Turning Big Data into the Big Picture

Pete CompsConduent, Director, Business Development

Chicago, IL

Xavier DefrenneConduent, Director, Fare Collection

Atlanta, GA

Traditional Reports

• Tabular− Excel

− Business Intelligence tools

− Tables, rows and columns

− Occasional graph

• Geographic Information Systems (GIS)− Require geo-located data (e.g., stop location)

− Typically produce static images

− Limited use

Typical Sources of Transit “Big Data”

• Fleet Management (CAD/AVL) systems

• Automatic Passenger Counters

• Fare collection systems

• GTFS

• Maintenance Management Systems

• Mobility Enablers− TNC (Uber, Lyft)

− Scooters (Lime, etc.)

− Bikeshare

Challenge:

Inconsistent content and format

Data Consolidation

• Use common data elements (e.g., timestamps) to rationalize data from multiple sources

• Build a “data warehouse” that provides a ready source of consolidated data

• Automate data mining process so consolidated data is always current

MAP

Fare Collection

Passenger Counter

GTFS

Maintenance Management

TNC (Uber, Lyft)Scooters

Bikeshare

CAD / AVL

Mobility Analytics Platform (MAP)

MAP helps with fact-based decision making and planning

• “Big Data” mining and consolidation

• Easily defined query parameters

• Analysis using AI

• Visualization− Geographic

− Interactive

− 4th Dimension with scenario playing (am-pm, hour per hour, vehicle progression)

• Simulation

MAP Views

MAP Views

MAP Views

MAP Views

Video

• Example with Houston

• 4 Views− Validations across the network

− Vehicle load

− Schedule adherence (early/late)

− Origin/Destination

Vehicle Load

4

Vehicle Load

Observation of real stop times

(Tap on validations)

Alignment of real / theoretical journey

Passenger alighting forecasting

Inference of vehicle load

3

2

4

1

4

Key Presentation Take-Aways

• “Big Data” mining and consolidation allows comprehensive analytics

• Interactive visualization enhances analysis− Geographic

− 4th dimension animation