Various Contrivances

Skipping past the unnecessarily dramatic title, The Broken Algorithm That Poisoned American Transportation does make some useful points. As seems typical though these days, the good points are likely not the ones a quick reader would take away. My guess is most people see the headline and think that transportation demand models (TDMs) are inherently broken. Despite my biases, I don’t think this is actually true.

For me, the most important point is about a third of the way through:

nearly everyone agreed the biggest question is not whether the models can yield better results, but why we rely on them so much in the first place. At the heart of the matter is not a debate about TDMs or modeling in general, but the process for how we decide what our cities should look like.

Models are just a tool for helping guide decisions. Ideally we would use them to compare alternatives and pick a favoured “vector” of change (rough direction and magnitude). Then with continuous monitoring and refinements throughout the project’s lifecycle, we can guide decisions towards favoured outcomes. This is why scenario planning, sensitivity tests, and clear presentation of uncertainty are so important. This point is emphasized later in the article:

civil engineers doing the modeling tend to downplay the relevance of the precise numbers and speak more broadly about trends over time. Ideally, they argue, policymakers would run the model with varying population forecasts, land use patterns, and employment scenarios to get a range of expectations. Then, they would consider what range of those expectations the project actually works for.

Although I’m not a civil engineer, this sounds right to me! I get that people want certainty and precise numbers, I just don’t think anyone can provide these things. Major infrastructure projects have inherent risks and uncertainty. We need to acknowledge this and use judgement, along with a willingness to adjust over time. There is no magical crystal ball that can substitute for deliberation. [Me working from home:🧙‍♂️🔮]

Fortunately for the modellers among us, the article does acknowledge that we’re getting better:

As problematic as they have been, the models have gotten smarter. Especially in the last decade or so, more states are working from dynamic travel models that more closely reflect how humans actually behave. They are better at taking into consideration alternate modes of transportation like biking, walking, and public transportation. And, unlike previous versions, they’re able to model how widening one section of road might create bottlenecks in a different section.

But, wait:

Still, experts warn that unless we change the entire decision-making process behind these projects, a better model won’t accomplish anything. The models are typically not even run—and the results presented to the public—until after a state department of transportation has all but settled on a preferred project.

😔 Maybe it wasn’t the model’s fault after all.

This brings as back to the earlier point: we should be favouring more sophisticated decision-making processes, not just more sophisticated models.