Rethinking the regularity in mobility patterns of personal vehicle drivers: A multi-city comparison using a feature engineering approach |
| |
| Authors: | Yuhan Ji Song Gao Tam Huynh Chris Scheele James Triveri Jacob Kruse Collin Bennett Yicheng Wen |
| |
| Institution: | 1. Geospatial Data Science Lab, Department of Geography, University of Wisconsin-Madison, Madison, Wisconsin, USA;2. Arity, Chicago, Illinois, USA |
| |
| Abstract: | Large-scale trajectory data offer a finer lens into the regularity in individual mobility choices. Previous studies have exerted efforts to measure the regularity in people's location visiting patterns. However, the complexity of travel behavior at different spatial and temporal scales has not been adequately considered. To capture regularity in a more comprehensive manner, we construct human mobility profiles with interpretable features at three levels, that is, location, motif, and route, on personal vehicle drivers. A feature engineering approach is designed to analyze the extent to which individuals exhibit multi-level regularity. The analysis pipeline includes feature selection, user segmentation and profiling, and feature importance evaluation. Our empirical study analyzed over 4 million trips of 3743 personal vehicle drivers collected over a month in six metropolitan areas in the United States. The weak correlations between features confirm the validity of quantifying regularity from different aspects. We discovered five clusters of drivers (i.e., gig drivers, homebodies, movers, typical drivers, and work-focused commuters) that differ in their regularity to commute to the workplace and the inclination to participate in non-work activities. A similar driver segmentation and profiling pattern is found in all of the studied metro areas. The minor differences are interpreted from the distribution of mobility features and urban features. The proposed method using multi-level feature engineering provides a generic framework to study regularity and can be readily adapted to other mobility data sources by customizing the features. The improved understanding of mobility patterns within the built environment is valuable for innovating urban transportation solutions. |
| |
| Keywords: | |
|
|
