| 书目名称 | Mobility Data-Driven Urban Traffic Monitoring |
| 编辑 | Zhidan Liu,Kaishun Wu |
| 视频video | |
| 概述 | Elaborates the basic workflow of mobility data-based urban traffic monitoring.Discusses future research directions on mobility data-driven traffic analysis.Solves real-world traffic problems with emer |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 描述 | .This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring...This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-basedurban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing fram |
| 出版日期 | Book 2021 |
| 关键词 | Urban traffic monitoring; Mobility data; Intelligent Transportation System; Traffic modelling; Compress |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-16-2241-0 |
| isbn_softcover | 978-981-16-2240-3 |
| isbn_ebook | 978-981-16-2241-0Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 |
发表于 2025-3-21 16:13:04
