Abstract
Urban rail transit demand analysis and forecasting is an essential prerequisite for daily operations and management. This paper categorizes the proposed demand forecasting methods, and focuses on traditional models, statistical models and machine learning approaches, according to their features and fields. Especially, influential and widely-used methods including the four-stage model, land use models, time series methods, Logit regression, Artificial Neural Networks (ANNs) and other referring methods are all taken into discussion.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference |
| Editors | Robert J. Howlett, Lakhmi C. Jain, Lakhmi C. Jain, Giuseppe De Pietro, Luigi Gallo, Lakhmi C. Jain, Ljubo Vlacic, Robert J. Howlett |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 300-309 |
| Number of pages | 10 |
| ISBN (Print) | 9783319922300 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018 - Gold Coast, Australia Duration: 20 Jun 2018 → 22 Jun 2018 |
Publication series
| Name | Smart Innovation, Systems and Technologies |
|---|---|
| Volume | 98 |
| ISSN (Print) | 2190-3018 |
| ISSN (Electronic) | 2190-3026 |
Conference
| Conference | 11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018 |
|---|---|
| Country/Territory | Australia |
| City | Gold Coast |
| Period | 20/06/18 → 22/06/18 |
Bibliographical note
Funding Information:Acknowledgement. This study is supported by the General Projects (No. 71771050) and Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Natural Science Foundation of Jiangsu Province in China (BK20150603).
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2019.
