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 allocation 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 considerably reduces the model complexity by decoupling the spatial-temporal structures of the sensing task, and exploring the non-uniqueness of the minimum fleet size problem. 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扫描城市，是城市交通活动产生的正向社会外部性。由于公交系统具有广泛的空间覆盖、可靠的运营和低维护成本等优势，在随车感知中常被考虑。对底层监测场景（如空气质量、交通状态和道路平整度）而言，在公交车队中分配有限数量的传感器以确保其最优时空分布至关重要。本文提出一种基于行程的传感器分配问题，该问题明确考虑了车队必须执行的定时班次，其中部分班次需执行传感任务。为解决大规模实例中的计算挑战，我们设计了一个多阶段求解框架，通过解耦传感任务的时空结构，并探索最小车队规模问题的非唯一性，显著降低了模型复杂度。基于成都市中心400平方公里区域的实际案例研究证明了该模型解决大规模问题的有效性。研究发现，协调公交调度与传感任务可大幅提升时空传感覆盖率。我们还针对该方法的应用提供了若干模型扩展与实践建议。