城市群内部出行强度的距离衰减效应

doi:10.13229/j.cnki.jdxbgxb.20230017

Abstract

Abstract:

There are some problems in travel feature research on the scale of urban agglomeration， such as insufficient basic data and difficult horizontal comparison， thus web crawler is used to obtain some fundamental data such as migration OD flow matrix among the cities in China， and then the cumulative proportion curve features of migration intensity are analyzed. Further， a gravity model is built to fit and analyze the change characteristics of inter-city migration intensity along with distance within different urban agglomerations. This model uses Baidu migration data to represent the inter-city travel intensity， take the shortest road network distance as the distance parameter， and combine with urban permanent population. What's more， the concept of “accessibility” is defined as the average travel time weighted by central city “quality”， then the accessibility under road transport， railway transport and comprehensive transport is calculated respectively and the relationship between accessibility and distance-decay coefficient is analyzed.^{It is found that the travel distance-decay effects are obvious for most of the city clusters and the migration interactions have reached the city-scale level.}

Key words: transportation planning and management, city clusters, migration data, distance-decay effect, gravity model

CLC Number:

U491

Li-ying WEI,Huan-huan PENG. Distance-decay effects of travel intensity within city clusters[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3199-3208.

Figures/Tables 9

Table 1

Fig.1

Fig.2

Table 2

Table 3

Forms of common distance decay functions"

模式	函数
指数型	$f (d i j) = e x p (- β d i j) (β > 0)$
幂律型	$f (d i j) = β i j - β (β > 0)$
高斯型	$f (d i j) = β i j - β d i j 2 (β > 0)$
指数截断幂律型	$f (d i j) = e x p (- a d i j) d - β (β > 0)$
对数正态型	$f (d i j) = e x p - β l n d i j 2 (β > 0)$
平方指数型	$f (d i j) = e x p (- β d i j 2) (β > 0)$
平方根指数型	$f (d i j) = e x p (- β d i j 0.5) (β > 0)$

Table 3

Fig.3

Table 4

Fitting results of gravity model distance decay function for different city clusters"

城市群等级	城市群名称	$α$	$β$	R²	距离衰减函数
国家级城市群	京津冀城市群	-1.316	1.237	0.564	$y = - 1.237 x - 1.316$
	长三角城市群	-0.567	1.723	0.604	$y = - 1.723 x - 0.567$
	珠三角城市群	-0.411	1.684	0.559	$y = - 1.684 x - 0.411$
	成渝城市群	+2.465	1.982	0.783	$y = - 1.982 x + 2.465$
	长江中游城市群	+3.819	2.405	0.757	$y = - 2.405 x + 3.819$
区域级城市群	哈长城市群	+5.337	2.268	0.589	$y = - 2.268 x + 5.337$
	辽中南城市群	-1.693	1.045	0.552	$y = - 1.045 x - 1.693$
	山东半岛城市群	+0.848	1.700	0.832	$y = - 1.700 x + 0.848$
	海峡西岸城市群	+2.888	2.229	0.889	$y = - 2.229 x + 2.888$
	中原城市群	+3.321	2.304	0.709	$y = - 2.304 x + 3.321$
	关中平原城市群	+40.25	2.169	0.662	$y = - 2.169 x + 4.025$
	北部湾城市群	+8.093	2.921	0.772	$y = - 2.921 x + 8.093$
	天山北坡城市群	-1.993	0.813	0.275	$y = - 0.813 x - 1.993$
地区级城市群	呼包鄂榆城市群	+10.045	3.267	0.693	$y = - 3.267 x + 10.045$
	兰西城市群	+10.960	3.138	0.636	$y = - 3.138 x + 10.959$
	滇中城市群	+0.948	1.492	0.522	$y = - 1.492 x + 0.948$
	黔中城市群	+3.163	1.806	0.692	$y = - 1.806 x + 3.163$
	晋中城市群	-0.079	1.220	0.523	$y = - 1.220 x - 0.079$
	宁夏炎黄城市群	0.814	1.106	0.361	$y = - 1.106 x + 0.814$

Table 4

Table 5

Calculation results of accessibility of city clusters"

