Transport planning plays an outsized role in shaping the future of our metropolises. Land use and the daily activities of residents depend on transport and the access it provides. Transport only exists to serve activities people want to engage in. Growing cities naturally want to expand transport infrastructure. Because transport takes a long time to develop and deploy, and tends to be irreversible, it is worth some effort to try to get it right.
It is believed that effective planning requires accurate and reliable computerised transport models (UTPS or “four-step” models in the US, “Strategic Transport Models” in Australia), which serve as tools to predict and analyse the impacts of various transport policies and investments.
The models however are a fantasy:
Fantasy: n. “the power or process of creating especially unrealistic or improbable mental images in response to psychological need” – Merriam-Webster
These large-scale are either believed, to the detriment of all, or not believed but merely a kabuki-like, Potemkin-esque, box-ticking exercise to satisfy rules and regulations laid down in the name of scientism and used to justify the preferred actions of those in power, rather than to inform those actions in the first place.
Why they are dangerous? They place a misleading veneer of rationality over the top of single-point extrapolation. They are accepted at face value by a credulous press.
These models are almost always estimated based on a single survey at a single point in time, resulting in a single coefficient for every variable. This is fine only if nothing else changes and you have very high certainty in the rightness of your model. Casual observation of the world (cough, COVID, cough) suggests things sometimes change. So 2050 forecasts of travel demand are based on models estimated using 2010 surveys being run in the 2020s. These are no more likely to be correct within a reasonable margin of error than a random number generator. One only has to look back at the accuracy of previous forecasts.
Even if conducted in good faith (though not accurately) these models can lead to the misallocation of public funds by overstating (or understating) the benefits of transport projects. (And they are often not conducted in good faith). (The misestimation of costs is another department). When decision-makers rely on these flawed models, they may divert resources from other essential services and investments, such as education, healthcare, or social programs. This misallocation of resources can have long-term negative consequences for the communities affected.
Overly optimistic models can mislead decision-makers into supporting the wrong transport projects, projects that are not viable, cost-effective, or beneficial to the public. This can result in the approval of projects that might never reach completion or fail to deliver the promised benefits, wasting time, money, and political capital.
Unrealistic models can also contribute to environmentally harmful decision-making. By underestimating the potential impacts of a project on air quality, greenhouse gas emissions, or natural habitats, leads to projects that compromise environmental sustainability and public health.
When transport projects based on Fantasy Modeling fail to deliver on their promises, public trust in the planning process and government institutions can be severely damaged. The credibility (or what remains) of the transport planning profession suffers as a result, undermining public trust in future projects and initiatives. This erosion of trust makes it more challenging to garner public support for future transportation investments, creating a vicious cycle of skepticism and underinvestment in critical infrastructure.
Transport models are the centrepiece of the traditional “Predict and Provide” approach to transport planning. Newer thinking has moved us toward “Vision and Validate”. I am on-board with communities “envisioning” their future. But I am not entirely sure how we “validate” the future without living through it. The universe is Computationally Irreducible, we cannot know the future without running the full experiment, there are no short cuts. The same is true for the growth of cities and their networks.
Sometimes you just have to take the leap, understanding the theory and evidence about how transport infrastructure reshapes the world in a positive feedback way (more access → more development, more development → more infrastructure and more access), while recognising the randomness of life and unknowns of the world. So you should do what you think would move you in the right direction, even if forecasts cannot give you the certainty you desire. This argues for small incremental moves (leaping small distances) rather than massive ones, so your missteps are not as lethal and your elephants not so white.
My recent Fantasy Modeling post struck a nerve. Due to the fracturing of the modern internet, comments are everywhere (email, Mastodon, LinkedIn, even Twitter). I will round up the best that are not already on Substack, and some replies.
On LinkedIn, the Transport Modellers were highly defensive [Also LinkedIn truncated the full post, but no one reads to the … ] .
On the other platforms, people were much more antagonistic to modellers. Obviously comments are continuing, so I stopped collating when I stopped collating. If your comment on some other platform didn’t make it, please feel free to comment below.
