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土地利用混合度对轨道交通车站客流的影响
引用本文:李俊芳,姚敏峰,季峰,向蕾.土地利用混合度对轨道交通车站客流的影响[J].同济大学学报(自然科学版),2016,44(9):1415-1423.
作者姓名:李俊芳  姚敏峰  季峰  向蕾
作者单位:同济大学 道路与交通工程教育部重点实验室,上海 201804,同济大学 道路与交通工程教育部重点实验室,上海 201804;华侨大学 建筑学院,厦门 361021,同济大学 道路与交通工程教育部重点实验室,上海 201804,同济大学 道路与交通工程教育部重点实验室,上海 201804
基金项目:上海市科学技术委员会科技攻关项目,城市轨道交通车站接驳交通规划方法研究与应用示范, 编号:1123120300;国家自然科学基金面上项目资助,市郊轨道交通综合体建筑模式及其适用性评价研究,编号51478198;
摘    要:针对土地利用混合程度如何影响轨道交通车站客流的问题,建立非线性回归方法对其进行量化研究.采用递远递减权重和相邻车站重叠区域人口分配权重来计算加权人口,并以车站客流/加权人口作为因变量,从而分析由土地利用混合程度引起的车站客流变化.构建最小二乘支持向量机模型来分析土地利用混合程度、岗位居住人口比以及车站客流间相互关系.最后,以日本东京都109个车站的实际数据进行案例分析,案例结果表明土地利用混合程度对车站客流影响较弱,而岗位居住人口比与车站出站客流呈现显著正相关.因此,客流预测过程中应以岗位居住人口比代替土地利用混合程度作为关键因素.

关 键 词:交通工程  车站客流预测  最小二乘支持向量机  土地利用混合程度  岗位居住人口比
收稿时间:2015/10/21 0:00:00
修稿时间:2016/6/15 0:00:00

Quantitative Study on How Land Use Mix Impact Urban Rail Transit at Station level
LI Junfang,YAO Minfeng,JI Feng and XIANG Lei.Quantitative Study on How Land Use Mix Impact Urban Rail Transit at Station level[J].Journal of Tongji University(Natural Science),2016,44(9):1415-1423.
Authors:LI Junfang  YAO Minfeng  JI Feng and XIANG Lei
Institution:Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China,Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China; Architecture Colleague of Huaqiao University, Xiamen 361021, China,Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China and Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Abstract:This essay focuses on how land use mix quantitatively impacts urban rail transit ridership at station level by nonlinear regression model. Distance decay weight and weight of population in mutual service area assigned to each station are used to weigh population within service area of station. Then ridership divided by weighted population is taken as dependent variable to analyze what is the relationship between land use mix and ridership at station level. Least square support vector machine is the ideal model to do the above thing. Finally, data of 109 stations in Tokyo, Japan are taken as case study, result of which shows land use mix has a little influence on ridership at station level and meanwhile, employment/inhabitants within service area of station has a significant influence on ridership at station level. So, employment/inhabitants should substitute land use mix and be taken as key predictor for ridership at station level.
Keywords:traffic engineering  ridership forecasting at station level  least squares support vector machine (LS SVM)  land use mix  ratio of employment to inhabitant
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