Knowledge Commons of Institute of Automation,CAS
Urban Trip Generation Forecasting Based on Gradient Boosting Algorithm | |
Zhishuai Li1,2; Gang Xiong1; Yu Zhang3; Meng Zheng3; Xisong Dong1; Yisheng Lv1,2 | |
2021-09-22 | |
会议名称 | 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) |
会议日期 | 2021-7-15 |
会议地点 | Beijing, China |
摘要 | The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48738 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Yisheng Lv |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Beijing Municipal Institute of City Planning and Design |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhishuai Li,Gang Xiong,Yu Zhang,et al. Urban Trip Generation Forecasting Based on Gradient Boosting Algorithm[C],2021. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Urban_Trip_Generatio(345KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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