Exploring the sensing power of mixed vehicle fleets
Ke Han, Wen Ji, Yu, Nie, Zhexian Li, Shenglin Liu
TL;DR
This paper formalizes the drive-by sensing coverage (DSC) problem to optimize sensing utility under budget by coordinating mixed fleets of taxis, buses, and dedicated vehicles. It introduces a space-time weighted sensing utility $\Phi$ based on the convex reformulations for taxi-bus subproblems and a dual-spatial-scale team orienteering approach for dedicated vehicles, enabling an alternating solution algorithm and a DV-routing framework. Empirical results in Longquanyi and transferability tests across three cities show that mixed fleets yield substantial performance gains (higher $\Phi$) and closer alignment to target sensing distributions with notable cost savings, particularly when bus networks are sparse or daytime activity is high. The findings offer practical guidance for urban sensing planning and reveal a positive externality of mobility on sensing power, with DV deployment providing meaningful boosts under various budget and temporal settings.
Abstract
Vehicle-based mobile sensing, also known as drive-by sensing, efficiently surveys urban environments at low costs by leveraging the mobility of urban vehicles. While recent studies have focused on drive-by sensing for fleets of a single type, our work explores the sensing power and cost-effectiveness of a mixed fleet that consists of vehicles with distinct and complementary mobility patterns. We formulate the drive-by sensing coverage (DSC) problem, proposing a method to quantify sensing utility and an optimization procedure that determines fleet composition, sensor allocation, and vehicle routing for a given budget. Our air quality sensing case study in Longquanyi District (Chengdu, China) demonstrates that using a mixed fleet enhances sensing utilities and achieves close approximations to the target sensing distribution at a lower cost. Generalizing these insights to two additional real-world networks, our regression analysis uncovers key factors influencing the sensing power of mixed fleets. This research provides quantitative and managerial insights into drive-by sensing, showcasing a positive externality of urban transport activities.