23 citations · 30 across the 13 of their papers we have counts for
5 papers · 1 filter
Using reinforcement learning to minimize taxi idle times
Kevin O'Keeffe, Sam Anklesaria, Paolo Santo +1
Taxis spend a significant amount of time idle, searching for passengers. The routes vacant taxis should follow in order to minimize their idle times are hard to calculate; they dep…
Modeling vehicular mobility patterns using recurrent neural networks
Kevin O'Keeffe, Paolo Santi, Carlo Ratti
Data on vehicular mobility patterns have proved useful in many contexts. Yet generative models which accurately reproduce these mobility patterns are scarce. Here, we explore if re…
On the Use of ArXiv as a Dataset
Colin B. Clement, Matthew Bierbaum, Kevin P. O'Keeffe +1
The arXiv has collected 1.5 million pre-print articles over 28 years, hosting literature from scientific fields including Physics, Mathematics, and Computer Science. Each pre-print…
A review of swarmalators and their potential in bio-inspired computing
Kevin O'Keeffe, Christian Bettstetter
From fireflies to heart cells, many systems in Nature show the remarkable ability to spontaneously fall into synchrony. By imitating Nature's success at self-synchronizing, scienti…
Urban sensing as a random search process
Kevin O'Keeffe, Paolo Santi, Brandon Wang +1
We study a new random search process: the \textit{taxi-drive}. The motivation for this process comes from urban sensing, in which sensors are mounted on moving vehicles such as tax…