多离散连续（MDC）选择分析的深度神经网络和结构计量经济学模型的比较分析.pdf

1、COLLABORATE.INNOVATE.EDUCATE.A Comparative Analysis of Deep Neural Network and Structural Econometric Models for Multiple Discrete-Continuous(MDC)Choice Analysis1XYZ Xth,2019June,2023Aupal MondalChandra R.BhatCOLLABORATE.INNOVATE.EDUCATE.2IntroductionThere is an emerging trend of using machine learn

2、ing models,to analyze individual decisions,in consumer behavior and transportation-related studies.These machine learning(ML)models are consistently found to achieve much higher predictive performance compared to the traditional discrete choice models(DCM).Deep Neural Network(DNN)and Random Forest(R

3、F)algorithms,in particular,are found to regularly outperform any other ML models and DCMs in predictions.COLLABORATE.INNOVATE.EDUCATE.PredictionsInterpretability3Introduction There is a constant debate about the computation perspectives and prediction accuracy offered by ML methods and the interpret

4、ability and behavioral foundations ingrained in theory-driven choice models.Theory-driven choice models based on economic and domain-specific theory,emphasize interpretability,make explicit assumptions,can work with smaller datasetsMachine Learning models “black-box”models,prioritize predictive accu

5、racy,can learn complex patterns,generally require large datasets.COLLABORATE.INNOVATE.EDUCATE.4IntroductionA few studies have attempted to address the interpretability issue in ML models,while a few have focused on utilizing a“synergistic”approach to harness the interpretability of theory-driven cho

6、ice models and the predictive accuracy of ML-based models.However,most earlier explorations compare the ML-based methods to the simple MNL model as the“strawman”.In any case,comparative studies on ML models versus theory-driven choice models have primarily focused on single discrete choice models.Cu