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Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (2): 307-314    DOI: 10.3785/j.issn.1008-973X.2019.02.014
Civil Engineering, Traffic Engineering     
Regional integration of Qianzhong cities using highway big data
Fen-jie LONG1,2,3(),Long-fei ZHENG1,3,Lang SHI1,3,Zi-yan CHEN1,3
1. Hang Lung Center for Real Estate, Tsinghua University, Beijing 100084, China
2. Guizhou Institute of Technology, Guiyang 550003, China
3. Department of Construction Management , Tsinghua University, Beijing 100084, China
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Abstract  

The core of regional integration is the free flow of production factors among various cities, and the flows of production factors such as labor and trade between cities are important indexes of urban integration of city group. The traffic flows among the 33 counties (districts) of 5 prefecture-level cities in the Qianzhong cities were calculated to reflect the labor and trade flows among districts and counties, according to the collection of 16 783 229 toll data of 165 highway toll stations in the Qianzhong cities from April 1, 2016 to December 30, 2016. The toll data were placed under the framework of the gravity model, and the administrative areas, population, distance and other interference factors were controlled. The degree of regional integration in the Qianzhong cities was analyzed with the criterion that whether administrative boundary had great impact on labor and trade flows or not. Results showed that the overall integration of Qianzhong cities was relatively low, and the administrative boundary had a significant negative impact on labor and trade flows. For the first time, the degree of regional integration of the Qianzhong cities was analyzed through highway big data, and a new perspective and method for the assessment of the regional integration was provided. This method can solve the problems of subjective dependence and lack of micro-information in previous studies.



Key wordsQianzhong cities      regional integration      gravity model      highway big data      Poisson regression     
Received: 26 January 2018      Published: 21 February 2019
CLC:  U 4  
  O 212  
Cite this article:

Fen-jie LONG,Long-fei ZHENG,Lang SHI,Zi-yan CHEN. Regional integration of Qianzhong cities using highway big data. Journal of ZheJiang University (Engineering Science), 2019, 53(2): 307-314.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.02.014     OR     http://www.zjujournals.com/eng/Y2019/V53/I2/307


基于公路大数据的黔中城市群一体化研究

城市群一体化的核心是要素在城市间无障碍地自由流通,城市间人员、货物等要素的流量是测度城市群一体化程度的重要指标. 通过收集2016年4月1日至12月30日黔中城市群辖区内165个高速公路收费站16 783 229条收费数据,汇总城市群内5个地级市(州)33个区县之间的车流往来数量,计算各区县之间的贸易和人员流量. 将其置于重力模型框架下,在控制行政面积、人口、距离等干扰因素的条件下,依据行政边界是否对人员和贸易流量存在显著影响来测度黔中城市群的一体化程度. 结果表明,黔中城市群整体上一体化程度较低,行政边界对人员和贸易流量均存在显著的负向影响. 首次通过高速公路大数据对黔中城市群一体化程度进行分析,为城市群一体化的评价提供了新的视角和方法,解决了以往研究中存在的主观性依赖和微观信息缺失的问题.


