Enhancing transit data quality in Florida with AI‑powered solutions

As part of a regional Smart Community initiative in Florida, transportation agencies sought to improve multi-modal trip planning and routing services by standardizing transportation datasets into a format that is both readily accessible and easily consumable.

A key challenge was managing inconsistent and complex transit data from multiple agencies, where unstructured data, varying formats, and discrepancies made it difficult to integrate real-time and static transit feeds into a single, reliable system. The objectives of this effort included:

• Maintaining the functionality of existing investments in routing software technology.
• Improving the quality of General Transit Feed Specification (GTFS) and GTFS Real-Time (GTFS-RT) data sourced from different transit agencies.
• Providing accurate service alerts to enhance trip planning for end users.
• Ensuring reliable journey data by improving both static and real-time transit information.

Like many transit agencies, Florida’s transportation network receives data feeds from multiple sources, often with unique systems and formats, leading to inconsistencies. Previous efforts to enhance data quality had been ad hoc, but a more systematic, structured approach was required to ensure accuracy, reduce processing time, and enhance passenger experience.

To tackle these challenges, Ito provided data aggregation and validation services, leveraging its expertise in delivering high-quality public transit data for global journey planners. This solution took a systematic, forensic, and dynamic approach to data quality management, ensuring passengers had access to the most accurate and reliable information possible.

Using a data-as-a-managed-service model, the approach included:
• A blend of innovative technology, systems, and processes to manage and enhance transit data quality.
• Over 100 data quality checks to detect and correct discrepancies in stops, routes, schedules, and trip timings.
• Automated and manual refinements, ranging from simple adjustments like stop name corrections to more complex trip re-timing and data validation.

The data was aggregated from three key transit agencies in Florida, each with distinct data formats and requirements:
• Go Lynx – A major transit provider serving Central Florida
• Votran – The public transportation system in Volusia County
• SunRail – A commuter rail network operating in Greater Orlando

By integrating openly available data sources, including custom formats, the system provided a single, standardized output that could be easily expanded to include additional agencies in the future.

In addition, a service alert management tool was deployed, allowing agencies to create and distribute real-time updates for planned and unplanned disruptions. These alerts were seamlessly distributed across social media, internal applications, and journey planning tools, ensuring passengers received timely and accurate information about service changes.

The project delivered a single, high-quality transit data feed, combining data from multiple agencies into a consistent, accurate, and fully integrated system. The outcomes included:
• Ongoing data monitoring and validation, ensuring continuous data quality improvements.
• Real-time service alerts to notify passengers about disruptions and changes, particularly during extreme weather, staffing shortages, or major events.
• Seamless integration with existing journey planning platforms, maximizing the value of prior technology investments.
• Enhanced passenger experience, with greater accuracy in stop locations, trip planning, and routing estimates.

This approach has allowed Florida’s transportation agencies to shift focus back to core activities—trip planning, service optimization, and network management—while ensuring they are using the most up-to-date and accurate transit data.

Key data quality improvements

Enhancements across the transit network

Stop locations & merging – Over one-third of journeys benefited from adjustments to stop locations, improving routing accuracy and ensuring passengers wait at the correct stop.
Headsigns & route clarity – Updates to vehicle headsigns improved the clarity of service routes, reducing passenger confusion—especially on circular services.
Terminal loop corrections – Adjustments to terminal loop routing improved data accuracy, eliminating unnecessary trip extensions for riders.
Service & timing links – Duplicated service links (e.g., layovers) were streamlined, and unrealistic travel times between stops were corrected to ensure accurate journey planning.
Naming standardization – Consistent formatting was applied across datasets, improving data alignment between transit agencies. Over 6,500 text items were refined for clarity.

Through AI-powered automation, systematic data validation, and seamless integration, this initiative has set the foundation for a more connected, efficient, and passenger-friendly transit network in Florida.