Traditionally we figure folks will walk a quarter mile to a bus line, maybe a bit further to a rail station.
But an analysis from Dallas suggests that rent premiums can be found as much as a mile away from light rail…
Over at Human Transit, Jarrett has an excellent post detailing why dedicated park & ride facilities (as opposed to shared use of existing parking lots that have another principal function – like church lots) make no sense from either a transportation or land use point of view, especially when there is no charge for parking. We see this absurdity locally, with TriMet charging for card-lock cage bike parking, but allowing autos free parking. (My beef is NOT with charging for bikes, it’s with the free auto parking!)
Historically I think we owe this pattern of development to a degree to the Federal Transit Administration. They used to grade New Starts applications on a metric called TSUB (Transportation System User Benefit) which was essentially a score for how many people you move how far, how quickly. In that formula, boarding a lot of riders from park & rides probably gave you a boost in a score, at least in the early years of operation of your corridor (before TOD built out).
As a key factor in determining rates for Portland’s proposed street utility fee, the Institute of Transportation Engineers’ (ITE, henceforth) Trip Generation Manual has gotten a lot of love lately among local transportation wonks. It is worthwhile, then, to take a quick trip through the weeds of the manual to better understand where the opportunities and complications lie when it comes to using this data as the backbone of our fee structure.
First, a bit of context. The art/science/guesswork of predicting the trip generation of various land uses is the first of four steps undertaken in transportation forecasting. Along with the ensuing three steps—determining where, generally, the trips will originate and end; choosing which mode they will utilize; and identifying the time, route choice, and other properties of the individual trips—the goal is to understand the future needs of the transportation system based on current land use and development patterns. Because most jurisdictions have a concurrency requirement—a stipulation that roads and intersections must have adequate capacity to accommodate new demand concurrent with a proposed development—trip generation is of particular interest to folks in my line of work so that we might identify what developers must do to meet this requirement, blissfully ignoring any notion of induced demand.
To this end, ITE has been aggregating and disseminating trip generation data since the first edition of the manual was published in 1976 (the current edition is the ninth). The context manifests in the data in myriad ways. For any of the 172 land uses listed in the manual, the robustness of the dataset is likely a function of both how often that particular land use arises, and how much NIMBY-ism it’s likely to inspire. The well-known suburban bent of the data owes to the fact that most development over the last 40 years has occurred in the suburbs or exurbs, so this is where the vast majority of studies have been conducted.
The trips described by the manual are one-way trips, so what one might colloquially describe as “a trip to the grocery store” is actually two trips: the trip from home to the store, and the return trip home. This is important for analysis purposes—the “trip to the grocery store” will indeed traverse an intersection along the way twice—but results in quantities that are twice as high as what one might intuit. This means that in a closed system each trip is double counted, as both the home and the grocery store would be credited with generating both an inbound and an outbound trip in this example.
The manual provides two general mechanisms for determining the trip generation of a given land use. The first is a mathematical function derived through what’s called regression analysis, which attempts to fit the cleanest possible curve to a set of disparate data points. The second is the trip rate, often expressed as a number of trips per thousand square feet. But it’s important to recognize that square footage is often not the best (or even a viable) independent variable for predicting trip generation. For example, student enrollment is a much better predictor of trip generation for schools, employee count is a better predictor for offices, and the number of ‘fueling positions’ is a better predictor for gas stations.
Interestingly, there’s another predictor for trips generated by a gas station that works better than floor area: the amount of traffic using the street that it’s located upon. That’s because gas stations generate a large number of pass-by trips, which are trips that are ultimately headed to another destination (this destination is credited with generating the primary trip) but stop at a business located directly along the way. A similar type of trip—a diverted trip—is also a trip ultimately headed elsewhere, but in this case the pit stop entails a small amount of out-of-direction travel. There are also internal capture trips, which describe trips that take place entirely on roads and facilities located within a mixed use development. Note the suburban bias there; in the city, this would likely take the form of a person parking once and visiting several locations on foot, using the public streets.
