A great article on transport modelling did the rounds over the past couple of weeks, raising questions about how much reliance we put on these algorithms in our transport decision-making process. We have criticised transport modelling on many occasions over the years, especially in relation to the City Rail Link business case – where inadequate modelling probably led to the project being delayed for a number of years.
The recent article on Vice.com has a higher level criticism than many of the technical flaws we’ve identified. It does this by asking whether transport modelling is behind many of the undoubtedly terrible transport decisions that have been made over the years. But before it gets to that issue, the article helpfully provides a bit of a “Modelling 101” guide:
Travel demand models come in different shapes and sizes. They can cover entire metro regions spanning across state lines or tackle a small stretch of a suburban roadway. And they have gotten more complicated over time. But they are rooted in what’s called the Four Step process, a rough approximation of how humans make decisions about getting from A to B. At the end, the model spits out numbers estimating how many trips there will be along certain routes.
As befits its name, the model goes through four steps in order to arrive at that number. First, it generates a kind of algorithmic map based on expected land use patterns (businesses will generate more trips than homes) and socio-economic factors (for example, high rates of employment will generate more trips than lower ones). Then it will estimate where people will generally be coming from and going to. The third step is to guess how they will get there, and the fourth is to then plot their actual routes, based mostly on travel time. The end result is a number of how many trips there will be in the project area and how long it will take to get around. Engineers and planners will then add a new highway, transit line, bridge, or other travel infrastructure to the model and see how things change. Or they will change the numbers in the first step to account for expected population or employment growth into the future. Often, these numbers are then used by policymakers to justify a given project, whether it’s a highway expansion or a light rail line.
Major transport plans and policies in Auckland have leaned very heavily on transport demand models. For some pieces of work it seems like the models have provided some useful insights – such as in ATAP where modelling indicated that Auckland simply can’t build its way out of congestion, instead needing to manage travel demand through initiatives like road pricing. But for other pieces of work models seem incredibly unsuitable – like the ‘Access for Everyone’ city centre transport strategy, where Auckland Transport’s implementation work is relying on transport modelling that’s incapable of understanding how making driving less attractive will result in fewer people driving. This is just setting the process up for failure (either inadvertently or deliberately, one can never tell with Auckland Transport) and raises the issue of why the transport profession has abdicated responsibility to transport models so much. The Vice magazine article picks this up further:
Either way, 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.
TDMs, its critics say, are emblematic of an antiquated planning process that optimizes for traffic flow and promotes highway construction. It’s well past time, they argue, to think differently about what we’re building for.
“This is the fundamental problem with transportation modeling and the way it’s used,” said Beth Osborne, director of the non-profit Transportation for America. “We think the model is giving us the answer. That’s irresponsible. Nothing gives us the answer. We give us the answer.”
A recurrent theme over the past 50 or so years in land-use and transport planning has been this idea that key decisions about where to grow our cities or what projects to build are simply responding to the preferences the public has, rather than shaping the very options people have available to them. For transport, models make travel demand seem like a completely external force that must be predicted and then provided for. This approach is central to the thinking that has got us into the congested, unsafe, unsustainable mess we find ourselves in. Most transport models (and most traffic engineers to be honest) think of travel demand like water and roads like stormwater pipes. In reality though, travel decision-making is way more complicated.
In the models, any trip made today will be made perpetually into the future no matter how much worse traffic gets.
Experts refer to this as “fixed travel demand,” which is essentially an oxymoron, because travel demand is almost by definition not fixed. We are always deciding whether a trip is worth taking before we take it. One of the major factors in that decision-making process is how long the trip will take. TDMs work the exact opposite way by assuming that if people want to go somewhere they will. Only then will they calculate how long it will take.
For this reason, some urban planners derisively refer to this approach as “the lemming theory of demand,” said Joe Cortright, an urban economist for the consulting firm Impresa and contributor to the website City Observatory, because it assumes people will keep plowing onto highways no matter how bad congestion gets.
