Urban sensing as a random search process

Kevin O’Keeffe, Paolo Santi, Brandon Wang, CarloRatti

Physica A: Statistical Mechanics and its Applications
Volume 562, 15 January 2021, 125307

We study a new random search process: the 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 random walks and persistent random walks.

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