Within these pages, readers will find information and resources on transit data, with a particular focus on the ITS transit data sharing, integration, and management needs of transit agencies. Better integration and management of ITS transit data could improve the use of these data by transit agencies to support transit planning and day-to-day operations.

The transit industry has enormous volumes of data applicable to many different facets of the business. Transit data apply to core aspects for transit monitoring (stops, vehicles, connections, and facilities) and static transit schedules/timetables. They also apply to a range of business systems including spatial data, fare systems, ridership, customer service, information systems, asset management, operators, security, revenue, parking, and payroll. 

Data from transit intelligent transportation systems (ITS) is also a valuable resource for transit service planning and operations. In particular, vehicle location and passenger activity data from automatic vehicle location (AVL), automatic passenger counter (APC), and automatic fare collection (AFC) systems can be used to provide essential insights into transit operations. They can also inform decision-making to increase the efficiency, productivity, and safety of transit service. There are, however, significant challenges for transit agencies in accessing and using this data. Many agencies cannot get to the data at all or do not understand the data they have. Data validation and quality control, integration and matching across various data sets, and aggregating data are all hard tasks. Additionally, there are issues developing the types of reports, tools, and analytics that contribute to informed decision making. Even when transit agencies, researchers, and consultants do address these challenges, they often have difficulty sharing their work with their peers in the industry because the same types of data may be managed differently among transit agencies. The result is that transit ITS data is rarely used to its full benefit. 1

Additional Resources:

The Metrolinx (Toronto) 2041 Regional Transportation Plan (RTP) 

This presentation recommends the development of a regional transportation big data strategy: creating a regional transportation big data portal, providing consistent and transparent data collection, management, and reporting. Additionally discusses establishing regional standards for transportation data sourcing, formatting, privacy, security, ownership and reporting.

Service Interface for Real Time Information (SIRI)

SIRI is an extensible and modular standard allowing for C2C communications, agency-to-public communications, and agency-to-infrastructure communications. SIRI allows for the structured exchange of real-time information about schedules, vehicles, and connections with general informational messages related to the operation of the services. SIRI defines functional services including:

Transit Communications Interface Profiles (TCIP)

TCIP is an extensible and modular standard allowing for data exchange between transit business systems, subsystems, components, and devices. It covers C2C, agency-to-public, agency-to-infrastructure, and system-to-system communications. The benefits extend beyond vehicles, operations, and passenger information to business systems and subsystems noted above.

General Transit Feed Specification (GTFS)

GTFS defines a common format for public transportation schedules and associated geographic information. GTFS "feeds" let public transit agencies publish their transit data and developers write applications that consume the data in an interoperable way. The initial and main benefit of this standard included a free online trip planner available to the public to look up transit information and plan transit trips.


GTFS-ride is an open, fixed-route transit ridership data standard developed through a partnership between the Oregon Department of Transportation and Oregon State University. It allows for improved ridership data collection, storing, sharing, reporting, and analysis.