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Public Transit ITS Data Collection and Analysis: Large- and Small-Agency Lessons Learned

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Slide 1: Title Slide

Public Transit ITS Data Collection and Analysis: Large- and Small-Agency Lessons Learned

Talking Technology and Transportation (T3) Presentation

June 20, 2007

Slide 2: Today's Speakers

Host:
Charlene Wilder
Office of Mobility Innovation
Federal Transit Administration

Presenters:
Thomas Guggisberg
David Gehrs
Capital District Transportation Authority (CDTA)
Albany, NY

Michael Haynes
Chicago Transit Authority (CTA)
Chicago, IL

Slide 3: Disclaimer

This presentation contains references to brand names and proprietary technologies. This information is provided in the specific descriptions of ITS applications at the presenting agencies, and does not in any way constitute an endorsement of those brands or technologies by US DOT.

Slide 4: ITS Peer-to-Peer Program

• Sponsored by the US DOT's ITS Joint Program Office, in cooperation with ITS America
• Provides short-term technical assistance on ITS planning, procurement, deployment, and operational challenges
• Connects agencies with an existing base of ITS knowledge and expertise within the transportation community
• AKA "P2P"

Slide 5: How the P2P Visit Came About

• CDTA contacted the ITS P2P Program about increasing its understanding of how another transit agency uses data for service planning
• ITS P2P agreed to support two CDTA staff members' travel for a site visit with Chicago Transit
• The two agencies produced a report detailing outcomes

Slide 6: The Purpose of the P2P Visit

To share experiences and improve ...

• ... the processes behind managing data and disseminating information
• ... data collection and analysis techniques
• ... large-scale ITS project deployments
• ... how we "operationalize" ITS and data
• ... how we make better use of service standards
• ... relationships with internal/external stakeholders
• ... the challenges to both large and small agencies

Slide 7: CTA & CDTA Side-by-Side

CTA	CDTA
$1 billion operating budget.(FY2006) 10k+ employees Bus fleet of over 2,000 vehicles serving Chicago and 40 suburban communities Over 4 million people live in service area 154 regular routes 1.55 million daily boardings (0.95m bus 0.60m rail) 2,530 miles of bus routes, 224 miles of rail lines	$64 mil. operating budget (FY2007-2008) 500+ employees Bus fleet of 250 vehicles serving a 4-county service area that encompasses over 2300 square miles Over 750,000 people live in the service area 44 regular routes 35,000 daily boardings 400 one-way paratransit trips daily

Slide 8: Presentation Outline

1. Overview of ITS/Data Experiences at CDTA and CTA
2. Analysis Challenges
3. New Data Analysis and ITS Projects
4. Summary of Findings from the P2P Visit
5. P2P and Speaker Contact Information

Slide 9: 1. Overview of ITS/Data Experiences at CDTA and CTA

Slide 10: Overview of ITS

Mobile Data Communications System
Fully integrated ITS Solution

• CAD/AVL = Computer Aided Dispatch/Automatic Vehicle Location GPS
• Voice and Data Communication
• Silent Alarms, Including Emergency Button
• On-board MDT = Mobile Data Terminal (Co-pilot)
• On-board Next-Stop Announcements and Display
• Real-time Passenger Information at Stops/SMS
• TSP = Transit Signal Priority
• APC = Automatic Passenger Counting
• Supervisory Schedule Interrogation
• Web/Maintenance/Scheduling System Interfaces
• Statistical Reporting — Data Capture — Incident Reporting

Slide 11: Overview of ITS

[This slide is a flow chart displaying an overview of ITS as it applies to the Capital District Transportation Authority's (CDTA) transit operations. The Transmission Control Protocol/Internet Protocol suite (TCP/IP), consisting of a Computer Aided Dispatch/Automatic Vehicle Locater (CAD/AVL) server; dispatch workstations; and supporting workstations, is shown communicating with the internet and CDTA's initialization program. The TCP/IP is connected to both a Variable Message Sign, displaying passenger information, as well as a data radio interface. The data radio interface is shown sending information to a radio which then sends the information on to a radio station tower. The radio station tower is shown transmitting information to and receiving transmissions from the transit fleet. At the same time, the transit fleet is sending signals to a global positioning system (GPS) which is sending transmissions back to the fleet. The fleet is shown sending a transmission to a transit priority device, which has interface to a intersection controller. ]

Slide 12: Overview of ITS

[This slide is a continuation of the overview of ITS as it applies to CDTA's transit operations. The top portion of the slide displays a connection between the wireless LAN, using the 802.11b standard, and a Low Impedance Stereo Audio (LiSA) receiver and a LiSA decoder & control unit, both with interface to a intersection controller. Also shown is a connection between a set of mobile radios and the wireless LAN, again using the 802.11b standard. The bottom portion shows the inside of a transit bus and the functions of the onboard computer, including: passenger counter, odometer, on board display, destination board, door contact, emergency switch, external loudspeaker, and covert mic & voice radio.]

