Selection bias in build-operate-transfer transportation project appraisals

• Examine the possible existence of selection bias in BOT project appraisal process.
• Selection bias could be one of the factors contributing to inaccurate forecasts.
• Selection bias associated with the highest flow estimate criterion.
• Two approaches to quantify level and chance of inaccuracy caused by selection bias.

Recent empirical studies have found widespread inaccuracies in traffic forecasts despite the fact that travel demand forecasting models have been significantly improved over the past few decades. We suspect that an intrinsic selection bias may exist in the competitive project appraisal process, in addition to the many other factors that contribute to inaccurate traffic forecasts. In this paper, we examine the potential for selection bias in the governmental process of Build-Operate-Transfer (BOT) transportation project appraisals. Although the simultaneous consideration of multiple criteria is typically used in practice, traffic flow estimate is usually a key criterion in these appraisals. For the purposes of this paper, we focus on the selection bias associated with the highest flow estimate criterion. We develop two approaches to quantify the level and chance of inaccuracy caused by selection bias: the expected value approach and the probability approach. The expected value approach addresses the question “to what extent is inaccuracy caused by selection bias?”. The probability approach addresses the question “what is the chance of inaccuracy due to selection bias?”. The results of this analysis confirm the existence of selection bias when a government uses the highest traffic forecast estimate as the priority criterion for BOT project selection. In addition, we offer some insights into the relationship between the extent/chance of inaccuracy and other related factors. We do not argue that selection bias is the only reason for inaccurate traffic forecasts in BOT projects; however, it does appear that it could be an intrinsic factor worthy of further attention and investigation.