April 4, 2022
Report-building and data analysis are essential for transit agencies to record and understand what’s happening on the ground with their systems, but often it’s just as important to understand what is not happening, and why it was missed. Operator shortages, weather conditions, and other unexpected events can result in missed or incomplete trips, which can be difficult for agencies to track.
Recently, Swiftly updated its On-Time Performance otpByStop CSV export and added new Run-Times CSV exports that support agencies that want a better way of reporting, measuring, and understanding missing service.
This is valuable for the following reasons:
The OTP CSV export has been updated so that it now includes scheduled stops even when no vehicle arrivals or departures were observed. With these additional data points, agencies can more easily calculate on time performance and missing service. So how does it work?
Download the On-Time Performance CSV export by All Stops for any queried period of time. (Note: Swiftly does not provide unobserved stop data for same-day data.) Cells under column V, “observed_time” remain blank if no vehicles were observed at this stop. Stops with unobserved times automatically appear at the top of the export to more easily identify and sort the data.
Additionally, this export includes columns (Q-T) to indicate if a Swiftly Service Adjustment was made. This helps customers better track and understand how unobserved stop data relates to service adjustments such as detours, stop closures, canceled trips, and modified departure times. Unobserved stop data can reflect other issues as well including AVL outages.
The Run-Times module has been updated with a new tripStats CSV export and a new tripObservation CSV export. Using the tripObservations CSV export, it is possible to investigate missing service. So how does it work?
First, the tripObservations CSV export can be downloaded from the Components tab in the Run-Times Analysis view. Column J, “num_complete_trips” indicates the number of trips where a complete data set of departure and arrival stop data was observed. Column H, “num_scheduled_trips” indicates the total number of scheduled trips for that time. Column J can be summed for the total number of complete trips which can then be subtracted or divided by the sum of total scheduled trips in Column H. In the image below, 24 trips were completed out of 28 scheduled trips, leaving 4 trips, or 14.2% of trips, missing.
Additionally, in order to understand exactly how many stops were observed for partially completed trips, the tripObservations CSV export can be downloaded from the Distribution Run-Times Analysis view. Using this export, users can refer to Column P, “isCompleteTrip.” If cells in this column are marked “TRUE” then the trip was completed, if it is marked “FALSE” then there is missing stop data. Columns N and Column O can be used to identify how many stops are missing.
This export also includes columns X and Y to indicate if a Swiftly Service Adjustment was made in order to help customers better track and understand how incomplete stop trips relate to service adjustments such as detours, stop closures, canceled trips, and modified departure times. Incomplete trips due to stops with missing arrival or departure data can reflect other issues as well including AVL outages.
As with any other Swiftly report, agencies have the ability to analyze the missing data in Excel or with data analytics programs such as PowerBI or Tableau. It also gives agencies the flexibility to summarize the data using their own definitions for missing trips such as whether or not extremely late trips are included and how partial trips are handled.
These updates are just a first step in helping agencies track missing service. Swiftly is continuously updating its platform to provide agencies with greater insight into when, where, and how their system is, and isn’t, performing.
See how Swiftly can improve service reliability, passenger information, and operational efficiency at your agency with an in-depth demo.