An integrated suite of front-end tools and back-end services that empower decision making throughout the transit agency.
Transit ridership and performance have traditionally been measured with costly manual surveys, requiring substantial time and effort and therefore yielding small and infrequent samples.
Modern transit systems generate vast amounts of data, but proprietary and legacy fare-transaction and vehicle-location systems can be challenging to integrate for analytics.
Korbato offers a software platform built by transit experts using proven, cutting-edge research to harness these data to inform transit policy, planning, and operations.
Performance analytics gives insights into transit service delivery from the system perspective.
Combines vehicle-location, passenger-counter, network, and schedule data to meticulously clean and correct each vehicle’s stop-level history in space and time.
Powers a growing suite of tools for transit professionals.
Ridership analytics merges vehicle and fare data to reveal ridership and to view performance from the rider’s perspective.
Combines fare-transaction data with corrected vehicle data to reconstruct each anonymous cardholder’s daily travel history, including the inference of origins, destinations, and linked trips.
Powers a growing suite of tools for transit professionals.
More coverage for less cost. Harnessing vehicle-location and fare-transaction data reveals ridership and performance across all vehicles, dates, and times, yet at a fraction of the cost of infrequent manual surveys.
Optimized service and scheduling. By weighting excess journey time, waiting time, and crowding by the number and spatial distribution of riders experiencing them, vehicles and resources can be allocated more optimally and equitably.
Objective impact analysis. By comparing passenger behavior and journey attributes before and after service or network changes, or during outages or pilots, their benefits and costs can be quantified from the passenger’s perspective.
Seamless mobility. Observation of linked passenger journeys across modes and operators can reveal opportunities for and impacts of fare policy adjustments, transfer coordination, and other interventions to reduce barriers to regional travel.
Data integration. Data from disparate sources—from vehicle location to fare processing, from realtime feeds to agency and vendor databases—are combined and enhanced on Korbato’s secure cloud platform. The enriched, interoperable, privacy-protected data power the web tools and can be fed back to the transit agency for in-house analysis.
Continual innovation. Korbato works closely with transit agencies to identify and prioritize new features and improvements. As a cloud-based software service, the platform and its underlying algorithms are continually refined and expanded, at no additional cost to our customers.
In addition to providing the transit analytics platform, Korbato offers technical consulting services to the transit industry. Our team members have partnered with transit agencies and municipalities on four continents on projects ranging from IT modernization and data strategy to ridership modeling and real-time operations control.
For consulting inquiries, please contact us.
Korbato is founded by transit experts who have pioneered state-of-the-art inference and modeling algorithms as academic researchers and who have implemented those technologies at leading transit agencies.
Gabriel Sánchez Martínez co-founded Korbato to bring cutting-edge data analysis to transit agencies. He taught the graduate public transportation course at MIT for three years and has published research on agent-based simulation, real-time bus and rail operations control, and ridership modeling using automatically collected transit data. Prior to Korbato, Gabriel developed technology for the transit agencies of London, Boston, Chicago, and San Juan. He holds a PhD in transportation from MIT and a civil engineering degree from UPR Mayagüez.
Jay Gordon co-founded Korbato to make the research he developed at the MIT Transit Lab available to agencies of all sizes. Prior to Korbato he worked as a researcher and public servant for the transit agencies of London, Boston, and San Francisco. Jay has a background in software engineering with interests in data fusion, cartography, and data visualization. He holds masters degrees in transportation and urban planning from MIT and studied geography and planning at UC Berkeley.
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