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The Effects of Schedule Padding

An excerpt from (the background section of) the first draft of my thesis proposal:

“The bus is so slow! Isn’t rail just better?”
The popular confusion created by the conflation of coaches and railcars with their typical relation to automotive traffic has wasted billions of public dollars1 (and caused me no end of frustration). Though the superficial wheel may not much matter, the general public is right to sense a distinction in the reliability and speed of transit services that operate in mixed traffic and those that are given priority over such traffic. As the public more and more aggressively demands train-based transit services, these should be read as demands for increased speed and reliability (among several other things) and planners should respond by modifying existing services to meet these demands.

Speed and reliability are a function in large part of how many potential delays a line will encounter along it’s course. Random delay results from unplanned disruptions such as higher than expected passenger loads, traffic, serial red lights, etc. Scheduled delay, also known as schedule ‘padding’ is delay that is built into scheduled transit services that allows them to be tolerant of unscheduled disruptions by acknowledging their average effects in advance. Agencies try to balance scheduled and unscheduled delay to create schedules that are neither too slow nor too often disrupted by random delay. While the public often reacts negatively to random delay events, they’re typically unaware of schedule padding, though both are dependent on basically the same environmental factors.

Since the public, and not transit schedulers, are in control it becomes important to explain delay and it’s causes and effects to a lay audience and thereby to direct them toward a fruitful response. Further, since funds for radical infrastructure interventions may be difficult to find in the current political regime, attention should be focused on marginal cases and incremental improvements to surface-running bus lines.

Simply, the question is: where can the smallest new delay-avoidance technique create the biggest improvement in speed and reliability for existing services?


As a first step toward an answer to this question, I’ve created a rough measure of the amount of schedule padding identifiable from just the schedule information itself. It’s not perfect by any means, but I’m going to run with it for a moment and see where it leads me. First I identified all unique trip segments in the transit system. A segment is defined here as the travel between two unique stops, so…

( stop A -> travel -> stop B ) = segment 1
( stop B -> travel -> stop C ) = segment 2
( stop A -> travel -> stop B ) = segment 1 again
( stop B -> travel -> stop A ) = segment 3
for a total of 4 segments, 3 unique in our example.

For SORTA’s current GTFS schedule, we observe:

Total Segments Unique Segments
Weekday 164,621 5,159
Saturday 103,757 3,914
Sunday 69,380 3,550

Each segment has some times associated with it:

  1. A departure from the first stop
  2. An arrival at the second stop
  3. Implicitly, the time scheduled to complete the segment.

Because schedulers expect that the amount of time it will take a bus to get from A to B will be different at different times of day, these otherwise identical segments will have different durations. By finding the deviation from the minimum duration for each segment, we can get a crude measure of the schedule padding built into the system.


Hours of Padding Scheduled Vehicle Hours % padding
Weekday 414.60 1,925.22 21.53%
Saturday 174.19 1,036.57 16.80%
Sunday 97.36 664.13 14.66%

This method estimates that 21.5% of the weekday schedule is actually scheduled delay, more than 400 hours of it, each weekday. That is, at least relative to the fastest any bus is scheduled to complete a segment. Just where is this scheduled delay anyway? When and where are the schedules most heavily padded? I’ll save a spatial exploration for later, but let’s take a very preliminary peek into the temporal dimension.

The first question we must ask is: when are all of the segments? By taking a central moment as the time, we can plot them, in a kernel-smoothed histogram :

SORTA segments per hourThis clearly shows the basic level of service throughout the day and week. It’s not a great measure of that as such, but it does give us a definite sense of the balanced weekday rush-hours and diminished weekend service.

Then since most of the segments are padded,we ask when are the segments without padding? On the same scale, we get:

SORTA unpadded segments per hourAs we might have expected, there is less random delay and thus less need for padding when the streets and buses are less congested: early morning and late at night. It also appears that there are relatively fewer padded segments on the weekends, though the total number of unpadded segments is roughly the same as on weekdays.

Ok, so when is the padding itself and how much of it is there? Note that we’re measuring something different here.

SORTA padding in timeNow, this definitely has a different shape than the overall distribution of schedule segments, but it’s a little hard to compare them when they’re so far apart. Let’s combine all of these into one plot. I might have got a little carried away in Inkscape…SORTA Schedule Padding by Time-of-Day


I’ll just let that speak for itself for now. We’ll get into spatial visualizations of this data next, and eventually real-time comparisons and measures in space-time.

Show 1 footnote

  1. Citation needed…
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Posted in: Back to Basics | Data
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Lean Urbanism

I never do this, but…after watching this video for the fourth or fifth time over the last year, I feel the need to simply ‘share’ it, with relatively little comment. Take an hour out of your day to give it a proper listen, especially if you’re a planner or you’re otherwise working in government. If you’re a ‘millennial’ like me, a group disjoint with the previous one, watch it with caution perhaps; Duany has a great many good ideas to offer, along with the observation that there’s not much we in particular can do about them. Except move to where the government is more fully broken than here.

There’s not much to the visual, so you can just listen and not miss much.

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Posted in: Politics

Car Wars: Return of the Gentry

Star Wars is not a story of a war between stars but a war about them. Stars set the scale of the series, it’s characters travelling in great loops about their massive references, always thinking on the scale of planets and moons and whole races of people. As the scale of their epic battle dictates the scale of their thoughts and attacks, so cars set the scale of our struggle today. Our American lives orbit these machines.

At the height of the battle we see our powerful protagonists, the rich, on the edge of a knife splitting between dark and light, seclusion or civilization. The tide is turning, and the rebel alliance may gain the upper hand…

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Posted in: Aphorism | Silly Bullshit

Delay Variance Through Time

Just some preliminary results from my first attempt to archive real-time bus data, these from the Champaign-Urbana ‘Mass Transit’ District:

Animated Delay Variance

This is a look at variance in the distribution of delayed buses throughout the day. It’s only looking at off-schedule buses right now so we aren’t seeing any change in the proportion of precisely on-time buses (if there is any such change). The little clock in the top right is the time of day the event was recorded, and right below that is the number of events used to ascertain the momentary distribution. For now, this sample size is as much a reflection of when I was running my computer as it is of schedule frequency.

I assumed that the first and last percentiles of the overall distribution were outliers and clipped them off.

I don’t actually see much going on here except random fluctuation, but I suspect this will get much more interesting with larger samples and many more and more diverse agencies included. I’m actually quite eager to see what that turns up! Right now, I’m working on developing a tool to query APIs from the Toronto Transit Commission and Philly’s SEPTA. Unfortunately, because neither Cincinnati transit agency is willing to share their data, which they’ve been collecting and using for years, it will be impossible to include them in this sort of analysis.

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Posted in: Data
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Bike Map locations

So far I know for a fact that bike maps have been dropped off in the following locations, though I’m certain there are others:

I’ll keep adding to this list as I carry maps out the door. Check back! And as I said before, I’m happy to send a couple in the post if I haven’t made it to your neighborhood yet — or even if you’re out of town. Just send me an email with your address.

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Posted in: Events

Gratuitous lists

In response to a gratuitous question posed by a potential flatterer, I respond drunkenly:

“What needs fixed in Cincinnati? What would you change? Give me a bulleted list of projects. Things a person might actually do. Transportation related. And do it by tomorrow.” (I paraphrase)

…so susceptible an ego.

I write these things for you too, my readers, because I think an honest word is worth at least four silences — and because of point two: treat people like they’re capable of an adult conversation and perhaps they will be. Whether you care to surpass my bombast or surcease it, I’ll see all comers to the debate.

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Posted in: Personal | Plans
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