We have long known in the transportation planning community that the use of trip generation for local area review, and the Institute of Transportation Engineers (ITE)'s procedure for estimating trip generation is broken in any number of ways. Shoup's Truth in Transportation Planning is a classic critique of the problems.

[Trip generation is the first step in the conventional four-step transportation forecasting process (followed by trip distribution, mode choice, and route assignment), widely used for forecasting travel demands. It predicts the number of trips originating in or destined for a particular traffic analysis zone.]

While we could (and perhaps should) through the whole kit and caboodle into recycling, in practice trip generation methods will be with us decades from now (even as traditional work, shopping and driving disappear). So there is a small academic movement to make the methods better. The most recent issue of JTLU 8(1) has a special section on Trip Generation, including several papers about how to adjust and improve ITE's Trip Generation methods based on better data.

Part of the problem is that ITE is functionally a for-profit organization, and makes bank on selling the Trip Generation Manual and associated software (recognizing the fact that use of ITE Trip Generation rates is ensconced in law and regulation).

What has long been needed is an open source database of trip generation studies so that better fits to actual site conditions can be used in analysis. I recall in my youth some engineers in Montgomery County, Maryland trying to set something up, but this was well before the world wide web made that easy.

Fortunately that day is upon us. Mike Spack and company have set up TripGeneration.org, which is populated with open access trip generation studies (licensed under a Creative Commons license), and for which they hope to grow the data set.

This set is professionally collected and currently holds 2500+ hours of data. Signing up gets you sent the current Trip Generation Data Spreadsheet.

This is new, and I assume as it grows the data will get better and better, as will the methods for inputting and extracting data. Kudos to Mike, Nate, and others at Spack Consulting for getting this going. I look forward to seeing where this goes, as Big Data and new sensors make data collection increasingly ubiquitous.

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