# Presentation: 702512

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

**Talking Technology and Transportation (T3) Presentation**
- June 20, 2007

## 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

## 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.

## 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”

## 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

## 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

## CTA & CDTA Side-by-Side

- CTA
- $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

- CDTA
- $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

## Presentation Outline

- Overview of ITS/Data Experiences at CDTA and CTA
- Analysis Challenges
- New Data Analysis and ITS Projects
- Summary of Findings from the P2P Visit
- P2P and Speaker Contact Information

**Overview of ITS/Data Experiences at CDTA and CTA**

## 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

## Overview of ITS

## Overview of ITS

## 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

## 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)

## Data Integration

## 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*

## 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

## CAD/AVL Incident Reporting

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

## Example – Incident Reporting  

## Statistical Reporting

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

## Decision Examples

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

## Data Analysis from ITS Data

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

## 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**

## ITS Transit Data Integration 

- System demands accurate data
- System produces very useful data

- Good data _**from**_ buses requires good data sent _**to**_ buses!

## Bus-Side Integration Components

- GPS
- Wireless LAN

- APC Sensors

- TCH – Operator Log-on

- IVN-II

- Destination Signs

## AVAS Data Flow

## AVAS Data Development Timeline

**Ongoing development**

## AVAS Data Architecture

## 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

## Slide 31

## Quick Historical Map (Google Map)

## 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

## 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

**Terminal / Timepoint Schedule Adherence** (BLIS)

**Run-Time Analysis**

**Headway & On-Time Analysis**

## Sample Run-Time Output

## 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](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

## Percent of Observed Bus Intervals 60-Seconds or Less for Sept – December 2006

- Percent of Observed Bus Intervals **60-Seconds **or Less for Sept – December 2006

## 2. Analysis Challenges

## Challenges & Suggestions 

- 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)

## Challenges & Suggestions

- 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

- 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

## New Data Analysis and ITS Projects

## 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

## 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

## 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

## 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

## Summary of Findings from the Peer-to-Peer Visit

## Summary of Findings  

- 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

## P2P and Speaker Contact Information

## 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](mailto:p2p@volpe.dot.gov)_
- Program Contacts:
- 		Terry Regan		Ron Giguere
- 		US DOT Volpe Center    ITS Joint Program Office
**To learn more, visit **www.pcb.its.dot.gov/res_peer.asp

## Speaker Contact Information

**	**_**Capital District Transportation Authority**_
**	****Thomas Guggisberg -** Director of Information Technology _[thomas@cdta.org](mailto:thomas@cdta.org)__ - 518-437-8326_
**	****David Gehrs -** Planner/Analyst
- 	_[DavidG@cdta.org](mailto:DavidG@cdta.org)__ - 518-437-6853_
**	**_**Chicago Transit Authority**_
**	****Michael Haynes -** Project Manager
- 	_[michael.haynes@transitchicago.com](mailto:michael.haynes@transitchicago.com)__ - 312-681-3619_
**	**_**FTA Office of Mobility Innovation**_
**	****Charlene Wilder –** ITS Program Manager
- 	_[charlene.wilder@fta.dot.gov](mailto:charlene.wilder@fta.dot.gov)__ - _

## Questions?