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$^2$ 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.

翻译：随车感知（即基于车辆的移动传感）是一种新兴的数据采集范式，通过利用车辆移动性以低成本实现城市感知。这构成了城市交通活动的积极社会外部性。公交车系统因其广泛的空间覆盖、可靠运行和低维护成本而被广泛用于随车感知研究。对于底层监测场景（如空气质量、交通状态和道路平整度），关键问题在于如何将有限数量的传感器分配给公交车队，以优化其时空分布。本文提出一种基于行程的传感器部署问题，该问题明确考虑了车队必须执行的计划班次，其中部分班次需执行感知任务。为应对大规模实例的计算挑战，我们设计了一个多阶段求解框架，通过线路预选和双层优化解耦感知任务的时空结构。由此，计算复杂度从现有车辆方法中与车辆数相关的组合复杂度降低为与线路数相关的次线性复杂度。以成都市核心区域400平方公里为对象的实际案例研究验证了该模型解决大规模问题的有效性。研究表明，协调公交调度与感知任务可显著提升时空感知覆盖率。我们还提供了若干模型扩展及该方法在实际应用中的实践建议。