And here we have arrived at the biggest failing of the Trip Generation Manual with regard to our purposes: The manual implicitly considers only vehicular trips. Assuming that only a nominal number of trips are non-automotive might work for the ‘burbs, but this often causes the stated trip rates to be wildly inaccurate in the city. Recent work by Professor Kelly Clifton’s research group at Portland State University confirms what we might have suspected: The more “urbany” a built environment, the more inaccurate the assumption that all trips are automotive is likely to be.
Thus, there is a need to distinguish between the vehicular trips quantified by the ITE manual and what are generally called person trips, which include all trips regardless of mode. Fortunately, as Clifton verifies, the latter seems to be relatively consistent regardless of the built environment. So perhaps by considering only vehicular trips, but doing so primarily in auto-centric locations, the manual has inadvertently provided a good proxy for estimating the differences in person trips generated from one land use to the next. There are exceptions, of course—the manual will understate the person trips generated by a school compared to other land uses, for example, due to the prevalence of buses in travel to and from schools. But it seems that basing a potential street fee on person trip rates inferred from Trip Generation Manual data is defensible and keeps with the spirit of the residential fee in being mode-independent. Basing the fee on vehicular trips, by contrast, would be far more complicated to implement and would leave unsolved many of the issues with the gas tax that Chris Smith wrote about last week.
The manual offers a lot of utility with regard to predicting trip generation, but really it’s just one piece of a puzzle that fits together differently from one land use to the next, and one business to the next. To accurately model the trip generation of a particular business requires a heck of a lot more than the published trip rate, which does not consider countless predictors and is often derived from a small sample size. While using these rates as the basis of the street fee would hardly be the first or most egregious misuse of the data, it seems inevitable that it will result in some businesses substantially overpaying and others substantially underpaying. As luck would have it, that seems to fit with the spirit of this fee quite well.
One of my big and untested (but unrebutted) hunches about the urbanism revolution, the drop in vehicle-miles traveled per person and so forth, is that it all flows from the rapid and mostly unexplained decline in crime rates that began in 1994.
As cities became safer, the first to notice were the young, poor, mobile and liberal. It seemed strange to our parents — but then, our parents’ bizarre fears of the central city seemed strange to us.
Just as, I’m sure, the rise of those fears (also known as the 1960s) seemed strange and unfair to my vaguely Germanic grandparents.
I’ve been watching the sixth season of Mad Men, the one that happens in 1968. Scenes on the main character’s once-quiet Manhattan balcony are being interrupted by screaming sirens; the middle-class couple who buy into the Upper West Side find themselves besieged by crooks. It was a rapid change in atmosphere that’s backed up by the statistics:
50 years later, local crime trends have reversed, perceptions of local crime have followed, and so have the tides of urbanism. As Mayor Hales put it in a speech last month, the flight to the suburbs was a round trip. The Don Drapers of the world are again buying Pearl District lofts, the Peggy Olsens are again renting two-bedrooms on Division or Thurman, and they’re both biking in to Wieden on Monday morning.
Whatever the cause, Americans do seem to be more or less aware that crime has gotten better, as long as you’re asking us about crime in our own personal lives. If you ask whether crime is a problem in the United States in general, most people, fed on Nancy Grace and Fox 12, will tell you it’s bad and getting worse. But when it comes to our own trains, parks and streets, we tend to be in closer touch with reality.
On the other hand — and if my hunch is wrong, I actually think this is why it is — there’s a chance the causality flows the other way. Maybe cities aren’t getting safer, and therefore more desirable and expensive, because urban crime has slowed. Maybe urban crime has slowed because poor criminals were, as early as 1994, being joined in the central city by gentrifiers and, ultimately, priced out of central cities — driven into neighborhoods where even a decent crook has to own a car to make a living.
Whatever the reason, I’ll be watching the various theories closely as they develop. Here’s why: what fortune giveth unto urbanism, fortune is just as likely to take away.
As someone who’s staked a lot on the continued desirability of living in the central parts of U.S. cities, I’m worried about the final two data points on this chart. And if I were you, you’d be worried too.