土地利用视角下的轨道站点客流预测——以深圳市为例

doi:10.13249/j.cnki.sgs.2018.12.016

摘要/Abstract

摘要：

从土地利用视角分析轨道站点客流的影响因素,基于直接估计模型,选择不同类型用地楼板面积、公交线路数量、站点中心性、站点可达性4个变量建立非线性回归函数,分析不同类型用地客流生成率与站点可达性之间的衰减规律。结果表明：① 相比于线性衰减规律,客流生成率随站点可达性的降低更符合指数衰减规律,用地距离地铁站0~0.5 km时客流生成率下降最快。② 单位面积各类用地客流生成率由大到小依次为：交通用地>办公用地>商业用地≈城中村>工业用地>居住用地>其他用地。③ 客流生成率受站点可达性影响强度由大到小依次为：商业用地>办公用地>交通用地>其他用地>居住用地>城中村>工业用地。

关键词: 交通与土地利用, 轨道交通, 站点客流预测, 深圳市

Abstract:

Analyzing the numerical relationship between transit ridership and land use is significant to station location, land use planning and rail transit property development. Although there have been a growing number of studies on long-term ridership forecasting, limited effort has been made to investigate the impacts of different land use types on ridership and ridership decay rules. Two problems are considered in this paper. First, how do the land development intensity of different types influence station ridership? Second, what are the relationships between the walking distance to the station and trip rates for different land types? This paper attempts to answer the questions based on the direct ridership model. A two-step nonlinear regression model is established and the parameters are estimated by the Gauss-Newton method. Four variables including floor area of different land types, bus lines, centrality and walking distance to the station are included in the model. The first-step model separates the ridership produced by the land use from the total ridership. In the second-step model, two types of distance-decay function, exponential-decay function and linear-decay function, are considered. The parameters of exponential model and linear model are calculated according to . Smart card data of 94 subways in Shenzhen are used to valid the model. The results are: 1) Compared to the linear decay tendency, the decay rule is more in line with the exponential form; 2) Trip rates of different land types ordering from large to small are: transport land > commercial land > official land ≈ urban village > industrial land > residential land > other land; 3) The impacts of walking distance on the trip rate ordering from large to small are: commercial land > official land > transport land > other land > residential land > urban village > industrial land. Due to considering the factors of land use and walking distance to the station, the proposed model provides a quite accurate prediction of the station ridership. Ridership forecasting is crucial for the analysis of project viability and urban rail transit planning. With the help of estimated ridership, subway planners can proactively judge whether a site is suitable for setting transit station. The decay rules of different land types can provide reference for property development around the stations and travel analysis.

Key words: traffic and land use, rail transit, station ridership forecasting, Shenzhen City

中图分类号:

TU984.191

孔祥夫, 杨家文. 土地利用视角下的轨道站点客流预测——以深圳市为例[J]. 地理科学, 2018, 38(12): 2074-2083.

Xiangfu Kong, Jiawen Yang. A New Method for Forecasting Station-level Transit Ridership from Land-use Perspective: The Case of Shenzhen City[J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(12): 2074-2083.

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变量类型	变量	变量描述	变量特性	最大值	最小值	平均值
因变量	R	站点总客流,包括进站与出站客流之和（人/d）	连续	93561	511	27583
距离变量	d	建筑距最近地铁站的路网距离（km）	连续	0.800	0.003	0.493
各类用地	RES_LD	居住用地楼板面积（m²）	连续	669852.80	3.59	5931.19
楼板面积	COM_LD	商业用地楼板面积（m²）	连续	453710.48	4.50	6331.69
楼板面积	OFF_LD	办公用地楼板面积（m²）	连续	404602.10	9.75	18162.58
	IND_LD	工业用地楼板面积（m²）	连续	149573.75	4.00	2402.58
	VIL_LD	城中村楼板面积（m²）	连续	43710.00	1.00	666.71
	TRANS_LD	交通用地楼板面积（m²）	连续	263312.28	4.80	8869.62
	OTH_LD	其他用地楼板面积（m²）	连续	550749.00	1.00	2659.49
节点中心性	Closeness	接近中心性	连续	0.103	0.040	0.077
	Betweenness	中介中心性	连续	0.312	0.000	0.110
公交变量	Bus	公交线路数量	连续	65	1	22.10

参数	估计值	标准差	上限	下限
接近中心性	7.655	5.141	-2.566	17.876
中介中心性	1.801	0.707	0.396	3.205
公交线路	74.306	60.613	-46.189	194.802
居住	0.003	0.001	0.000	0.006
商业	0.006	0.003	0.000	0.013
办公	0.007	0.003	0.000	0.014
工业	0.005	0.003	-0.001	0.011
城中村	0.006	0.003	0.001	0.012
交通	0.030	0.015	0.001	0.059
其他	0.001	0.002	-0.007	0.002

站点类型	项目	指数模型	线性模型
居住型	RMSE 平均客流量	2767 27061	3276 27061
	样本数	24	24
办公型	RMSE 平均客流量	3260 32978	3455 32978
	样本数	24	24
商业型	RMSE 平均客流量	12084 54643	12922 54643
	样本数	6	6
混合型	RMSE	1895	2173
	平均客流量	20225	20225
	样本数	39	39
交通型	标准残差均值平均客流量	2500 52023	2404 52023
	样本数	3	3