Abstract Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means of surveying urban environment by leveraging the mobility of public or private transport vehicles. Buses, for their extensive spatial coverage and reliable operations, have received much attention in drive-by sensing. Existing studies have focused on the assignment of sensors to a set of lines or buses with no operational intervention, which is typically formulated as set covering or subset selection problems. This paper aims to boost the sensing power of bus fleets through active scheduling, by allowing instrumented buses to circulate across multiple lines to deliver optimal sensing outcome. We consider a fleet consisting of instrumented and normal buses, and jointly optimize sensor assignment, bus dispatch, and intra- or inter-line relocations, with the objectives of maximizing sensing quality and minimizing operational costs, while serving all timetabled trips. By making general assumptions on the sensing utility function, we formulate the problem as a nonlinear integer program based on a time-expanded network. A batch scheduling algorithm is developed following linearization techniques to solve the problem efficiently, which is tested in a real-world case study in Chengdu, China. The results show that the proposed scheme can improve the sensing objective by 12.0%–20.5% (single-line scheduling) and 16.3%–32.1% (multi-line scheduling), respectively, while managing to save operational costs by 1.0%. Importantly, to achieve the same level of sensing quality, we found that the sensor investment can be reduced by over 33% when considering active bus scheduling. Comprehensive comparative and sensitivity analyses are presented to generate managerial insights and recommendations for practice.

Highlights • Bus drive-by sensing is a promising way to survey urban environment at low cost. • Studies have focused on sensor deployment without intervening bus operations. • We explore the sensing power of bus fleets unlocked by multi-line scheduling. • This allows sensors to be optimally circulated while timetabled trips are served. • A real-world case study reveals its cost-effectiveness and managerial insights.