“It’s not so much about the measurement being wrong, it’s that the whole underlying thesis is wrong,” said University of Connecticut professor Norman Garrick. “You’re not thinking about how people behave and how they’re using the system. You’re just saying this is how it happened in the past [and] this is how it will happen in the future, even though you’re injecting this big change into the system.”
While most good strategic models don’t have completely fixed demand – a new public transport project can affect the mode choice people make for example – many of the model’s fundamentals like daily trip numbers are fixed. Furthermore, from what I understand some of the more detailed models (like what Auckland Transport uses in the city centre) do have completely fixed demand, meaning that they literally aren’t able to replicate the real behaviour changes people make in response to changes on the street network.
Models also tend to under-estimate ‘induced demand’ – which probably explains why our motorway remain congested (at least before Covid lockdowns!) despite countless past projects that were meant to fix that congestion.
This phenomenon is called induced demand, and it is not merely a thought exercise. It is precisely what has happened in nearly every case where cities build new highways or expand old ones.
“Recent experience on expressways in large U.S. cities suggests that traffic congestion is here forever,” wrote economist Anthony Downs in his 1962 paper The Law of Peak-Hour Expressway Congestion. “Apparently, no matter how many new superroads are built connecting outlying areas with the downtown business district, auto-driving commuters still move to a crawl during the morning and evening rush hours.”
Experts have known about induced demand for generations, yet we keep adding more highways in the Sisyphean task of attempting to build our way out of rush hour traffic. To fully appreciate the absurdity of this quest, look no further than the $2.8 billion freeway project in Katy, Texas that was supposed to reduce commute times along the expanded 23-lane freeway, the widest in the world. All too predictably, congestion only increased, and commute times are longer still.
A 2011 paper called “The Fundamental Law of Road Congestion” concluded “increased provision of roads or public transit is unlikely to relieve congestion” because every time new lane-miles are added, trip miles driven increase proportionately. The more highways and roads we build, the more we drive. (The flip side is also true: in the rare cases when highways are temporarily out of commission, such as the case with the Alaskan Way Viaduct in Seattle, traffic doesn’t get much worse.) And TDMs have been totally ignorant of it.
Does this mean transport modelling is a complete waste of time? Should we stop using models altogether? Not necessarily.
Jarrett Walker, commenting on the same article in a recent post, highlights that modelling can be useful when used properly – but it should never be the decision-maker and it should never be seen as simulating exactly what will happen. Models make far too many assumptions about the future for that to ever be true. As Jarrett illustrates, many of these assumptions are based on what’s happened in the past – which makes sense from some perspective as that’s the evidence we have. But from another perspective, especially if you’re deliberately trying to achieve change, these assumptions might be wildly wrong.
The best modeling is not nearly as dumb as the examples Gordon highlights. But the problem of all modeling is that to show the effects of a proposed action, you have to assume that everything else in the background will remain constant, or at least will continue changing only along predictable paths.
When the modeling process considers many possible futures, the one that is most like the past is called the conservative assumption, as if that means “this is the safest thing to assume.” This assumption seems calm and rational, attracting many people who would never call themselves conservative politically. But fact, assuming that the future will be like the past can be crazy if the trajectory defined by the past is unsustainable — environmentally, financially, or morally. “Unsustainable” means that it is going to change, and in that case, the “conservative” assumption is really the “self-delusion” assumption.
Transport modeling can’t be thrown out, but it never tells us what to do. It is a basic logical fallacy to say that “the modeling shows we must do x.” All modeling insights are if-then statements. A full version of this statement, which I would like to see at the beginning of every modeling-drive transportation study, is: “This report shows that if the future matches our assumptions, then you can expect this outcome. But the future may not be like that. In fact, maybe it shouldn’t be like that. So what really happens is up to you.”
So perhaps modelling isn’t complete junk science. But always be enormously sceptical when people appear to be abdicating responsibility for a decision to modelling. And always question whether the modelling is simply guiding us to repeat the mistakes of the past.