Towards Fine-Grained Spatio-Temporal Coverage for Vehicular Urban Sensing Systems

Vehicular urban sensing (VUS), which uses sensors mounted on crowdsourced vehicles or on-board drivers' smartphones, has become a promising paradigm for monitoring critical urban metrics. Due to various hardware and software constraints difficult for private vehicles to satisfy, for-hire vehicles (FHVs) are usually the major forces for VUS systems. However, FHVs alone are far from enough for fine-grained spatio-temporal sensing coverage, because of their severe distribution biases. To address this issue, we propose to use a hybrid approach, where a centralized platform not only leverages FHVs to conduct sensing tasks during their daily movements of serving passenger orders, but also controls multiple dedicated sensing vehicles (DSVs) to bridge FHVs' coverage gaps. Specifically, we aim to achieve fine-grained spatio-temporal sensing coverage at the minimum long-term operational cost by systematically optimizing the repositioning policy for DSVs. Technically, we formulate the problem as a stochastic dynamic program, and solve various challenges, including long-term cost minimization, stochastic demand with partial statistical knowledge, and computational intractability, by integrating distributionally robust optimization, primal-dual transformation, and second order conic programming methods. We validate the effectiveness of our methods using a real-world dataset from Shenzhen, China, containing 726,000 trajectories of 3848 taxis spanning overall 1 month in 2017.