Exploring the sensing power of mixed vehicle fleets

Ke Han, Wen Ji, Nie, Yu (Marco), Zhexian Li and Shenglin Liu

Transportation Research Part B: Methodological, 2024, vol. 190, issue C

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.

Keywords: Drive-by sensing; Mixed fleet; Vehicle mobility; Optimization; Social externality (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.trb.2024.103066

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