Abstract Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus transit systems are widely considered in drive-by sensing due to extensive spatial coverage, reliable operations, and low maintenance costs. It is critical for the underlying monitoring scenario (e.g. air quality, traffic state, and road roughness) to assign a limited number of sensors to a bus fleet to ensure their optimal spatial–temporal distribution. In this paper we present a trip-based sensor deployment problem, which explicitly considers timetabled trips that must be executed by the fleet while a portion of them perform sensing tasks. To address the computational challenge in large-scale instances, we design a multi-stage solution framework that decouples the spatial–temporal structures of the sensing task through line pre-selection and bi-level optimization. As a result, the computational complexity is reduced to be sub-linear w.r.t. the number of lines, rather than combinatorial w.r.t. the number of buses in existing vehicle-based approaches. A real-world case study covering 400 km in central Chengdu demonstrates the effectiveness of the model in solving large-scale problems. It is found that coordinating bus scheduling and sensing tasks can substantially increase the spatial–temporal sensing coverage. We also provide a few model extensions and recommendation for practice regarding the application of this method.
Highlights Bus drive-by sensing is a promising way to survey urban environment at low costs. Existing vehicle-based sensor deployment is a combinatorial problem. A trip-based model considers timetabled trips while performing sensing tasks. A multi-stage solution algorithm yields sub-linear complexity w.r.t. the number of lines. A real-world case study reveals its effectiveness and managerial insights.
Trip-based mobile sensor deployment for drive-by sensing with bus fleets
2023-10-30
Article (Journal)
Electronic Resource
English
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