I will admit to being a bit less precise in the wording of the problems than I should have been given the diversity of my audience (modellers/adjacent professionals/advocates/non-modellers, Americans/Australians/Europeans/others, developers/users/economists/decision-makers) who will all perceive things differently. There are a few inter-related problems:
- Time-frames (I was primarily talking about long-term (30/40 year models), The underlining issue here seems to be the absurd time frames these models operate within, disregarding the adaptability of society to the infrastructure it encounters.
- Transport determinism has been a leading approach in the planning and implementation of infrastructure projects. There are two sides of this. One the models are deterministic. They always produce the same outcome for the same inputs. There are of course scenarios, but there is in the end a single answer. The second side is the implicit assumption that transport systems directly influence the social and economic aspects of urban areas. Given the multitudinal variables involved, the calibration and validation of these models demand an unobtainable level of exactitude for what is asked of them. Compare with traffic simulation models, in particular, whose modellers insist on precision that strategic models cannot dream of.
- Tools (Which of course vary, and whose accuracy deteriorates with time). It is possible in theory to explain much of today in the aggregate with some high error. That error is however sufficiently high that it should concern us about the embedded modeling assumptions. We can consider both:
- Software: How many different software packages can the industry justify? Competition is good, but software is a classic high fixed-cost, low variable-cost industry, and we might be better off with fewer rather than more suppliers. And of course since the client is almost always the public, it should be a really good Open Source (OS) package. All (most of) the public agencies should probably get together and fund ongoing development of a single OS package. I know there have been discussions along these lines. I haven’t seen evidence of it actually happening. There are plenty of OS packages that can serve as foundations (MatSim being one example), Zephyr Foundation hosts others, though the website hasn’t been updated for a few years.
- Models: But the real problem is the models. The classic 1950s four-step model remains with us somehow. Agent-based models are an improvement (both theoretically and from a policy usefulness perspective – though not for forecasting) but are not yet standard. We need not just standardised open source software, but standardised open source models, with all of the details published for whoever wants to dismantle and critique them.
My general exasperation about this is “how hard can this be?” We were talking about this 30 years ago.
Despite the massive investments — I’d estimate in the billions of dollars globally in the seven decades since we began modelling, with hundreds of millions spent annually on the software, data collection and formatting, data sciences, and coding just to build models that can be applied for various projects — little progress has been made. The continuous tweaking, adding multipliers and constants, doesn’t seem to deliver more accurate results. We could of course spend that money better, but we should also ask why, if our models are so good, why don’t they transfer between cities? Are people really different, or just their environment?
But even if you get the models (er. for marketing purposes we call them Digital Twins) right for today, and reduce the inaccuracy to an acceptable level, you still haven’t solved the computational irreducibility problem. We cannot know the future without living through it. The real world is sufficiently complicated that even a model that perfectly replicated today would be no better than a random number generator in 40 years.
Society’s Adaptability and the Potential for Nowcasting
A few additional points. We should not get too fixated on minor differences in proposed projects.
- Transport models which do not endogenize changes in land use (and they cannot do that accurately either), will completely miss the adaptation process.
- Society invariably adapts to whatever infrastructure it finds. Travelers mold and reshape behaviours, and patterns in response to changes in the built environment, while the infrastructure and land development themselves are mutually causal.
- You build the bridge here, the town grows here. You build it there, the town grows there. In a 100 years, building the replacement bridge there instead of here won’t make sense, even if your model originally said there was better than here.
This observation brings to the forefront the idea of “nowcasting.” Instead of getting bogged down with forecasting, it might be more fruitful to focus on the present, analyzing the current implications of a proposed infrastructure project or land use change. That is, we take today’s conditions and insert the project and nothing else. We should be asking: does this work according to the preferred values today? How long is the payoff justifying the costs involved, both monetarily and socially? Things that pay off sooner (have a higher rate of return) are generally better than those that promise pay offs far into the future.
In this context, we should recognise that the wisdom of 19th century UK parliamentarians, who had lesser dependence on data and more on ground realities and human insights, and successfully picked winners for the London Underground, might sometimes prevail over today’s forecasting models.