Traffic Impact Study Improvements: Part 2 – Would Multiple Results Help Us?
Guest Post by Bryant Ficek, PE, PTOE, Vice-President at Spack Consulting
Earlier this year, I detailed how our standard process for a Traffic Impact Study has several points of assumptions at best or guesses at worst. This post continues that discussion. The original post, “Top 6 Ways to Pick Apart a Traffic Study” is available here. Other posts are expected to follow on this subject.
Originally, I had intended to move right down the list of 6 discussed in that first post; traffic counts through background growth. I have ideas on how we as an industry could improve how we complete a study involving all of those items. Granted, there may be better ways on some items, which is way I am opening up the debate and hoping for feedback from colleagues. But before I finished a write-up on the next assumption, we had a detailed discussion on the end result of these potential new processes – would multiple scenarios and thus results help us?
Before we try to answer that, let me provide a little background. I like to think of our current traffic impact study as a linear process. We start with counts, move onto existing analysis, forecasting, future analysis, and then determining mitigation and recommendations. Graphically, I would say our process looks like this:
We start at one end and move through each of the points until we reach a conclusion. Each of these points represents one data point of input or output. Sometimes we have discussions about the points before the project, sometimes it occurs during or after and we make adjustments. But in all cases, we remain with one data point for each subsection.
With our new process we are implementing, detailed in Part 1 here, we are expanding the initial point in hopes of getting better base data. We obtain counts for two days, apply a seasonal factor, and end up with an adjusted count as the starting point. Using the previous graphic, our adjustment makes it look like this now.
That’s a step in the right direction, but still with room for potential improvements in my opinion. So what if we expanded more of those points? Our analysis software has improved enough to allow us to quickly test multiple scenarios, particularly if only changing one input at a time. So imagine if our study process instead considered:
- two different background growths reflecting high growth and low growth
- three trip generation results reflective of different levels of new traffic based on varying trip generation rates, different pass-by/multi-use/internal rates, and higher or lower levels of transit/bicycle/pedestrian traffic
- two different trip distribution patterns based on actual counts and then adjusted to reflect more balance
That scenario would end up with 12 data points of future analysis and look something like this:
Don’t get lost in the debate about how many points to analyze or how to determine what each point should be just yet. Instead, focus on the end product and the original question – does having multiple results help us? Or, stating it a different way, how do we go from 12 future analyses to one recommendation?
To further illustrate the example, I evaluated one intersection assuming a scenario with a standard office development. Two sets of background growths were determined as well as three sets of trip generation and two sets of trip distribution. Thus, the unsignalized intersection was analyzed 12 times with varying levels of future traffic. The graph below shows the future analysis results in terms of overall intersection average delay (and Level of Service), with the existing results for comparison. The future analysis result that would have been determined using our standard process is also highlighted.
As shown, the standard process would have resulted in Level of Service (LOS) D and our subsequent recommendation would likely not have included mitigation. However, the other results show five more at LOS D, one at the LOS D/E border, four at LOS E, and one edging close to the LOS E/F border. If this was your study, how would you interpret this data? And what would your recommendation be based on?
I think a reasonable conclusion would be state that the intersection is sufficient, but a plan should be developed for mitigation to cover 10 out of the 12 scenarios, roughly the 85th percentile. A slightly more conservative conclusion would say build mitigation to cover eight out of the 12 scenarios and plan for the worst case. But that’s the engineer taking a measured look at the data.
Once it leaves our hands and is reviewed by others, the graph is likely to turn into something of a Rorschach Test. A developer is likely to look at the data and conclude nothing is needed since half the results reveal no difficulties. A government official could look at the data and conclude that the worst case needs to be built to protect against any adverse impacts. Opinions on each extreme will be easy to find and likely complicate the already often tense discussions of what should be built as the result of a development.
That leaves us with the conclusion that more analysis, and more data points, is a good thing from the engineering standpoint. It can provide more confidence in our opinions and ultimate conclusions. But once it leaves our hands, it is not likely to be as well received and may make the ultimate solution or compromise more difficult to reach. In that regard, is it worth it to even begin traveling down this path and, bringing back full circle, does having multiple results really help us? I welcome any of your thoughts on the matter.
Did you miss the other installments of the Traffic Impact Study Improvements series? Here are the links to the other articles: