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 plus or minus 20%; for 25% of road projects, the difference is larger than plus or minus 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 in forecasts are different for rail and road projects, with political causes playing a larger role for rail than for road. The cure is more accountability and reference class forecasting. Highly inaccurate traffic forecasts combined with large standard deviations translate into large financial and economic risks. But such risks are typically ignored or downplayed by planners and decision-makers, to the detriment of social and economic welfare. The paper presents the data and approach with which planners may begin valid and reliable risk assessment. (Author/publisher)
Abstract