title: A multi-stage method for connecting participatory sensing and noise simulations.
abstract: Most simulation-based noise maps are important for official noise assessment but lack local noise characteristics. The main reasons for this lack of information are that official noise simulations only provide information about expected noise levels, which is limited by the use of large-scale monitoring of noise sources, and are updated infrequently. With the emergence of smart cities and ubiquitous sensing, the possible improvements enabled by sensing technologies provide the possibility to resolve this problem. This study proposed an integrated methodology to propel participatory sensing from its current random and distributed sampling origins to professional noise simulation. The aims of this study were to effectively organize the participatory noise data, to dynamically refine the granularity of the noise features on road segments (e.g., different portions of a road segment), and then to provide a reasonable spatio-temporal data foundation to support noise simulations, which can be of help to researchers in understanding how participatory sensing can play a role in smart cities. This study first discusses the potential limitations of the current participatory sensing and simulation-based official noise maps. Next, we explain how participatory noise data can contribute to a simulation-based noise map by providing (1) spatial matching of the participatory noise data to the virtual partitions at a more microscopic level of road networks; (2) multi-temporal scale noise estimations at the spatial level of virtual partitions; and (3) dynamic aggregation of virtual partitions by comparing the noise values at the relevant temporal scale to form a dynamic segmentation of each road segment to support multiple spatio-temporal noise simulations. In this case study, we demonstrate how this method could play a significant role in a simulation-based noise map. Together, these results demonstrate the potential benefits of participatory noise data as dynamic input sources for noise simulations on multiple spatio-temporal scales.
authors.affiliation: Shenzhen Research Institute, The Chinese University of Hong Kong, 2nd Yuexing Road, Nanshan District, Shenzhen 518057, China. humingyuan@gmail.com.
authors.forename: Mingyuan
authors.initials: M
authors.lastname: Hu
authors.affiliation: Shenzhen Research Institute, The Chinese University of Hong Kong, 2nd Yuexing Road, Nanshan District, Shenzhen 518057, China. cheweitao@cuhk.edu.hk.
authors.forename: Weitao
authors.initials: W
authors.lastname: Che
authors.affiliation: Department of Geo-information Processing, Faculty of ITC, University of Twente, Hengelosestraat 99, Enschede 7500 AE, The Netherlands. zhangqiuju0928@gmail.com.
authors.forename: Qiuju
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Center of Remote Sensing, Tianjin University, No. 92, Weijin Road, Tianjin 300072, China. luoqingli2003@163.com.
authors.forename: Qingli
authors.initials: Q
authors.lastname: Luo
authors.affiliation: Shenzhen Research Institute, The Chinese University of Hong Kong, 2nd Yuexing Road, Nanshan District, Shenzhen 518057, China. huilin@cuhk.edu.hk.
authors.forename: Hui
authors.initials: H
authors.lastname: Lin
mesh_terms: D002947:Cities; D016208:Databases, Factual; D004784:Environmental Monitoring; D006801:Humans; D009622:Noise