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 taxis, allowing urban environments to be opportunistically monitored. Inspired by the movements of real taxis, the taxi-drive is composed of both random and regular parts; passengers are brought to randomly chosen locations via deterministic (i.e. shortest paths) routes. We show through a numerical study that this hybrid motion endows the taxi-drive with advantageous spreading properties. In particular, on certain graph topologies it offers reduced cover times compared to persistent random walks.
@article{arxiv.1901.08678,
title = {Urban sensing as a random search process},
author = {Kevin O'Keeffe and Paolo Santi and Brandon Wang and Carlo Ratti},
journal= {arXiv preprint arXiv:1901.08678},
year = {2020}
}