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|
Each segment has some times associated with it:
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|
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 :
This 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:
As 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: hours of padding per hour.
Now, 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…
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.