Understanding human mobility and trip demand through sparse trajectories of private e-bikes
文献类型：期刊论文
| 作者 | Wang, Peixiao5; Zhang, Hengcai5; Cheng, Shifen5; Lu, Feng4,5; Zhang, Tong3; Chen, Zeqiang2 |
| 刊名 | JOURNAL OF CLEANER PRODUCTION
 |
| 出版日期 | 2024-09-15 |
| 卷号 | 471页码:143444 |
| 关键词 | Human mobility Private e-bike trajectories e-bike trip Model interpretability Driving mechanisms |
| DOI | 10.1016/j.jclepro.2024.143444 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Understanding human mobility and trip demand through e-bike trajectories is crucial for urban planning, environmental enhancement, and sustainable development. However, existing studies predominantly focus on shared (e-)bike trips, neglecting private e-bike trips. With the recent availability of sparse trajectory for private e-bikes, we established a novel analysis framework to reveal human mobility and trip demand in Wuhan, China. First, we propose a two-step method for extracting trip behavior from sparse trajectories of private e-bikes, involving the identification of staying areas and the generation of e-bike trips. Second, we establish a spatial random forest method to capture the nonlinear relationship between private e-bike trips and driving factors. Finally, we use the interpretable SHAP method to reveal the driving mechanisms of e-bike trips and explore the impact of various factors on these trips. The results indicate that (1) trip distances of private e-bikes follow a lognormal distribution, with an Adj. R-Square of 0.99, while trip times exhibit a Hill distribution, with an Adj. RSquare of 0.95; (2) Private e-bike trips are not commonly employed to address the first/last mile problem in public transportation and are more frequently used for daily commuting needs, with over 65% of these trips covering distances greater than 1 km or lasting longer than 5 min; (3) private e-bike trips positively correlate with the density of POIs like Hospital, School, and Transportation Station. However, compared to shared (e-)bike trips, Transportation Station Density, especially Metro Station Density, is less important for private e-bike trips; and (4) private e-bike trips are also positively correlated with Congestion Level and House Price, meaning that areas with severe traffic congestion or high housing prices tend to have more private e-bike trips. This study provides a new framework for understanding private e-bike trip patterns, also helping authorities better grasp the factors influencing e-bike trip demand. |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| WOS记录号 | WOS:001301725900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207940]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhang, Hengcai |
| 作者单位 | 1.China Univ Geosci, Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China 2.Wuhan Univ, State Key Lab Informat Engn Surveying, Mapping & Remote Sensing, Wuhan 430079, Peoples R China 3.Fujian Collaborat Innovat Ctr Big Data Applicat Go, Fuzhou 350003, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,et al. Understanding human mobility and trip demand through sparse trajectories of private e-bikes[J]. JOURNAL OF CLEANER PRODUCTION,2024,471:143444. |
| APA | Wang, Peixiao,Zhang, Hengcai,Cheng, Shifen,Lu, Feng,Zhang, Tong,&Chen, Zeqiang.(2024).Understanding human mobility and trip demand through sparse trajectories of private e-bikes.JOURNAL OF CLEANER PRODUCTION,471,143444. |
| MLA | Wang, Peixiao,et al."Understanding human mobility and trip demand through sparse trajectories of private e-bikes".JOURNAL OF CLEANER PRODUCTION 471(2024):143444. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。