Roozbeh Ketabi

TL;DR: This paper investigates the promise of deep learning by conducting a comparative analysis of conventional (seasonal) models, and multiple variants of recurrent neural models, based on 40 day-long traffic density data from 58 cameras in London, and introduces a spatial forecast model similar to the multivariate model.

TL;DR: The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types, and applies systematic analysis to wireless traces from a large university campus.

TL;DR: In this article, a novel combined spatio-temporal similarity score based on the weighted geometric mean (WGM) was proposed to find clusters of trips that were spatially and/or temporally separable using spectral clustering.

TL;DR: In this paper, the authors conduct a multidimensional analysis to quantitatively characterize mobility and traffic spatio-temporal patterns, for laptops and smartphones, leading to a detailed integrated mobility-traffic analysis.