Pursuing the impossible (?) dream: Incorporating attitudes into practice-ready travel demand forecasting models
本文提出用机器学习方法将态度变量归入家庭出行调查数据，实证表明归入的态度能提升预测模型的解释力和准确性，并建议未来调查加入少量态度题项。
• Offers practical ways to impute attitudes into household travel survey datasets. • Machine learning methods contribute to the proposed process. • Presents empirical results from four demonstration studies. • Imputed attitudes improved forecasting-oriented travel behavior models. • Supports including attitudinal statements in future household travel surveys. Despite the fact that our existing models are not up to the job of predicting travel behavior in today’s rapidly changing landscape, and despite considerable evidence that attitudes help us explain behavior more completely and more meaningfully, attitudes are nowhere to be found in practice-oriented travel demand forecasting models. Two main objections have been raised to their inclusion: they are too cumbersome to measure, and difficult-if-not-impossible to forecast. This paper reports on the considerable progress that has been made toward overcoming the first objection, through the use of machine learning methods to train a prediction function on smaller-scale research-oriented survey datasets, and then applying that function to impute attitudes into large-scale household travel survey datasets. Internal evaluations show that we can estimate attitudinal factor scores with moderate fidelity when using socioeconomic/demographic, land use, and targeted marketing variables, and with high fidelity when using just a few attitudinal marker variables. External evaluations demonstrate that the imputed attitudes lead to improved behavioral insight and predictive ability for forecasting-oriented models. With respect to the second objection I have only sketched some ideas for moving forward, but there are clearly some practical steps that could be taken at very little marginal cost, such as including as few as 10 attitudinal marker statements in future household travel surveys.