Muni Trips with Service Gaps

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San Francisco Municipal Transportation Agency


Target: 13%

Target: 15%
Result: 18.4% of Muni Trips with Service Gaps


The regularity of vehicle arrivals is the most important indicator of customer experience and reliability for the Muni system, which carries 720,000 average daily riders and is key to helping the City meet its economic and environmental goals. It enables the San Francisco Municipal Transportation Agency (SFMTA) to track its ability to deliver frequent, reliable service. Data on where service gaps in transit service occur also helps the SFMTA understand where potential transit priority projects will be most effective.

Most Muni passengers ride routes that are scheduled to run frequently, and as such are unlikely to consult a printed schedule when planning their transit trips. For frequent routes, what is most important for customer experience is that the time between buses and trains (“headway”) is regular and close to the headways in the schedule. For example, if every single trip on the 5-Fulton were exactly 6 minutes late, on-time performance would be 0 percent (not a single bus arrived at the scheduled time), but customer experience with wait times would be excellent (the time between buses was exactly as scheduled, meaning no one waited longer than the scheduled headway).

Measuring service gaps shows how Muni is performing on providing regular, reliable service. Regularity of vehicle arrivals has other ancillary benefits aside from minimizing passenger wait time. When buses are evenly spaced, crowding is also reduced, since gaps in service exacerbate crowding on the first bus at the end of a gap in service.


Note: In January 2019 there was a change in calculation methodology. The transition was from bunching and gaps of the Rapid Network (a subset of 11 Muni line) to service gaps of all Muni lines. Historical data is currently available from August 2017 on. 


The SFMTA is committed to fostering an urban environment where sustainable modes of travel are desirable, accessible, and preferred over operating a private vehicle. In line with the city’s Transit First Policy, the agency will continue to work on its ongoing service enhancements and multimodal infrastructure improvements across the city. The agency’s focus on improving and increasing the use of sustainable modes will result in decreased congestion, better transit performance, and enhanced networks for all modes in the city.

Percentage of Muni trips with service gaps increased in the summer of 2018. The Twin Peaks Tunnel shutdown required additional buses and more operators to be trained to drive them, while at the same time newly purchased, technologically-advanced light rail vehicles required even more operator training. This created a perfect storm of circumstances that stressed Muni’s training pipeline and resulted in an operator shortage causing an increase in service gaps.

Going forward, the SFMTA’s commitment to increase operator training and availability and its ongoing fleet replacement are expected to improve service delivery and vehicle reliability, which should contribute to reduced service gaps. More Muni Forward service improvements and transit priority projects are proposed for routes across the city, which should also continue to improve performance.

How Performance is Measured

The SFMTA monitors the regularity of vehicle departures by measuring the actual departure times at designated points along transit routes and comparing them to the average hourly scheduled headways (time between vehicles). Percentage of Muni service gaps is determined by dividing the number of gaps in headways by the total number observed departures and is reported system-wide. A gap occurs if a vehicle arrives more than 5 minutes later than the scheduled headway.

View the Rapid Network and non-Rapid Network service gaps on the SFMTA's website.

The number displayed on the scorecard page represents a fiscal year average of the values in the charts above.

Additional Information


Please visit DataSF for the scorecard data.