Introducing the GTFS-realtime Validator

Future work will focus on enhancing the analysis tool to automate data collection for a large number of feeds over an extended time period.Most feeds had 5 or fewer types of errors, but many occurrences of those errorsNext stepsBased on our experience deploying a GTFS-rt feed and multimodal transit app, as well as the development and testing of the GTFS-realtime Validator tool, the transit industry must focus on real-time data quality as well as data availability..The number of errors and warnings found in industry feeds reflect significant data issues that impact riders and, based on research, leads to reduced ridership and satisfaction with the transit agency and its service..Real-time data that contains integrity issues (e.g., trips with out-of-sequence predictions or conflicts with GTFS data) are very problematic for transit apps to parse; many transit apps, including Google Maps, the Transit App, and OneBusAway, will drop all predictions for that trip, resulting in users seeing the schedule information instead of real-time information.The good news, however, is that research shows good quality real-time data leads to increased ridership and satisfaction with the agency..Transit agencies can focus on improving data quality by getting involved with the GTFS-rt improvement process and voting for proposals that clarify how producers and consumers should interact..Agency can also use the GTFS-realtime Validator tool when creating and maintaining GTFS and GTFS-rt feeds to ensure that no errors and warnings occur, and require that their AVL vendor (including during the Request for Proposals process) also use such a validation tool before feeds will be accepted..Feed creators such as AVL vendors can use the validator in their own product development lifecycle to shorten quality assurance testing time and improve the quality of the data.As mentioned earlier, it should be noted that as of November 2017, the GTFS-realtime Validator tool does not detect errors in the predictions themselves (i.e., whether a vehicle actually arrived or departed when it was predicted), which is another significant source of problems encountered by riders..Future work should examine adding prediction accuracy analysis to the GTFS-realtime Validator, perhaps via integration with other tools such as TheTransitClock..Future work can also focus on enhancing the automated analysis tool to increase both the number and duration of feeds evaluated.Feedback is welcome!.Please let us know your thoughts in the comments below..Happy data wrangling!AcknowledgementsOur work at the Center for Urban Transportation (CUTR) at the University of South Florida (USF) on the development of the open-source GTFS-realtime Validator has been funded by the National Institute for Transportation and Communities (NITC)..The contents of this article reflect the views of the authors, who are solely responsible for the facts and the accuracy of the material and information presented herein.This article is an abbreviated version of Transportation Research Board 2018 paper 18–05585 “Quality Control — Lessons Learned from the Deployment and Evaluation of GTFS-realtime Feeds”.References[1] Kari Edison Watkins, Brian Ferris, Alan Borning, G..Scott Rutherford, and David Layton (2011), “Where Is My Bus?. More details

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