Traffic Risk in PPPs, Part II: Bias in traffic forecasts—dealing with the darker side of PPPs

Photo: Susanne Nilsson| Flickr Creative Commons

This is the second of a three-part series on traffic PPPs.

"It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so."
– “The Big Short” 
 
Forecasting traffic accurately is a very difficult and thankless task, as I explained in my previous blog: Traffic Risk in Highway PPPs, Part I: Traffic Forecasting. As such, this gives rise to very real financial risks if these forecasts turn out to be wrong. This risk has crystallized many times as manifested in high-profile distressed projects, bankruptcies, renegotiations and bailouts.

So what’s driving the inaccuracy and resulting risk in traffic forecasts? In the Public-Private Infrastructure Advisory Facility (PPIAF) and Global Infrastructure Facility (GIF) publication, Toll Road PPPs: Identifying, Mitigating and Managing Traffic Risk, we postulate that forecasting inaccuracy comes from three sources:

• Error: The inaccuracies that result from the errors of the forecasting method itself and that are internal to the forecasting process. These are effectively the result of (involuntary) human error that occurs during the development of the traffic study and specifically occur when the traffic forecaster is trying to establish the existing demand for travel in the study area (the so-called “in-scope” traffic). Examples include sampling errors in traffic surveys and traffic model errors.
• Uncertainty: These are the inaccuracies that are typically out of the control of the traffic forecaster. They represent the changes in the external environment that occur over time and that were not foreseen or assumed at the time the traffic study forecasts were originally developed. Examples include large increases in fuel prices, economic recessions, or development of competing facilities.
• Bias: Traffic forecasts can be artificially high to facilitate a specific goal of a project party, for example, a bidder trying to develop a winning bid for a project or a government official trying to ensure a project achieves government approval.

Error and uncertainty should, in theory at least, cancel each other out. Forecasters, on average, are just as likely to be over-forecasting as under-forecasting because of errors and uncertainties in their forecasts. For example, you might over-count some vehicles at one count site, but just as likely under-count somewhere else.

But empirical evidence has shown that it is all a bit more nefarious than that. Many studies have found that traffic forecasts skew toward overestimation, indicating that systematic biases are present in the forecasting process.

Bias is therefore the real enemy in the forecasting process.  Error and uncertainty are just a reflection of our imperfect knowledge of the existing and future state, and as we stated in the first blog, we need to go easy on the transport economist fraternity on this. But bias, in its many forms, is something much more deliberate and has even led recently to legal proceedings against forecasting firms who stand accused of deliberately inflating traffic forecasts.

This is why we have given it special attention to bias in the second of the PPIAF Issue Briefs on traffic risk, Delusion, Distortion, and Curses: Bias in Traffic Forecasting, where we outline what causes bias and how governments can act to reduce it through the project cycle.

We hope you'll find it useful reading.

Read these next:

• See all PPIAF traffic risk briefs
• Visit the Transportation section of the PPP Knowledge Lab

Report

Toll-Roads PPPs: Identifying, Mitigating and Managing Traffic Risk
 

Related Posts

Traffic Risk in Highway PPPs, Part I: Traffic Forecasting — It’s ok to be wrong, just try to be less wrong

Traffic Risk in PPPs, Part III - Allocating Traffic Risk: Prophet & Loss