General Attitudes and Mode Choice : A mode choice study in Stockholm using Schwartz value-items grouped by personal characteristics

Sammanfattning: Value-items from the Schwartz scale of Values have been added to travel data to investigate if the value-items can be used to model mode choice. Two kinds of mode choice models, both discrete choice models, multinomial models (MNL) and the Machine Learning Models Random Forests (RF) were constructed, using Travel Diary data (RVU) and additional data from European Social Survey (ESS). The additional data was connected to the base data by grouping the individuals using three key variables: gender, age, and household income. Models were then created with and without any data from the value-items to screen for variables that had an impact on the model. The RF model predicted the correct modes for all but the smaller groups, car passengers and biking. While the MNL model had less success accurately assessing which mode someone had chosen. The MNL with additional grouped value-items improved, while the models created using Random Forest had no difference in accuracy based on the addition. Even though there were some significant value-items in the MNL-models, the expected consequences from them small, as the base model specification might be insufficient in incorporating more relevant variables. Based on the Random Forest having no use from the value-items along with them being of similar importance no value-items stood out for further testing. The main findings were thus that no value-items of particular interest could be found with the RF model while the results for the MNL-model were inconclusive. Suggested improvements for further similar studies would be to perform grouping using data for a longer time frame and or to use a value-model as input for the mode choice modelling. It is deemed appropriate to study what values people associate with specific modes directly, and to investigate if other models such as car ownership models or models of choices between different versions of the same mode could be more suitable for additional value-data.