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建成环境对出租车出行需求影响的时空间分异模式 被引量:6

The Spatial-Temporal-Nonstationary Effect of Built-Environment on Taxi Demand
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摘要 出租车作为城市重要的公共交通工具,在满足居民日常出行需求、提高出行效率方面的作用举足轻重。已有研究分析了城市建成环境对居民出租车出行需求影响的空间异质性,但受限于交通大数据的可获取性,尚未考虑到该影响在时间维度上的差异,而研究时空差异恰恰可为制定更符合居民出行需求的城市交通系统规划、日常管理与决策以及智慧城市建设等提供参考依据因此本文考察了在时间维度下,建成环境对出租车出行需求影响机制的时空特征与模式。结果发现,人口密度的影响在外围地区具有全天候的正向作用,而在城市内部则一直呈现负向影响且随时间推移,影响的"负值中心"逐渐西移;就业岗位密度的影响在全域空间上均为正向影响强度由中心向外围地区逐渐增强,且随时间推移影响的强弱区域范围发生融合与重组;土地利用混合度的影响强度较大,作用机制与所处地区的中心地级别有关,但时间维度上的差异不明显;道路网密度在全域均具有正向影响作用,且外环路以内地区为全天候影响较弱的区域晩高峰时间段出现"双弱中心"的时空演变格局。最后通过聚类发现,人口密度主导影响的地区全天都分布在城市边缘地带,就业岗位密度与之相似;土地利用混合度主导影响的地区从早至晚有从中心城区向外扩散的趋势,且呈现中心地区负向作用、外围地区正向作用的影响机理;道路网密度主导影响的地区在不同时间段呈现跃迁与扩张变化,经历了从郊区到中心区再回到外围生态休闲用地上的过程。 Taxi is an important component of urban transit system since it caters to a large amount of demand and enhance the transportation efficiency. In this paper, we understand the spatial-temporal variation of taxi ridership by Geographical Weighted Poisson Regression (GWPR). The taxi ridership is analysed by relating it to various built-environment variables. Population density is found to increase demand all day in surrounding regions while the negative-effect centre moves to the west gradually. The effect of employment density appears to be ring structure. Related to the business area rank, effect of the land use mix turns to be stable during different periods, and the demand among the outer ring roadis positively associated with taxi ridership all day,while there appears to be double Centre during evening peak.In addition,there are some spatial-temporal differences on the mainly influenced areas by the builtenvironment,and the summary of spatial-temporal pattern may provide valuable insights for predicting taxi demand as a function of spatial-temporally explicit variables which may have implications on taxi pricing, taxi industry regulation had urban planning.
作者 谢蔚翰 周素红 XIEWeihan;ZHOU Suhong
出处 《现代城市研究》 CSSCI 2018年第12期22-29,共8页 Modern Urban Research
基金 国家自然科学基金项目(41522104) 广东省自然科学基金项目(2017A030313228).
关键词 出租车出行需求 时空间分异 地理加权泊松回归 built-environment taxi demand spatial-temporal-nonstationary geographical weighted poisson regression
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