Every month, Metro’s customer service committee looks at a presentation on operating statistics, which includes a chart showing the latest bus “on-time performance” percentage. Usually, the number is around 73-75% and reflects the number of buses that arrive within a certain time before or after the published schedule.
This number is not helpful as a management tool. If the on-time percentage improves or degrades, without looking any further would Metro be able to say why? If the percentage degrades drastically, could the Board do anything other than ask Management to do a better job?
The number does not identify problems with bus bunching, especially on frequent routes. Second, the number does not improve understanding and correction of bus system problems. Management needs to be able to identify trends, detect problems with individual routes or trips, and focus their attention on the areas that might need more resources or oversight.
The reported on-time percentage doesn’t promote accountability to the public. Wouldn’t it be nice if you knew how poorly your bus line performed, and knew why Metro was devoting a lot of time to improving that other bus route before yours?
London has a great bus on-time performance measurement program. Because the bus lines in London are operated by private contractors, it’s very important for the local transit authorities to accurately measure on-time performance because there are real financial incentives or penalties involved.
Here’s the difference in how Metro measures on-time performance compared to London, as an example: Imagine a bus line that is supposed to have service every ten minutes, but experiences bunching. Take five buses in a row starting at 8am (see top line in the figure), bunch the middle three together and spread the other two out (see bottom line in the figure). This is a worst case, but under Metro’s on-time percentage rules, all of these buses are considered “on-time”, because each bus is within two minutes ahead of or seven minutes behind schedule (the green bar shows the range of times the 8:20 bus could arrive and still be considered on-time). A passenger arriving for the 8am bus will have just missed it (it left two minutes early), and will have to wait until 8:17 for the next bus, a wait of just over seventeen minutes for a bus that’s supposed to come every ten!
London looks at bus on-time measuring differently. They measure how often buses pass by certain points on the network and track the “excess waiting time”. All that time you have to wait for a bus that’s running late or is bunched with others is added up and averaged over the route, and the excess waiting is compared to how much you’d normally have to wait assuming you come to the bus stop randomly. In our example above, the average scheduled wait time is five minutes, and there are two buses you’d have to wait on average eight minutes, so the excess waiting is six minutes total, about 1.5 minutes per bus, or about 30% extra (note that buses don’t get credit for making you wait less than average).
This makes it easy to see when high-frequency buses are not meeting the required headways, and London applies this calculation to all buses that are supposed to come every 12 minutes or better. They even post the information on the web quarterly.
For frequent buses, Metro should change the way they measure on-time performance. The current measurement does not work for frequent buses. The London model is customer-oriented, compares various bus routes’ performance, and gives a sense of the magnitude of the problem.
In addition to this change for high-frequency bus routes, Metro should start regularly reporting on-time performance figures for all bus routes, as part of the monthly ridership report. They should also highlight the worst performing lines for each jursidiction. If the problem is somehow Metro’s fault, the route can receive the appropriate management attention. More likely, traffic congestion or other factors are at fault, and Metro Board members would then have data in hand to make their case with state and local transportation officials, to make transit operation a higher priority on corridors that are experiencing poor performance.
By identifying and improving poor performing bus lines, we can get people moving to their destinations more quickly, and reduce operating costs. Faster travel speeds and more regular schedules would drive up ridership, improving Metro’s bottom line and allowing more service with the same local subsidy. Metro has to improve the way they present performance metrics to make it possible.