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首页> 外文会议>2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems >Machine learning or discrete choice models for car ownership demand estimation and prediction?
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Machine learning or discrete choice models for car ownership demand estimation and prediction?

机译：使用机器学习或离散选择模型来估算和预测汽车拥有量？

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Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. They show how some distinct choice of human behavior or preference influences a decision. They are also used to project future demand estimates to support policy exploration. This latter use for prediction is indirectly aligned with and conditional to the model's estimation which aims to fit the observed data. In contrast, machine learning models are derived to maximize prediction accuracy through mechanisms such as out-of-sample validation, non-linear structure, and automated covariate selection, albeit at the expense of interpretability and sound behavioral theory. We investigate how machine learning models can outperform discrete choice models for prediction of car ownership using transportation household survey data from Singapore. We compare our household car ownership model (multinomial logit model) against various machine learning models (e.g. Random Forest, Support Vector Machines) by using 2008 data to derive, i.e. estimate models that we then use to predict 2012 ownership. The machine learning models are inferior to the discrete choice model when using discrete choice features. However, after engineering features more appropriate for machine learning they are superior. These results highlight both the cost of applying machine learning models in econometric contexts and an opportunity for improved prediction and better urban policy making through machine learning models with appropriate features.
机译：离散选择模型广泛用于解释运输行为，包括家庭决定拥有汽车的行为。它们显示了人类行为或偏好的某些独特选择如何影响决策。它们还用于预测未来需求估算以支持政策探索。后一种用于预测的方法与旨在适合观察数据的模型估计间接地对齐并以其为条件。相比之下，尽管以可解释性和合理的行为理论为代价，但机器学习模型的派生是通过样本外验证，非线性结构和自动协变量选择等机制来最大化预测准确性。我们使用新加坡的运输家庭调查数据来研究机器学习模型如何胜过预测汽车拥有量的离散选择模型。我们使用2008年的数据来得出家用汽车的拥有权模型（多项式logit模型）与各种机器学习模型（例如，Random Forest，Support Vector Machines）进行比较，即估算模型，然后将其用于预测2012年的所有权。使用离散选择功能时，机器学习模型不如离散选择模型。但是，在更适合于机器学习的工程特性之后，它们变得更加出色。这些结果既强调了在计量经济学环境中应用机器学习模型的成本，也强调了通过具有适当功能的机器学习模型来改进预测和制定更好的城市政策的机会。

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