城市群等级	城市群名称	中心城市	$β$	公路运输可达性/h	铁路运输可达性/h	综合运输可达性/h
国家级城市群	京津冀城市群	北京市、天津市、石家庄	1.237	2.303	1.390	1.803
	长三角城市群	上海市、杭州市、南京市、合肥市	1.723	2.518	1.452	1.944
	珠三角城市群	广州市	1.684	0.906	0.495	0.786
	成渝城市群	重庆市、成都市	1.982	2.505	1.410	1.715
	长江中游城市群	武汉市、长沙市、南昌市	2.405	3.149	2.078	2.329
区域级城市群	哈长城市群	哈尔滨、长春市	2.268	2.772	2.067	2.410
	辽中南城市群	沈阳市、大连市	1.045	2.780	1.421	1.973
	山东半岛城市群	济南市、青岛市	1.700	2.993	1.762	2.030
	海峡西岸城市群	福州市、厦门市	2.229	2.767	0.588	1.947
	中原城市群	郑州市	2.304	1.348	0.675	1.217
	关中平原城市群	西安市	2.169	1.814	1.233	1.519
	北部湾城市群	南宁市、海口市	2.921	3.066	4.056	3.477
地区级城市群	呼包鄂榆城市群	呼和浩特市	3.267	2.926	3.385	3.270
	兰西城市群	西宁市、兰州市	3.138	1.795	1.850	1.830
	滇中城市群	昆明市	1.492	1.603	1.174	1.426
	黔中城市群	贵阳市	1.806	1.467	0.945	1.453
	晋中城市群	太原市	1.220	1.281	0.790	0.914

Table 5

Fig.4

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误差 \|（迁出规模指数-迁入规模指数）/迁出规模指数*100%\|	城市个数	城市占比/%	累计百分比/%
［0，5%）	204	55.43	55.43
［5%，10%）	103	27.99	83.42
［10%，15%）	42	11.41	94.84
［15%，20%）	12	3.26	98.10
［20%，40%）	7	1.90	100.00
［40%，+∞）	0	0.00	100.00

等级	城市群名称	指数型	幂律型	高斯型	指数截断幂律型	对数正态型	平方指数型	平方根指数型
国家级城市群	京津冀城市群	0.62	0.65	0.57	0.65	0.64	0.58	0.64
	长三角城市群	0.29	0.26	0.28	0.29	0.27	0.29	0.28
	珠三角城市群	0.43	0.45	0.38	0.45	0.45	0.39	0.45
	成渝城市群	0.17	0.17	0.15	0.17	0.17	0.15	0.18
	长江中游城市群	0.28	0.18	0.28	0.30	0.22	0.29	0.25
区域级城市群	哈长城市群	0.47	0.40	0.49	0.58	0.42	0.49	0.44
	辽中南城市群	0.26	0.20	0.22	0.26	0.23	0.23	0.25
	山东半岛城市群	0.46	0.44	0.45	0.46	0.45	0.45	0.46
	海峡西岸城市群	0.54	0.39	0.60	0.80	0.44	0.60	0.48
	中原城市群	0.20	0.17	0.21	0.22	0.18	0.20	0.19
	关中平原城市群	0.86	0.86	0.87	0.88	0.86	0.86	0.86
	北部湾城市群	0.33	0.25	0.38	0.42	0.27	0.37	0.29
	天山北坡城市群	0.31	0.31	0.31	0.36	0.32	0.31	0.32
地区级城市群	呼包鄂榆城市群	0.40	0.43	0.46	0.52	0.52	0.48	0.50
	兰西城市群	0.52	0.58	0.56	0.58	0.50	0.65	0.63
	滇中城市群	0.39	0.45	0.36	0.40	0.32	0.36	0.25
	黔中城市群	0.25	0.30	0.29	0.29	0.32	0.30	0.27
	晋中城市群	0.25	0.29	0.32	0.28	0.30	0.36	0.32
	宁夏炎黄城市群	0.21	0.26	0.29	0.26	0.29	0.32	0.34

Distance-decay effects of travel intensity within city clusters

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