Methods of Determining the Range of Non-motorized Travel Influence Area Under the Concept of “Metro Transit Micro-center”

doi:10.12141/j.issn.1000-565X.210651

Abstract

Abstract:

In response to the concept of land-use integration and creating micro-center around metro station in Beijing, this study extracted 23 quantitative indicators from public passenger flow, road network design, population density and land diversity to quantitatively analyze the built environment and travel characteristics of the non-motorized influence area based on multi-source big data. The connection characteristics of shared bicycles were taken into particular consideration. In order to compensate for the shortcomings of determining the influence range of metro stations by the traveler’s walking time, a classification model incorporating principal component analysis and K-means clustering was proposed to define the non-motorized influence area. Taking Beijing as an example, the study divided the metro stations into 4 clusters: inefficient connection-weak connectivity-residence oriented, efficient connection-high connectivity-balanced, efficient connection-weak connectivity-mixed, and efficient connection-high connectivity-work oriented. In order to verify the rationality of the clustering, the spatial auto-correlation was used to judge the spatial dependence of indicators. The results show that the spatial distributions of clusters 1, 3 and 4 do not differ significantly from the random model, while cluster 2 efficient connection-high connectivity-balanced stations has auto-correlation characteristics in space. Finally, based on the clustering results, the non-motorized influence areas of the metro stations were delineated as 2 000, 1 600, 1 600, and 1 700 m, respectively. The clarification of the non-motorized influence range of different metro station types can help urban planners determine the scope of micro-center construction and also lay the foundation for transport-oriented development of urban in the future.

Key words: metro micro-center, influence factor, principal component analysis, K-means clustering, non-motorized travel influence area

CLC Number:

CHEN Tingzhao, CHEN Yanyan, WANG Zili, et al. Methods of Determining the Range of Non-motorized Travel Influence Area Under the Concept of “Metro Transit Micro-center”[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(7): 56-65.

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编号	指标类型	指标	平均值	标准差	最小值	最大值
A1	客流指标	地铁登降量/人	41 865	27 017	4 608	152 249
A2		轨道站点早高峰系数进出比	2.345	3.012	0.068	15.337
A3		轨道站点晚高峰系数进出比	1.593	1.526	0.139	9.700
A4		公交登降量/人	15 244	12 646	0	105 403
A5		单车借还量/人	4 167	2 881	134	13 277
A6		轨道站点单车接驳量/人	907	637	53	2 735
A7		轨道站点单车接驳时间/s	586.565	99.128	412.813	1 182.493
A8		轨道站点单车接驳85分位距离/m	1 719.371	385.750	1 090.755	3 126.072
A9		轨道站点单车接驳早高峰系数进出比	1.059	0.366	0.325	3.147
A10		轨道站点单车接驳晚高峰系数进出比	1.046	0.355	0.363	3.296
B1	路网指标	轨道站点邻近距离/m	1 677	596	824	1 539
B2		公交站点邻近距离/m	251	61	32	260
B3		公交站点所经线路数量/条	51	30	4	47
B4		轨道路网度中心度	0.008 20	0.002 81	0.003 48	0.013 94
B5		轨道路网介数中心度	0.054 69	0.044 75	0.000 00	0.228 90
B6		轨道路网邻近中心度	0.074 33	0.013 30	0.035 23	0.097 52
B7		公交路网度中心度	0.002 66	0.001 55	0.000 34	0.007 43
B8		公交路网介数中心度	0.017 96	0.031 74	0.000 03	0.211 28
B9		公交路网邻近中心度	0.234 00	0.115 62	0.041 72	0.577 64
B10		路况等级	3.059	1.183	1.000	8.000
C1	人口指标	居住人口密度/（人·km^-2）	9 422	5 819	104	38 762
C2	人口指标	工作人口密度/（人·km^-2）	4 638	4 051	67	31 304
D1	用地多样性指标	功能混合度	1.568	0.265	0.003	1.977

主成分	方差贡献率/%	累计贡献率/%
1	36.96	36.96
2	12.89	49.85
3	8.79	58.64
4	7.17	65.81
5	5.01	70.82
6	4.66	75.48
7	3.96	79.44
8	3.33	82.77
9	2.82	85.59

类别	Moran’s I	Z得分	p值
簇1	0.100	1.680	0.093
簇2	0.064	2.718	0.007^***
簇3	0.027	0.931	0.352
簇4	0.033	1.383	0.167

类别	平均骑行接驳时间/s	平均骑行接驳85%分位距离/m	轨道站点慢行影响区范围/m
簇1	660	1 972	2 000
簇2	558	1 627	1 600
簇3	548	1 562	1 600
簇4	571	1 711	1 700

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