Mining User Behaviour from Smartphone data: a literature review

TL;DR

This literature review examines the challenges and potential of smartphone-based travel surveys, emphasizing data quality issues, methodological frameworks, and the critical role of ground truth in user behavior analysis.

Contribution

It provides a comprehensive analysis of the limitations, challenges, and considerations for deploying smartphone data in travel behavior research, offering practical recommendations.

Findings

Data quality critically impacts algorithm performance.

Physical device limitations affect data collection.

Ground truth accuracy influences travel diary generation.

Abstract

To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, computer intelligence algorithms take the role that operators have in Traditional Travel Surveys; since we train each algorithm on data, performances rest on the data quality, thus on the ground truth. Inaccurate validations affect negatively: labels, algorithms' training, travel diaries precision, and therefore data validation, within a very critical loop. Interestingly, boundaries are proven burdensome to push even for Machine Learning methods. To support optimal investment decisions for practitioners, we expose the drivers they should consider when assessing what they need against what they get. This paper highlights and examines the critical aspects of the underlying research and provides some recommendations: (i) from the device perspective, on the main physical limitations; (ii) from the application perspective, the methodological framework deployed for the automatic generation of travel diaries; (iii)from the ground truth perspective, the relationship between user interaction, methods, and data.