Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression
文献类型：期刊论文
| 作者 | Li, Bozhao1; Cai, Zhongliang1,2; Jiang, Lili3 ; Su, Shiliang1,2,4; Huang, Xinran1
|
| 刊名 | CITIES
 |
| 出版日期 | 2019-04-01 |
| 卷号 | 87页码:68-86 |
| ISSN号 | 0264-2751 |
| 关键词 | Taxi ridership Taxi trajectory data Urban mobility GWR Traffic source and sink places |
| DOI | 10.1016/j.cities.2018.12.033 |
| 通讯作者 | Cai, Zhongliang(zlcai@whu.edu.cn) ; Su, Shiliang(shiliangsu@whu.edu.cn) |
| 英文摘要 | Taxi is a core component of urban transit systems. Since they can provide more time-saving and convenient service than many other transit options, taxis have a certain passenger base. The analysis of taxi ridership can be used to better understand the travel mobility of passengers and the traffic structure of urban areas. In previous studies, taxi trajectory data have been widely used, especially in exploring taxi ridership, and point-of-interest (POI) data are usually used to evaluate the land-use type of a certain sub-district. On the basis of preceding research, this paper uses taxi trajectory data within the long time scale of one week. Five traffic factors are taken into consideration: pick-ups, drop-offs, and the ratio of pick-ups to drop-offs, pick-up probability and drop-off probability. The research model is divided into weekdays and weekends. For the calculation of probabilities, an index termed the Area Crossing Index is proposed to reflect the taxi cardinality and accessibility of a region. At the same time, POI and demographic data are used as explanatory variables. In this study, we also take the business hours of POIs into consideration. In order to explore the ridership in each hour, hierarchical clustering is used to determine the similarity characteristics of hourly dependent variables. Then, stepwise linear regression is used to screen and evaluate coefficients without collinearity. Finally, geographically weighted regression is adopted to evaluate spatial variability, and the coefficients of common explanatory variables on weekdays and weekends are examined. At the end of this paper, the causes of common explanatory factors on weekdays and weekends for each traffic factor are discussed. This paper also analyzes ridership by combining all the results of dependent variables and proposes some suggestions for taxi scheduling. |
| WOS关键词 | HUMAN MOBILITY PATTERNS ; TRAVEL PATTERNS ; TRANSIT RIDERSHIP ; SCALING LAWS ; LOCATIONS ; VEHICLE ; PREDICTION ; EMISSIONS ; FEATURES ; SUBWAY |
| 资助项目 | National Key Research and Development Program of China[2017YFB0503500] |
| WOS研究方向 | Urban Studies |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000463130100008 |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/48273]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Cai, Zhongliang; Su, Shiliang |
| 作者单位 | 1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China 2.Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan, Hubei, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 4.Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan, Hubei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Bozhao,Cai, Zhongliang,Jiang, Lili,et al. Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression[J]. CITIES,2019,87:68-86. |
| APA | Li, Bozhao,Cai, Zhongliang,Jiang, Lili,Su, Shiliang,&Huang, Xinran.(2019).Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression.CITIES,87,68-86. |
| MLA | Li, Bozhao,et al."Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression".CITIES 87(2019):68-86. |
入库方式： OAI收割
来源：地理科学与资源研究所
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