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How does inaccurate travel demand modeling forecasts affect transportation policy development or policy performance? 

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Inaccurate travel demand modeling forecasts can have significant implications for transportation policy development and policy performance. These forecasts, generated by AI-based models, may introduce prediction biases and fairness issues, potentially exacerbating social inequalities in decision-making processes . Limited studies have focused on addressing the fairness issues of these models, highlighting the need for fairness-aware travel demand forecasting methodologies . Additionally, research has shown that measured traffic is often lower than forecasted volumes, with a mean absolute deviation of 17% from the forecast . Factors such as road volume, functional class, time span, and the use of travel models can influence forecast accuracy . Furthermore, the presence of infrequently large prediction errors can impact the performance of demand-responsive public transport systems, highlighting the importance of accurate demand predictions for effective operation .

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The provided paper does not directly address the impact of inaccurate travel demand modeling forecasts on transportation policy development or policy performance.

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