Traffic Impact Study Improvements: Part 3 – All Trips Are Equal, But Some Trips Are More Equal Than Others

Using Local Trip Generation Data in Traffic Analysis

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.  Check out the “Top 6 Ways to Pick Apart a Traffic Study” for more on the general topic and expect more posts to follow on this subject.

The Institute of Transportation Engineers’ (ITE) Trip Generation Manual could be considered the Bible of Traffic Impact Studies (TIS). Composed of thousands of voluntary study submissions over many decades, this book is the most comprehensive list of average traffic per various land uses in the United States.  It is used by virtually all traffic engineers across the country, including us at Spack Consulting. If a development is proposed, we estimate the traffic according to ITE data, and follow-thru with the analysis and recommendations.

As good as this source is, it’s not perfect. ITE itself says, “At specific sites, the user may wish to modify trip generation rates presented in this document to reflect the presence of public transportation service, ridesharing, or other TDM measures; enhanced pedestrian and bicycle trip-making opportunities; or other special characteristics of the site or surrounding area.” In other words, take these rates with a grain of salt.

My primary issues with using ITE data are:

  • Restaurant Drive ThruOld and new data is mixed together. Is a study of an office trip generation from the 1980s still accurate given today’s environment?
  • No breakdown of the area where the studies were collected. Downtown is different than suburban is different than rural. Similarly, bike-friendly Minneapolis is different than car-centric Los Angeles is different than transit-heavy Manhattan.
  • An exact land use match is not always possible. Fast casual restaurants like Chipotle fit nicely between the official land uses of Fast Food Restaurant and High Turnover (Sit-Down) Restaurant.
  • Many land uses only have one or two incomplete studies available for use.

Combine these issues with the fact that many land uses have a very large standard deviation (a residential single family home has a standard deviation of 3.7 on a rate of 9.52 trips per dwelling unit, meaning the actual trip generation could be between 5.82 to 13.22 trips per dwelling unit), and it’s easy to see how trip generation is another TIS assumption that could be challenged.

There is another option that is recommended by ITE itself, collect local data. We’ve taken that suggestion to heart and actively consider whether collecting local trip generation data should be part of our plan. Sometimes, given the project schedule or other factors, this option is just not realistic. In those instances where the project would add significant value, our process includes:

  1. Look for three similar facilities in the surrounding area
  2. Set up cameras to capture trip generation at each of the three similar facilities at the same time the study intersection cameras are set
  3. Establish the trip generation for each similar facility and determine the weighted average rate for use in the study

This process provides us with a more confident and defendable trip generation rate which should lead toward better studies and more accurate results and recommendations. In addition, this method automatically accounts for reduced trip generation due to nearby transit facilities as well as bicycle and pedestrian facilities relevant to the study area.

Trip Gen Combined ImageWith a stockpile of previously gathered trip generation, and more on the way from our new studies, we have also dovetailed this new process into TripGeneration.org. This is our website to freely share trip generation data under a Creative Commons license. We’re hoping to transform the current ITE trip generation list of average rates into something that can be filtered into the most applicable data for each particular project.

Did you miss the other installments of the Traffic Impact Study Improvements series? Here are the links to the other articles:

  1. Part 1 – Traffic Counts
  2. Part 2 – Would Multiple Results Help Us?
  3. Part 3 – All Trips are Equal, But Some Trips are More Equal Than Others
  4. Part 4 – 8 Considerations When Generating Trip Distribution Patterns
  5. Part 5 – When is a Trip Not a Trip? How to Determine Trip Generation Types

Please note: I reserve the right to delete comments that are offensive or off-topic.

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