关键词: 黔中城市群,  区域一体化,  重力模型,  公路大数据,  泊松回归 
①根据2016年《中国城市统计年鉴》,贵州省公路客运和货运比例大于90%;同时,各个区县高速公路与其他公路的运量比例一般不存在显著差异.
Tab.1 
车牌 卡号 入口
收费站
入口时间 车型 出口
收费站
出口时间
贵Hxxxxx 513262270 黎平南 2016/5/917:08:37 1 锦屏站 2016/5/917:54:39
Tab.1 Sample of raw highway traffic flow data of Qianzhong cities
①根据该划分标准可知,1型车为小客车,5型车为15 t以上的货车,可以代表人流量和货流量. 2,3,4型车辆既包含货车也包含客车,因此并未出现在主回归分析中,而是作为稳健性检验的数据集. 同时,经过检验,5个市(州)5种类型车辆的比例不存在显著差异,因此可以认为1型和5型车代表一定比例的人流量和货流量,并且这个比例在5个市(州)是相同的.
Tab.2 
Fig.1 Total traffic flows between different cities and counties in Qianzhong cities
地级市(州) 所含区县 人口/万 面积/km2 车流类型 总交通量 1型车辆 5型车辆
注:地级市(州)的人口和辖区面积为截至2016年12月31日的数据,仅包含被划入黔中城市群的区县;交通量的数据单位为辆;括号内的数值代表流入(出)的车流量与城市内部区县的车流量之比
贵阳市 南明区、云岩区、白云区、花溪区、乌当区、观山湖区、清镇市、修文县、息烽县、开阳县 490 8 034 流入 1 098 445(37.2%) 779 279(39.4%) 5 349(154.1%)
流出 1 153 008(39.0%) 808 576(40.9%) 5 057(145.7%)
内部 2 954 058 1 976 373 3 471
遵义市 红花岗区、汇川区、播州区、绥阳县、仁怀市 287 8 146 流入 278 944(17.3%) 208 934(17.6%) 853(58.1%)
流出 276 403(17.1%) 207 075(17.5%) 835(56.9%)
内部 1 612 762 1 184 622 1 467
毕节市 七星关区、大方县、黔西县、金沙县、织金县 395 14 694 流入 308 881(39.5%) 222 122(42.1%) 1 510(76.4%)
流出 279 016(35.7%) 204 106(38.7%) 776(39.3%)
内部 782 510 527 400 1 976
安顺市 西秀区、平坝区、普定县、镇宁县 173 5 502 流入 314 079(74.5%) 216 691(79.8%) 2 031(140.7%)
流出 320 702(76.0%) 222 360(81.9%) 1 889(130.9%)
内部 421 844 271 542 1 443
黔(东)南州 都匀市、凯里市、福泉市、贵定县、瓮安县、长顺县、龙里县、惠水县、麻江县 269 15 882 流入 471 193(43.7%) 314 227(43.8%) 1 587(33.3%)
流出 442 413(41.0%) 299 136(41.7%) 2 773(58.2%)
内部 1 078 511 718 191 4 763
Tab.2 Administrative division of Qianzhong cities and traffic flows
Fig.2 Location of Qianzhong cities and traffic flows between counties
因子 VIF 因子 VIF
T 4.66 ηj 4.68
d 4.42 D4 4.69
μi 5.97 ? ?
Tab.3 Multicollinearity diagnosis of independent variables
变量 ln T D(1) D2 D3 D4 D5 D6 D7 D8 μi ηj AIC BIC Vuong Z
注:括号内为估计系数的标准差;*、**、***分别代表10%、5%、1%的显著性水平;以上6个模型的观测单位均为496个;Y表示控制了车辆流入县和流出县的固定效应(如区县人口、住所总量等因素)的影响;AIC、BIC分别为根据赤池信息准则和贝叶斯信息准则得到的似然比检验值; Vuong Z为Vuong统计量对应的标准化Z值;模型①~③中只使用了变量DD代表不同市(州)的区县;模型④~⑥中使用了变量D1~D8
模型① ?0.52***
(0.071)
?0.358***(0.033) ? ? ? ? ? ? ? Y Y 2 218 2 235 ?
模型② ?0.51***
(0.075)
?0.380***
(0.034)
? ? ? ? ? ? ? Y Y 2 204 2 220 ?
模型③ ?0.28
(0.230)
?0.570***(0.110) ? ? ? ? ? ? ? Y Y 1 588 1 622 4.19
模型④ ?0.39***
(0.075)
?0.174***(0.059) ?0.108
(0.066 1)
0.065
(0.076)
0.128
(0.082)
?0.127**
(0.052)
?0.297**
(0.065)
?0.135
(0.083)
?0.143***(0.054) Y Y 2 213 2 268 ?
模型⑤ ?0.38***
(0.079)
?0.167***(0.062) ?0.089
(0.070 4)
0.099
(0.081)
0.152*
(0.088)
?0.111**
(0.054)
?0.300***(0.071) ?0.133
(0.084)
?0.159***(0.057) Y Y 2 197 2 252 ?
模型⑥ 0.24
(0.180)
?0.293*
(0.161)
?0.056
(0.199)
0.208
(0.239)
0.072
(0.311)
?0.434*
(0.223)
?1.551***(0.346) ?0.618**
(0.288)
?0.428*
(0.228)
Y Y 1541 1605 5.94
Tab.4 Estimation results of regional integration in Qianzhong cities
变量 D D1 D2 D3 D4 D5 D6 D7 D8 μi ηj AIC BIC R2
注:同表 4 注. 另外,本表未列出控制变量 dt 的系数;R2 为拟合优度;为防止共线性问题,反映整个城市群一体化程度的 DD1~D8 是分开估计的
总车辆
OLS
?3.098***
(0.254)
?1.423**
(0.547)
?0.761
(0.472)
0.418
(0.505)
0.562
(0.490)
?1.005*
(0.582)
?1.495***
(0.363)
?1.091**
(0.516)
?0.690***
(0.189)
Y Y 2 268 2 122 0.291
人流量
OLS
?2.996***
(0.255)
?1.312**
(0.537)
?0.615
(0.454)
0.590
(0.497)
0.650
(0.495)
?0.862
(0.569)
?1.373***
(0.369)
?1.022**
(0.498)
?0.724***
(0.189)
Y Y 2 074 2 128 0.289
货流量
OLS
?1.590***
(0.199)
?0.677*
(0.385)
?0.378
(0.598)
0.254
(0.799)
?0.060 2
(0.887)
?0.746***
(0.263)
?1.043**
(0.416)
?0.671**
(0.319)
?0.235
(0.391)
Y Y 1 784 1 839 0.178
货流量
零泊松
?0.496***
(0.089 8)
?0.292*
(0.158)
?0.054 4
(0.196)
0.206
(0.236)
0.070 8
(0.308)
?0.433*
(0.221)
?1.555***
(0.344)
?0.620**
(0.287)
?0.433*
(0.226)
Y Y 1 539 1 598 ?
货流量
负二项
?0.728***
(0.099 1)
?0.318*
(0.168)
?0.193
(0.209)
0.174
(0.251)
0.043 8
(0.297)
??0.450**
(0.225)
?1.525***
(0.316)
?0.552*
(0.294)
?0.322
(0.254)
Y Y 1 567 1 625 ?
1+2 型车泊松 ?0.440***
(0.043 2)
?0.169***
(0.060 2)
?0.098 8
(0.068 1)
0.073 2
(0.078 4)
0.132
(0.084 5)
?0.118**
(0.052 6)
?0.300***
(0.067 6)
?0.136
(0.083 3)
?0.161***
(0.054 8)
Y Y 2 189 2 244 ?
4+5 型车零膨胀负二项 ?0.718***
(0.070 1)
?0.236**
(0.095 6)
?0.095 1
(0.105)
0.205*
(0.124)
0.189
(0.145)
?0.289**
(0.122)
?0.645***
(0.148)
?0.317**
(0.145)
?0.275**
(0.122)
Y Y 1 952 2 024 ?
Tab.5 Robust checks of regional integration evaluation model
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