3,369 research outputs found
Exploring the drive-by sensing power of bus fleet through active scheduling
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.Comment: 32 pages, 13 figures, 8 table
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