English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study compares the performance of a causal and a predictive model inmodeling travel mode choice in three neighborhoods in Chicago. A causaldiscovery algorithm and a causal inference technique were used to extract thecausal relationships in the mode choice decision making process and to estimatethe quantitative causal effects between the variables both directly fromobservational data. The model results reveal that trip distance and vehicleownership are the direct causes of mode choice in the three neighborhoods.Artificial neural network models were estimated to predict mode choice. Theiraccuracy was over 70%, and the SHAP values obtained measure the importance ofeach variable. We find that both the causal and predictive modeling approachesare useful for the purpose they serve. We also note that the study of modechoice behavior through causal modeling is mostly unexplored, yet it couldtransform our understanding of the mode choice behavior. Further research isneeded to realize the full potential of these techniques in modeling modechoice.

Causation versus Prediction: Comparing Causal Discovery and Inference with Artificial Neural Networks in Travel Mode Choice Modeling