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How (In)accurate are demand forecasts in public works projects?”
- Journal of the American
, 2005
"... Abstract This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical signi ..."
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Cited by 76 (6 self)
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Abstract This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risks. Such risks are typically ignored or downplayed by planners and decision makers, to the detriment of social and economic welfare. For nine out of ten rail projects passenger forecasts are overestimated; average overestimation is 106 percent. This results in large benefit shortfalls for rail projects. For half of all road projects the difference between actual and forecasted traffic is more than ±20 percent. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts have improved over time, as often claimed by forecasters, this does not show in the data. The causes of inaccuracy in forecasts are different for rail and road projects, with political causes playing a larger role for rail than for road. The cure is transparency, accountability, and new forecasting methods. The challenge is to change the governance structures for forecasting and project development. The article shows how planners may help achieve this.
Inaccuracy in Traffic Forecasts
- Transport Reviews
, 2006
"... ABSTRACT This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical signific ..."
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Cited by 16 (0 self)
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ABSTRACT This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risk. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts have improved over time, as often claimed by forecasters, this does not show in the data. For nine out of ten rail projects, passenger forecasts are overestimated; average overestimation is 106%. For 72 % of rail projects, forecasts are overestimated by more than two-thirds. For 50 % of road projects, the difference between actual and forecasted traffic is more than ±20%; for 25 % of road projects, the difference is larger than ±40%. Forecasts for roads are more accurate and more balanced than for rail, with no significant difference between the frequency of inflated versus deflated forecasts. But for both rail and road projects, the risk is substantial that demand forecasts are incorrect by a large margin. The causes of inaccuracy
The Case of Transportation
"... This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth U.S.$59 billion. The study shows with very high statistical significance ..."
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This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth U.S.$59 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. For 9 out of 10 rail projects, passenger forecasts are overestimated; the average overestimation is 106%. For half of all road projects, the difference between actual and forecasted traffic is more than ±20%. The result is substantial financial risks, which are typically ignored or downplayed by planners and decision makers to the detriment of social and economic welfare. Our data also show that forecasts have not become more accurate over the 30-year period studied, despite claims to the contrary by forecasters. The causes of inaccuracy in forecasts are different for rail and road projects, with political causes playing a larger role for rail than for road. The cure is transparency, accountability, and new forecasting methods. The challenge is to change the governance structures for forecasting and project development. Our article shows how planners may help achieve this. Bent Flyvbjerg is a professor of planning at Aalborg University, Denmark. He is founder and director of the university’s research program on large-scale infrastructure planning. His latest books are
DOI: 10.1080/01441640500124779 Inaccuracy in Traffic Forecasts
, 2005
"... ABSTRACT This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical signific ..."
Abstract
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ABSTRACT This paper presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$58 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risk. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts have improved over time, as often claimed by forecasters, this does not show in the data. For nine out of ten rail projects, passenger forecasts are overestimated; average overestimation is 106%. For 72 % of rail projects, forecasts are overestimated by more than two-thirds. For 50 % of road projects, the difference between actual and forecasted traffic is more than ±20%; for 25 % of road projects, the difference is larger than ±40%. Forecasts for roads are more accurate and more balanced than for rail, with no significant difference between the frequency of inflated versus deflated forecasts. But for both rail and road projects, the risk is substantial that demand forecasts are incorrect by a large margin. The causes of inaccu-
decisions about building transport infrastructure are based on reliable information, then it is exactly the
"... www.elsevier.com/locate/tra Transportation Research Part A 39 (2005) 522–5300965-8564/ $- see front matter 2005 Elsevier Ltd. All rights reserved.traffic forecasted at the time of making the decision to build that is of interest. Second, although ideally studies should take into account so-called d ..."
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www.elsevier.com/locate/tra Transportation Research Part A 39 (2005) 522–5300965-8564/ $- see front matter 2005 Elsevier Ltd. All rights reserved.traffic forecasted at the time of making the decision to build that is of interest. Second, although ideally studies should take into account so-called demand ‘‘ramp up’ ’ over a period of years, the empirical evidence and practical considerations do not support this ideal requirement, at least not for large-N studies. Finally, the paper argues that large samples of inaccuracy in travel demand forecasts are likely to be conservatively biased, i.e., accuracy in travel demand forecasts estimated from such samples would likely be higher than accuracy in travel demand forecasts in the project population. This bias must be taken into account when interpreting the results from statistical analyses of inaccuracy in travel demand forecasting.
