Mining User Behaviour from Smartphone data: a literature review
arXiv:1912.11259 · doi:10.1186/s12544-021-00516-z
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.
References in corpus (7)
- The map equation
- Inferring transportation modes from GPS trajectories using a convolutional neural network
- Deep Learning is Robust to Massive Label Noise
- Noise Tolerance under Risk Minimization
- The Long Road to Computational Location Privacy: A Survey
- TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
- semMatch: Road Semantics-based Accurate Map Matching for Challenging Positioning Data