Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression
Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression
复制标题
使用地理和时间加权回归研究城市环境对出租车乘客的时空影响
DOI：
10.3390/ijgi8010023
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发表时间：
2019-01
影响因子：
3.4
通讯作者：
Shunzhi Zhu
中科院分区：
文献类型：
--
作者：
Xinxin Zhang;Bo Huang;Shunzhi Zhu
出租车在城市交通系统中发挥着重要作用，其乘客量受城市建成环境的影响很大。使用传统的普通最小二乘法 (OLS) 回归或地理加权回归 (GWR) 探索了出租车乘客量与城市环境之间的复杂关系。然而，时间构成了一个重要的维度，特别是在分析时空每小时出租车乘客量时，这并没有有效地纳入传统模型中。在本研究中，应用地理和时间加权回归（GTWR）模型对每小时出租车乘客量的时空异质性进行建模，并可视化时空系数变化。为了测试 GTWR 模型的性能，我们使用一组工作日出租车上客点数据对中国厦门市进行了实证研究。使用兴趣点 (POI) 数据，将每小时的出租车乘客量纳入基于 500 × 500 m 网格单元的各种空间城市环境变量中进行分析。与 OLS 和 GWR 相比，GTWR 模型无论在模型拟合度还是解释精度方面都获得了最佳性能。此外，城市环境对出租车乘客量有重大影响。研究发现，道路密度会减少特定地点的出租车出行次数，而且随着时间的推移，公交车站的密度会与出租车乘客量产生竞争。 GTWR 模型为调查出租车客流量随时空城市环境变量的变化提供了宝贵的见解，从而促进出租车资源和交通规划的优化分配。
Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning.
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影响因子：
13.5
作者：
Guo, Yuanxi;Tang, Qiuhong;Zhang, Ziyin
通讯作者：
Zhang, Ziyin
地理加权与时间相关的逻辑回归模型。
影响因子：
4.6
作者：
Liu Y;Lam KF;Wu JT;Lam TT
通讯作者：
Lam TT
DOI：
10.1109/tits.2016.2521862
发表时间：
2016-04
影响因子：
8.5
作者：
Xianyuan Zhan;Xinwu Qian;S. Ukkusuri
通讯作者：
Xianyuan Zhan;Xinwu Qian;S. Ukkusuri
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
Xinwu Qian;S. Ukkusuri
通讯作者：
Xinwu Qian;S. Ukkusuri
影响因子：
4.9
作者：
Daniel Cardozo, Osvaldo;Carlos Garcia-Palomares, Juan;Gutierrez, Javier
通讯作者：
Gutierrez, Javier