Slide 13: ITS Data — Goals & Objectives

• Convert data to useful information to support operating and marketing decisions
• Provide data of the right quality, detail, relevance and timeliness to appropriate staff
• Assist staff in using data to drive decisions

Slide 14: Data Sources

• Farebox data — record for each customer boarding
• HASTUS (scheduling) — record for each trip, timepoint
• INIT (AVL, APC) — record for each stop (AVL) and each customer boarding (APC)

Slide 15: Data Integration

[This slide, titled data integration, shows a Venn Diagram displaying the relationship between CDTA's initializing program Automatic Vehicle Location/Automatic Passenger Counter (AVL/APC), the Genfare farebox, and the HASTUS scheduling software.]

Slide 16: Data Integration Examples

• Farebox, AVL — location of all boardings
• Farebox, scheduling — boardings by trip
• Scheduling, AVL — on-time and running time
• Farebox, scheduling, AVL — diagnostic data route, segment, and time period

Slide 17: Overview of ITS Data

• CAD/AVL Incident Reporting — Crystal Reports
• Statistical Reporting — CAD/AVL
  • Example: Automatic Passenger Counting, event lists/logs, on-time performance, etc.
• Integration of Fare Collection/ Scheduling/AVL Data
  • Example: Trip-by-trip running times

Slide 18: CAD/AVL Incident Reporting

• Accident
• Bus shelter
• Daily capacity
• Driver problem
• Incident
• Mechanical
• Problem
• Radio check
• Service deviation
• Service performance
• Service protection

Slide 19: Example — Incident Reporting

[This slide shows CDTA's incident report form. The form is broken into two sections, one pertaining to the incident itself and the other regarding bus change information. In the incident section there are fields for: incident type, date, route, block, vehicle, time, operator ID, operator name, recorded by, dispatcher, weather code, location, and direction. In the bus change information section there are fields for: depot, replacement vehicle, replacement operator ID, block delay, service delay, in service time, trips lost, in service location, and defect.]

Slide 20: Statistical Reporting

• Passenger counts
• Driver log-ins
• Schedule adherence
• Alarms

Slide 21: Decision Examples

• Running times
  • Trip
  • Between timepoints
• Service frequency and span
• Route alignment
• Express/limited service
• Fare and fare products

Slide 22: Data Analysis from ITS Data

• Background & data flow
• Analysis architecture and development timeline
• Sample web-based reports
• Analysis methodologies & samples

Slide 23: ITS Project Background

• The Americans with Disabilities Act (ADA) drives need to automate stop announcements
• A desire for comprehensive performance data with declining resources drives data collection
  • Automated Passenger Counting (APC)
  • Automate running-time analysis (AVL data)
• Both systems require accurate geolocation of bus stops on board the bus from a navigation system (GPS/odometer)
• Integration of systems provides for efficient use of complementary resources

Slide 24: ITS Transit Data Integration

• System demands accurate data
• System produces very useful data

[This slide displays the data integration relationship between a bus and computer using arrows to show a cycle. Caption reads "Good data from buses requires good data sent to buses." This is a classic slide in the deployment of transit ITS. The systems demand very accurate route and schedule data in order to produce useful operational and planning data. ]

Slide 25: Bus-Side Integration Components

[The first photo is a birds eye view of a transit bus, with the GPS and wireless LAN units (located on the top of the bus) being called out. An arrow connects this photo to the next photo of a Intelligent Vehicle Network-II (IVN-II), which has a call-out box next to it reading destination signs. The next three photos, also connected the photo of the IVN-II, are of: an odometer, a transit control head (TCH) operator log, and a automatic passenger counter (APC) sensor.]

Slide 26: AVAS Data Flow

[This slide displays a flow chart of CTA's Automated Voice Annunciation System (AVAS) data flow. The first box is of the HASTUS scheduling software sending schedule and route data to the bus tools workstation. The bus tools work station is also shown receiving schedule & route data from a GIS workstation which is connected to a field data collection unit — A van with GPS. From the bus tools workstation, schedule and route data is sent to the BusLink server, which is shown sending the data, via a wireless link, to a Intelligent Vehicle Network (IVN) located inside a bus. The Bus is shown sending AVL/APC data back to the BusLink server which then sends the AVL/APC data to a data ingestor and then on to the UNIX Oracle database. The UNIX Oracle database is also shown receiving schedule and route data directly from the bus tools workstation. From the Unix Oracle database the AVL/APC data is sent to the intranet web server which sends the AVL/APC data to and receives it from a user work station.]

