Description
| Product ID: | 9780443184284 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | NL |
| Title: | Handbook of Mobility Data Mining, Volume 1 |
| Subtitle: | Data Preprocessing and Visualization |
| Authors: | Author: Haoran Zhang |
| Page Count: | 222 |
| Subjects: | Social and ethical issues, Social issues & processes, Operational research, Transport: general interest, Operational research, Transport: general interest |
| Description: | Select Guide Rating Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. Further, the book introduces how to design MDM platforms that adapt to the evolving mobility environment, new types of transportation, and users based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This volume focuses on how to efficiently pre-process mobile big data to extract and utilize critical feature information of high-dimensional city people flow. The book first provides a conceptual theory and framework, then discusses data sources, trajectory map-matching, noise filtering, trajectory data segmentation, data quality assessment, and more, concluding with a chapter on privacy protection in mobile big data mining. |
| Imprint Name: | Elsevier - Health Sciences Division |
| Publisher Name: | Elsevier - Health Sciences Division |
| Country of Publication: | GB |
| Publishing Date: | 2023-01-26 |
