171 citations · 623 across the 28 of their papers we have counts for
4 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…
Estimating the potential for shared autonomous scooters
Dániel Kondor, Xiaohu Zhang, Malika Meghjani +3
Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of d…
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…