Daniel L. McFadden

The research I’d like to describe was initiated in response to the travel models that were available in 1970. At that time, the dominant tool for urban transportation planning was the gravity model. I thought it was just too coarse to yield sensitive predictions of travelers’ choices among modes. Besides, because it was nonbehavioral, it was a poor fit for those of us brought up on economic theory. Before recounting the exploratory path that led to discrete-choice models, I should first say something about gravity models.

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Donald Shoup

How far is it from San Diego to San Francisco? An estimate of 632.125 miles is precise—but not accurate. An estimate of somewhere between 400 and 500 miles is less precise but more accurate because the correct answer is 460 miles. Nevertheless, if you had no idea how far it is from San Diego to San Francisco, whom would you believe: someone who confidently says 632.125 miles, or someone who tentatively says somewhere between 400 and 500 miles? Probably the first, because precision implies certainty. Although reporting estimates with extreme precision indicates confidence in their accuracy, transportation engineers and urban planners often use precise numbers to report uncertain estimates. To illustrate this practice, I will draw on two manuals published by the Institute of Transportation Engineers (ITE)— Parking Generation and Trip Generation. These manuals have enormous practical consequences for transportation and land use. Urban planners rely on parking generation rates to establish off-street parking requirements, and transportation planners rely on trip generation rates to predict traffic effects of proposed developments. Many transportation models also incorporate trip generation rates. Yet a close look at the data shows that unwarranted trust in these precise but uncertain estimates of travel behavior can lead to bad transportation, parking, and land-use policies.

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