Blog

November 15, 2019

The Operator Data Blindspot and How It’s Hurting Transit Ridership

by Swiftly

Vehicle operators check their mirrors to see into their blindspots. It’s time operations teams do the same for the blindspots of their own data.

Transit reliability is the key to increasing ridership

During the last five years, transit ridership has declined in all but six cities across the country, and pundits have thought up innovative ways to stem the losses and turn the trend around. Partnerships with Uber? Yes! Trans-municipal long-range feasibility surveys? Pile ’em on! A local Hyperloop commuter rail? Ribbon cutting by 2040!

There are lots of flashy theories about how to increase ridership, and some of them do work, but the ones with the biggest impact tend to be the tried and true.

At the top of the list of how we actually increase ridership? Improving transit reliability.

Of course that raises the question: how do we improve reliability? Familiar strategies go a long way in doing this — things like building slack into schedules to account for unpredictable run-times, using GPS tools and real-time live maps to manage vehicle fleets, minimizing riders’ perceived wait times with real-time apps, to name a few. Even with these in place though, ridership has continued its leisurely ride downward and to the right. So what do we do?

Operator data holds important answers to transit reliability

One of the most overlooked contributors to transit reliability is how well agencies coach their operators to execute schedules and service plans. Are operators consistently leaving their first stop on time? Are they using recovery time to help keep things on track throughout the day? Are they taking their breaks to stay fresh and focused?

One of the most overlooked contributors to transit reliability is how well agencies coach their vehicle operators to execute schedules and service plans.

In our never-ending quest to help agencies improve the reliability of their transit service, we asked operations teams these exact questions, and the results surprised us. Shockingly, even the most advanced agencies couldn’t answer very basic questions about how their operators are actually executing scheduled service. Specifically:

  • Most agencies understand operator performance solely through anecdotal data. It’s usually via negative customer feedback.
  • As many as 6 out of 10 complaints are inaccurate. This means the “data” agencies have about operators is usually ad hoc and negatively skewed.
  • Many agencies are likely penalizing operators who actually deserve promotions; operators on the hardest routes usually carry the most passengers and often get a disproportionately high number of passenger complaints.

This is a huge blindspot in the way most agencies provide reliable service. Without consistent and representative data about vehicle operators, it’s impossible to get a clear picture of how well agencies are coaching their operators to conduct service the way it was planned.

The cost of the operator-data blindspot

What isn’t measured isn’t managed, and ad hoc operator data is hurting agencies in three key areas.

1. Schedule adherence. A plan is only as good as its execution, and when operators aren’t consistently coached on best practices with actual data, inconsistencies like early departures, bus bunching, and on-board delays will inevitably creep in. For example, whenever an operator starts a trip early, they’ll pick up fewer riders than intended, leaving more passengers for operators behind them, starting a domino effect of gapping, hurting the customer experience and on-time performance in the process.

Early departures from timepoints are also a red flag for scheduling teams to cut run-times on segments. It’s common for operators to leave timepoints a little early to give themselves extra time on upcoming segments that may be particularly congested. But they may be unwittingly shooting themselves in the foot. By departing early, they’re simply giving schedulers a reason to cut their run-times (“If they’re driving a segment in 12 minutes, why am I giving them 15?”), only leading to more stress in the long run. These kinds of issues are easy to coach operators on, but without accessible data to flag occurrences, they typically go unnoticed.

2. Operator morale. Happy drivers are the best drivers. And science backs this up. Studies show that when reward systems are aligned with performance, employees are much more likely to overperform and less likely to quit. Driving the bus can be a very rewarding profession, but its thankless moments can cause operators to feel stressed and dejected, especially when their supervisors give them flak for things outside their control.

Most agencies evaluate operator performance solely (or almost solely) through customer feedback, usually negative and often unfair. A few errant complaints take on an outsize weight when years of stellar service should be the guiding light.

Not only is this unfair, but there’s also a dollar cost attached to it. Whenever an employee quits, the price of finding a replacement is anywhere from 15 to 25% of their annual salary. With the current operator shortage, it may even be considerably more.

3. Safety. Swiftly data shows that vehicles leave their first stop late about 15% of the time. This can contribute to operators speeding up at yellow lights or going over the speed limit to make up time down the line. Even when an operator leaves the depot at 5:02, they’re still beholden to a timetable that assumes they departed at 5:00. While anecdotal, we’ve heard of unsafe driving habits stemming from these types of conditions. When bus riders feel unsafe, they’re less likely to return.

Mirrors are for blindspots. Let’s use them.

There’s no silver bullet to improving transit ridership, but the sum of many small, yet impactful, tactics is the next best thing. In an industry where projects commonly top the billion-dollar mark, strategies carrying five-figure price tags can accomplish the same things, just on a much smaller scale. Buses have mirrors for operators to see into their blindspots. It’s time operations teams do the same for the blindspots of their own data.


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