A comparative study of machine learning classifiers for modeling travel mode choice

By Julian Hagenauer, Marco Helbich. Expert systems with applications, 2017

400 citations recorded by the source corpus.

Subfields: Transportation

View Paper: https://www.semanticscholar.org/paper/431781a14131dbed081123606ad48c34c325963e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Transportation

1. Xiang Yan
2. Alan Yu
3. Xilei Zhao
4. Pascal Van Hentenryck

Citations in the corpus

• Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020, methodology): “For example, Hagenauer and Helbich (2017) compared the variable importance results of machine-learning methods with MNL. Cheng et al. (2019) applied variable importance to evaluate the relative importance of explanatory variables of the RF model to help provide important insights into formulating…”
• Tackling Climate Change with Machine Learning (2019, neutral): “[219] Julian Hagenauer and Marco Helbich.”
• The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations (2021, background): “2019) and weighing the variables associated with different travel modes (Hagenauer and Helbich 2017), and optimising electric vehicle sharing (Miao et al.”