds Freely and publically available Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  9. 2011 National Transit Profile Does this look familiar? Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  10. What is in the NTD? Modules include: Safety and Security Financials Capital funds, operating expenses, operator wages Assets Stations and maintenance facilities, transit way mileage, revenue vehicles Resources Number of employees, energy consumption Transit Service (Supplied and Consumed) Vehicle revenue miles, Vehicle revenue hours, unlinked passenger trips, passenger miles traveled Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  11. NTD Glossary (Expenses) Capital Expenses = purchase of equipment Useful life > one year and Greater than $5,000 Includes Facilities (Guideway, Passenger Stations, Administration Buildings, Maintenance), Rolling Stock (vehicles), Other Equipment Operating Expenses = operation of the agency Function (Vehicle Operations, Vehicle Maintenance, Non- Vehicle Maintenance, General Administration) Object Class (Salary&Wages+Fringe= Compensation, Services , Materials and Supplies, Utilities, Casualty and Liability, Purchased Transportation, Other) Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  12. NTD Glossary (Service Consumed) Unlinked Passenger Trips = boardings Passenger Miles = cumulative sum of the distances ridden by each passenger Average Trip Length = avg distance ridden for = passenger miles / unlinked passenger trips Average Passenger Load = avg # pass aboard a vehicle at any one time Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  13. NTD Glossary (Service Supplied) Average Speed = miles / hours in revenue service Revenue Service = operation when passengers can board and ride on the vehicle Vehicle Total Miles = all miles from pull out to pull in, including "deadhead" Vehicle Revenue Miles = miles in revenue service Vehicle Total Hours = hours from pull out to pull in, including "deadhead" Vehicle Revenue Hours = hours in revenue service Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  14. NTD Glossary (Service Supplied) Revenue Vehicle = vehicle in fleet available to operate in revenue service including spares and out for maintenance Vehicles Available for Maximum Service = vehicles agency has available to operate revenue Vehicles Operated Maximum Service = largest # vehicles operated at any one time Base Period Requirement = vehicles needed to serve the base (all- day) transit service Peak-to-Base Ratio = Max Service / Base Period Percent Spares = (Veh Available Veh Operated) / Veh Operated Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  15. NTD Performance Measures Service Efficiency Operating Expense per Vehicle Revenue Mile Operating Expense per Vehicle Revenue Hour Service Effectiveness Operating Expense per Passenger Mile Operating Expense per Unlinked Passenger Trip Service Effectiveness Unlinked Passenger Trips per Vehicle Revenue Mile Unlinked Passenger Trips per Vehicle Revenue Hour Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  16. Head to www.ntdprogram.gov to complete the IN-CLASS EXERCISE Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  17. Route and Trip Levels Are Similar We describe unlinked passenger trips with Route Stop Locations Route Scheduling & Efficiency Volumes of Passengers Access/ Egress Locations Travel and Boarding Times Trip Purposes Exact Characteristics Depend on Application Two ways to collect this detailed data Manually Automatically Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  18. Engineers can tailor route and trip analyses through MANUAL DATA COLLECTION Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  19. Manual Procedure Considerations Instruments: Pencil/paper Hand-held units Route Selection: 100 percent Sample Type of Checks : Ride check: on-board the vehicle Point checks: at a specific location Surveys also used (at trip-level) Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  20. Ride Checks Captures an entire route in detail Data collection is done on-board Checkers counts the number of boardings, alightings, & thru-passengers at each stop Distance between each stop may also be recorded Output: Route-level & Stop-level ridership data Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  21. Ride Check Data Sheet - Example Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  22. Ride Check - Basic Calculations 1. Total Unlinked Passenger Trips on the Route = Sum of all Boardings 2. Load Profile for the Route = Cumulative Boardings @ Stop Cumulative Alightings @ Stop Peak (maximum) load along the route Average load along the route 3. Passenger Miles Traveled on the Route = Sum of Stop-Level Load * Distance between Stops Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  23. Example Load Profile Bus Route Load Profile 50 Peak Load Point: 47 Pax 45 40 35 30 Passenger Load Average Load: 24 Pax Load per Stop 25 Average Load 20 15 10 5 0 Stop #1 Stop #10 Stop #20 Stop #30 Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  24. Point Checks Data collection is done at one (or more) stops along a route Number of passengers on the vehicle is recorded Often conducted at high (peak) load points along a route Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  25. Surveys Travel surveys are used to estimate complete origin-destination patterns Questions include boarding location, alighting location, transfer location More next lecture Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  26. Transit operators are increasingly relying on new technologies AUTOMATED DATA COLLECTION Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  27. Automated Data Sources 1. Automated Fare Collection (AFC) 2. Automated Passenger Counters (APC) 3. Automated Vehicle Location (AVL) Archived data from AFC and AVL systems is an important byproduct of installing these systems. Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  28. Automated Fare Collection (AFC) Magnetic stripe & smart cards Smart card systems have unique ID that provides entry (exit) information at the individual level Data not available in real-time (coming soon!) Magnetic stripe and smart cards together can produce station/stop level data Rail gateline counts Bus fare boxes (operator often punches a key indicating other fare types, such as free passes) Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  29. Smart Card Example: OD Estimation AM Inbound trip: tap-in at origin PM Outbound trip: tap-in at destination Infer that passenger is traveling from Brookhaven to Midtown and back AM Tap at Brookhaven PM Tap at Midtown Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  30. Automated Passenger Counters (APC) Sensors near vehicle doors count passenger on and offs, usually using infrared beams Some vehicles also determine weight/steps on-board vehicle Typically data not available in real-time Usually only on a sample (%) of bus fleet; redistribute buses to determine counts Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  31. Automated Vehicle Location (AVL) Originally, Signpost-beacon-based used to track the location of buses Post-2000, GPS-based technology Provided in real-time Usually matched with schedule through Computer Aided Dispatch (CAD) systems Also combined with other data sources (AFC & APC) to capture detailed ridership Valuable for service planning (i.e. determining running times, schedule adherence) Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  32. Signpost-based AVL Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  33. GPS-based AVL Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  34. Combining AVL & APC Detailed Route Level Analysis Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  35. DATA COLLECTION COMPARISON Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  36. Manual vs. Automated Data Manual Data Low capital costs High marginal (labor) costs Small sample sizes Limited spatial and temporal variation Often unreliable (errors by checkers) Longer data collection/processing times Automated Data High capital costs Low marginal (labor) costs Large sample sizes Detailed temporal and spatial data Biases can (usually) be corrected Available in (quasi) real-time Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  37. Passenger Counting Technologies (2008) Technology / Procedure # Systems Percentage Combination of manual and automatic 44 51.2% Manual (paper and pencil) only 18 20.9% APCs only 12 14.0% Other automated methods (farebox, hand-held units) 12 14.0% Total systems 33 100.0% Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  38. Future Trends Increasing use of automated data collection systems Including mixed modes (manual & automated) More disaggregate data used for planning and decision-making (refined units of analysis) Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  39. DATA STANDARDS Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  40. Transit Data Consumption The changing landscape Digitization Paper Schedules Interactivity Schedule 10 9:36 Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  41. General Transit Feed Specification shapes.txt routes.txt trips.txt agency.txt GTFS stops.txt calendar.txt stop_times.txt Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  42. How Does Open Data Help? Data access models Transit Agency Agency produces data and opens it once. DATA Agency responds to special requests by developers DATA Anyone can access data App Developers Small subset of riders find this specific tool useful. Riders Many riders access a diverse market of tools powered by GTFS. Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  43. Transit Open Data Timeline Source: Rojas, Francisca (2012) Transit Transparency: Effective Disclosure through Open Data Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  44. Open Schedule Data (GTFS) Adoption Source: Wong, James. (2013). Leveraging the General Transit Feed Specification (GTFS) for Efficient Transit Analysis. Proceedings of the 2013 Transportation Research Board Annual Meeting. 44 Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  45. Conclusions We need accurate data to make appropriate decisions for transit service. The National Transit Database (NTD) is the largest source of transit data in the US. Manual data collection consists in recording transit information in person. Automatic data collection uses captors in buses to record vehicle location, vehicle loads, etc. Future trend of opening up data based on standardized formats. Materials developed by K. Watkins, J. LaMondia and C. Brakewood

  46. Reference Materials in this lecture were taken from: Walker, J. (2011). Human transit: How clearer thinking about public transit can enrich our communities and our lives. Island Press. Furth, Hemily, Muller, Strathman (2006). Using Archived AVL-APC Data to Improve Transit Performance and Management. TCRP Report 113." Transportation Research Board. National Transit Database Sampling Manual (2009) "Sampling Tests Automatic Passenger Counters." The Inside Lane. 26 Sept. 2011. Boyle, D (2008). "TCRP Synthesis 77: Passenger Counting Systems. TRB, National Research Council, Washington, D. C. Materials developed by K. Watkins, J. LaMondia and C. Brakewood

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