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:驱动器遥感(即基于车辆的移动遥感)是一种新兴的数据收集模式,它利用车辆的动员以低成本对城市进行扫描,是城市运输活动的一种积极的社会外向性。由于广泛的空间覆盖、可靠的操作和低维护成本,公共汽车中转系统在驾驶式遥感中得到广泛考虑。对于基本的监测情景(例如空气质量、交通状况和道路粗糙)来说,至关重要的是向公共汽车车队分配数量有限的传感器,以确保其最佳的时空分布。本文介绍了一个基于交通的传感器分配问题,明确考虑了车队必须执行的定时旅行,而其中一部分则执行的是遥感任务。为了应对大规模情况下的计算挑战,我们设计了一个多阶段解决方案框架,通过将遥感任务的空间时空结构脱钩,探索最低机队规模问题的非独特性,从而大大降低模型的复杂性。一个覆盖400千瓦2美元的现实世界案例研究展示了该模型在解决大规模实践问题方面的有效性。我们发现,在大规模实践上应用这一模型的时间安排上,可以大大地协调这一空间测量方法。