Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data

ABSTRACT

In this paper, we present a series of machine learning approaches for better understanding people’s travel mode choice. The widely used Logit model is dependent on the assumption that the utility items are independent, violating this assumption caused inconsistent parameter estimations and biased predictions. To improve the prediction accuracy of mode choice, this paper employs the data fusion model based on stacking strategy and proposes a hybrid model of the unsupervised Denoising Autoencoder (DAE) combining with the supervised Random Forest (RF). A variety of features that may impact mode choice behavior are ranked and selected by using the feature selection algorithms. The proposed model, which is particularly useful and powerful in the choice behavior analysis and outperforms other widely used classifiers, is verified by travel diary data from Germany and Switzerland. The results can be used for better understanding and effectively modeling of human travel mode choice behavior.

KEYWORDS:

• Travel behavior
• travel mode choice
• machine learning
• feature selection
• data mining

Acknowledgement

Authors would like to thank the anonymous reviewers for spending time to review our paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Jianjun Wu http://orcid.org/0000-0002-3762-0065

Huijun Sun http://orcid.org/0000-0002-3989-7391

Additional information

Funding

This paper is partly supported by the Fundamental Research Funds for the Central Universities [grant number 2018YJS192], the National Natural Science Foundation of China [grant numbers 71890972/71890970, 71621001, 91846202 and 71822102], Beijing Municipal Natural Science Foundation [grant number L181008].