Slide 27: AVAS Data Development Timeline

Sep 2002	2003	2003	Jan 2004	2004	May–Sep 2004
AVAS installation started	AVAS rollout to fleet — data evaluation	Initial data exploration and quality control development	Start of migration of data to larger database	Development of AVAS / APC web-based reporting tools	Development of data warehouse methods

Jan–Apr 2005	May–Dec 2005	Mar–Sep 2006	Sep–Dec 2006	Feb–Apr 2007	Today
Continued development data exploration tools for Planning	Running time analysis development	Academic methods for headway metrics.(intern work)	Headway metrics analysis for system-wide reporting	Bus bunching charting tool & Headway metrics development	Ongoing development

Slide 28: AVAS Data Architecture

[The slide here represents the data flow of data from the buses to the BUS_STATE holding table which is processed twice daily into a Terminal Departures report, AVAS Data Quality report and associated web sites. The data are also processed into a vendor supplied APC reporting solution for APC analysis. We had a vendor help us build a data warehouse for the raw bus event data and we subsequently built a run time and headway monthly rollup analysis process against that data. Probably the easiest way users tap into the raw AVL data is with the AVAS web site a suite of pages (CLICK) that allow users to look up one bus for an hour or the history of an operator or a stop. I have some examples of these reports on the next few slides. We can return to this slide but keep in mind that all the data are processed twice a day in a two hour rollup into many applications and in some cases the raw data are processed monthly to produce data which drive other web pages or Excel reporting. ]

Slide 29: Web-Based Data Exploration

• Terminal departure performance (BLIS)
• Maintenance status & system performance history
• Daily route history
• Daily or hourly bus history
• Stop-level history
• Monthly bus use by garage and type
• Trip/route summaries from Ridecheck Plus
• Max load and route profiles (from RCP data)
• Run-time analysis to build better schedules

Slide 30: AVAS Bus Full Day History

[This slide displays a AVAS Bus Full Day History Report of a single bus for a day. We can see the start times of a the trips and the runs and operators who used this vehicle as well as what trips were run. ]

Slide 31: AVAS Bus Stop Log

[This slide displays a half-hour history from one bus; we can see the stops as well as if we stopped there or drove past without stopping. Unknown stops are also identified; if the bus is equipped with APC (and this one is) we are provided a raw count of the passenger movements.]

Slide 32: Quick Historical Map (Google Map)

[This slide displays the history of the bus plotted along a route on a map provided by Google. Stops along the route are color coded according to different events.]

Slide 33: AVAS Data for Performance Metrics

• Background on AVAS data processing
• Complexity of data analysis
  • Volume of data (3.2m records per day, 17GB per month)
  • System-wide metrics from automated data sources are not yet refined to state-of-the-practice methods, as no official standards define how to turn AVL data into simple metrics
• Presentation issues
  • Turning raw data into information is a challenge, especially to present results effectively
  • Effective presentation tools including web pages and map-based technologies are time-consuming to develop
• Bottom line
  • Development of a meaningful metric is not a trivial task
  • We are reaching out to the academic community as well as developing a strong back-end data structure to support a multitude of analysis and presentation methodologies

Slide 34: Analysis Methods

• Terminal Departure On-Time Performance
  • BLIS (reports on manual mode use)
  • Two weeks in scope
  • Easy join to schedule
• Run-Time
  • Monthly
  • Complex SQL and aggregation to obtain segment/route run times
• Headway & On-Time
  • Monthly
  • Complex SQL to obtain bus-bus time intervals and metrics
  • Deals with manual mode data
  • Still in development

Slide 35: Sample Run-Time Output

[This slide shows a scatter plot of run time observations taken from the Run Time Analysis web page. The X axis is titled "Hour of Day (24 hour time);" the Y axis is titled "Trip Travel Time (minutes)." The slide shows Route X49, the Western Express (limited stops) southbound from Berwyn to 97th street from September to November 2005. The chart shows the variability of run times between the midday and afternoon peak periods as a challenge to providing reliable service, with the PM peak showing the greatest variability in travel time.]

Slide 36: Headway Analysis

• Bus bunching
  • Buses on same route arriving within one minute of each other
  • Easy to analyze and compute from headway data
• Long gaps
  • Using New York City methodology.(http://www.mta.info/mta/ind-perform/month/nyct-b-wait.htm)
    • Headway plus 3 or 5 minutes for peak or midday, respectively
    • CTA's automated system has 300 times more data with 1/10th of the manpower!
• Broken AVAS can lead to gaps that are not really present; results are adjusted to compensate

Slide 37: Percent of Observed Bus Intervals 60-Seconds or Less for Sept – December 2006

[This slide shows the percent of observed bus intervals that are less than 60-seconds apart (bunched) for four months from September, 2006 to December 2006 with the day on the X-axis. The Y-axis is the time of day in half hour increments. The color at the intersections are the percent of bus intervals bunched. Concentration of deep red indicates between 8 and 10% bunched; blue is between 2-4% bunched. Events can be traced to bunching. Friday afternoon's are more likely bunched and the effect of school going back into session or out of session for the December holiday's is apparent. The impact of a snowstorm on bunching is apparent on the morning of December 1, 2006? Another concentration of red indicates bunching that took place due to a fire that took out the Orange Line and a derailment that happened near the same location a month later. So we can see the interaction between the rail system and bus bunching.]

