Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. Existing work on trajectory clustering often relies on similarity measurements that utilize raw spatial and/or temporal information of trajectories. These measures are incapable of identifying similar moving behaviors that exhibit varying spatiotemporal scales of movement. In addition, the expense of labeling massive trajectory data is a barrier to supervised learning models. To address these challenges, we propose an unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT). DETECT operates in three parts: first it transforms the trajectories by summarizing their critical parts and augmenting them with context derived from their geographical locality (e.g., using POIs from gazetteers). In the second part, it learns a powerful representation of trajectories in the latent space of behaviors, thus enabling a clustering function (such as k-means) to be applied. Finally, a clustering oriented loss is directly built on the embedded features to jointly perform feature refinement and cluster assignment, thus improving separability between mobility behaviors. Exhaustive quantitative and qualitative experiments on two real-world datasets demonstrate the effectiveness of our approach for mobility behavior analyses.

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Traffic incidents and their impacts have been largely studied to improve road safety and to reduce incurred life and economic losses. However, the inaccuracy of incident data collected from transportation agencies, especially the start time, poses a great challenge to traffic incident research. We present INFIT, a system that infers the incident start time utilizing traffic data collected by loop sensors. The core of INFIT is IIG, our newly developed inference algorithm. The key idea is that IIG considers the traffic speed at multiple upstream locations, to mitigate the randomness in traffic data and to distinguish among multiple impact factors. IN-FIT includes an interactive interface with real-world incident datasets. We demonstrate INFIT with three exploratory use cases and show the usefulness of our inference algorithms.