Logic-driven traffic big data analytics : methodology and applications for planning / Shaopeng Zhong, Daniel (Jian) Sun.
2022
HE336.A8 Z56 2022
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Logic-driven traffic big data analytics : methodology and applications for planning / Shaopeng Zhong, Daniel (Jian) Sun.
Author
Zhong, Shaopeng, author.
ISBN
9789811680168 (electronic bk.)
9811680167 (electronic bk.)
9789811680151
9811680159
9811680167 (electronic bk.)
9789811680151
9811680159
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (chiefly color)
Item Number
10.1007/978-981-16-8016-8 doi
Call Number
HE336.A8 Z56 2022
Dewey Decimal Classification
388.3/14
Summary
This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big datas impact on mobility patterns and urban planning.
Note
Includes index.
Access Note
Access limited to authorized users.
Added Author
Sun, Daniel, Jian, author.
Available in Other Form
Print version: 9789811680151
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
Logic driven traffic big data analytics: An introduction
Statistical models and methods
Spatial-temporal distribution model for travel origin-destination based on multi-source data
Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data
A ride-sourcing group prediction model based on convolutional neural network.
Statistical models and methods
Spatial-temporal distribution model for travel origin-destination based on multi-source data
Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data
A ride-sourcing group prediction model based on convolutional neural network.