Slide 38: 2. Analysis Challenges

Slide 39: Challenges & Suggestions (CTA)

• ITS deployment involves so many departments
• ITS data analysis is needed in both operations and planning
• Reports from vendor applications do not often meet needs
• Retaining analytical IT staff is essential to development process
• Establish cross-agency support for deployment
• Consolidate IT resources to facilitate analysis agency-wide
• Develop in-house data warehouse and reporting (use external resources for clearly defined projects)

Slide 40: Challenges & Suggestions (CDATA)

• Defining benefits in the form of useful information or reporting tools — defining reporting output
• Maintaining systems hardware and data to support this task
• Soliciting support from both management and operations staff for continued use of ITS tools over time
• Assist staff in using data to drive decisions
• Project management delays
• Provide data of the right quality, detail, relevance and timeliness to appropriate staff
• Set clear expectations between vendor and agency to supporting systems
• Convert data to useful information to support operating and marketing decisions
• Provide easy to use tools and access to information
• Well defined project plans, deliverables and project teams

Slide 41: 3. New Data Analysis and ITS Projects

Slide 42: New ITS Data Analysis Initiatives

• Headway / On-time / Trips Completed
  • Working on developing more reports from headway, run-time and the raw data store to compute meaningful metrics
  • Looking to use data to both improve operations through planning and report on operations
• Service Standards
  • Working to use processed APC data to apply to service standards find discrepancies
  • Effort is now reaching a more mature phase as we have renewed staff interest
• Route-by-Stop Analysis
  • Using AFC data to scale up APC data to find stops with the highest passenger activity
  • Data presented using GIS to help identify the most important stops

Slide 43: New ITS Data Analysis Initiatives

• Test trial on Routes 1, 10, 80, and 85
• Graphics (map) output
• Common data interface
• Common user interface
• User-defined reporting
• Simple data transfer from other systems
• Staff training

Slide 44: New ITS Initiatives

• Bus Time — Real-time next-bus predictions
  • Communications integration of a mobile access router with cellular card and existing on-board ITS (AVAS)
  • Web-based bus predictions currently piloted on one route
• TSP — Transit Signal Priority
  • Working to integrate with existing on-board ITS
  • Pilot expected by end of 2007

Slide 45: New ITS Initiatives

• Mobile Data Communications System Project Completion
• Fare Collection/AVL/Scheduling System Data — Web Portal
• Information Management Study
• Trip Planner — Web
• Real-Time Information Signs
• Enterprise Web Portal
• Transportation Development Plan
• Bus Rapid Transit — BRT

Slide 46: 4. Summary of Findings from the Peer-to-Peer Visit

Slide 47: Summary of Findings

• Small & large agencies have the same problems
  • Operations "buy-in"
  • Project deployment Issues are the same
  • Dedicated staffing for "new" technologies is necessary for "success"
  • Challenge of coordinating needs of Planning, Marketing, Operations
• Project tips
  • Next-stop arrival information - include route and bus number
  • Dedicated vehicle for maintaining real-world data
  • Use fellow agencies experiences to eliminate unnecessary project delays

Slide 48: 5. P2P and Speaker Contact Information

Slide 49: ITS Peer-to-Peer Program

To inquire about utilizing the ITS Peer-to-Peer Program:
Call 1-888-700-PEER (1-888-700-7337)
E-mail p2p@volpe.dot.gov
Program Contacts:
Terry Regan
US DOT Volpe Center
Ron Giguere
ITS Joint Program Office

To learn more, visit www.pcb.its.dot.gov/res_peer.asp

Slide 50: Speaker Contact Information

[CDATA's logo appears.]	Capital District Transportation Authority Thomas Guggisberg — Director of Information Technology thomas@cdta.org — 518-437-8326 David Gehrs — Planner/Analyst DavidG@cdta.org — 518-437-6853
[CTA's logo appears.]	Chicago Transit Authority Michael Haynes — Project Manager michael.haynes@transitchicago.com — 312-681-3619
[FTA's logo appears.]	FTA Office of Mobility Innovation Charlene Wilder — ITS Program Manager charlene.wilder@fta.dot.gov

Slide 51: Questions?

[Slide is titled "Questions." This slide has two photographs, one of a CDTA bus and the other of a CTA bus.]