CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network

TL;DR

CT-Mapper is an unsupervised algorithm that accurately maps sparse cellular trajectories onto a multilayer transportation network, leveraging physical transport properties and cellular coverage to improve mapping accuracy.

Contribution

The paper introduces CT-Mapper, a novel unsupervised method for mapping sparse multimodal cellular trajectories on multilayer transportation networks, outperforming naive models.

Findings

CT-Mapper accurately retrieves user paths despite sparse data.

Transition probability model is up to 20% more accurate than naive models.

Effective in a real-world setting with cellular and GPS data in Paris.

Abstract

Mobile phone data have recently become an attractive source of information about mobility behavior. Since cell phone data can be captured in a passive way for a large user population, they can be harnessed to collect well-sampled mobility information. In this paper, we propose CT-Mapper, an unsupervised algorithm that enables the mapping of mobile phone traces over a multimodal transport network. One of the main strengths of CT-Mapper is its capability to map noisy sparse cellular multimodal trajectories over a multilayer transportation network where the layers have different physical properties and not only to map trajectories associated with a single layer. Such a network is modeled by a large multilayer graph in which the nodes correspond to metro/train stations or road intersections and edges correspond to connections between them. The mapping problem is modeled by an unsupervised HMM where the observations correspond to sparse user mobile trajectories and the hidden states to the multilayer graph nodes. The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations. To evaluate CT-Mapper we collected cellular traces with their corresponding GPS trajectories for a group of volunteer users in Paris and vicinity (France). We show that CT-Mapper is able to accurately retrieve the real cell phone user paths despite the sparsity of the observed trace trajectories. Furthermore our transition probability model is up to 20% more accurate than other naive models.