原文传递
Decision Tree Based Station-Level Rail Transit Ridership Forecasting
| 题名： | Decision Tree Based Station-Level Rail Transit Ridership Forecasting |
| 其他题名： | Arlot,S.,and Celisse,A.(2010)."A survey of cross-validation procedures for model selection."Stat.Surv.,4,40–79. |
| 正文语种： | 英文 |
| 作者： | Xin Li |
| 关键词： | Urban rail transit station;Demand forecasting;Land-use patterns;Decision tree;Canonical correlation analysis (CCA) |
| 摘要： | This paper presents a decision-tree based model to forecast rail transit ridership at the station level according to the surrounding land-use patterns. The canonical correlation analysis (CCA) method is used to identify key land use variables by evaluating their degrees of contribution to the rail transit station demand, which can effectively reduce dimensionality and complexity of the decision tree. A full month of Smart Card data and detailed regulatory land use plan from Chongqing, China are collected for model development and validation. The proposed model offers the capability of targeting key lane use patterns and associating them with rail transit station boarding and alighting demand at a high level of accuracy. The proposed model can reveal underlying rules between rail transit station demand and land use variables, and can be used to assist in developing the Transit Oriented Development (TOD) plans to improve land use and transit operational efficiency. |
| 出版年： | 2016 |
| 论文唯一标识： | P-84Y2016V142N04006 |
| 英文栏目名称： | TECHNICAL PAPAERS |
| doi： | 10.1061/(ASCE)UP.1943-5444.0000331 |
| 期刊名称： | Journal of Urban Planning and Development |
| 拼音刊名(出版物代码)： | P-84 |
| 卷： | 142 |
| 期： | 04 |
| 页码： | 40-48 |
相关文献
- Improved Approach for Forecasting Extra-Peak Hourly Subway Ridership at Station-Level Based on LASSO
- Spatiotemporal Heterogeneity Analysis of Influence Factor on Urban Rail Transit Station Ridership
- Ridership Prediction of Urban Rail Transit Stations Based on AFC and POI Data
- How to Increase Rail Ridership in Maryland: Direct Ridership Models for Policy Guidance
- Clustering Analysis of Ridership Patterns at Subway Stations: A Case in Nanjing, China
- Network and station-level bike-sharing system prediction: a San Francisco bay area case study
检索历史
应用推荐
