Efficiency of Traffic Structure Based on SBM-Tobit-GWR Model
-
摘要: 在国家节约资源和生态环境保护战略的引导下，为了提高交通运输效率，改善交通运输结构，实现交通行业的绿色可持续发展，从交通投入与系统产出两个层面分析影响交通结构效率的因素，引入非期望产出的超效率SBM （slack based measure）模型，并考虑交通的环境效益，系统分析了我国30个省市的区域交通运输效率. 同时，应用Tobit回归方法与地理加权模型（geographically weighted regression，GWR）解析交通结构效率差异成因、空间分异特征，并提出相应的结构调整策略. 研究结果表明：交通运输综合效率存在地域分布差异，效率值排名前5的省份和效率值分别为上海（1.567）、广东（1.366）、云南（1.292）、江西（1.181）、安徽（1.160）；全国范围内，第二产业产值占GDP比重、人口密度、人均地区生产总值的回归系数分别为0.9513、0.7659、0.5691，三者对于交通结构效率的影响最为显著；分变量系数空间分布图显示不同地区各社会经济要素对于交通结构效率的影响程度存在空间异质性；现阶段我国整体交通效率的提升需要将交通基础建设的大规模、粗犷式发展转变为交通布局规划的精细化设计，优化资源配置，促进公共交通发展.Abstract: Under the guidance of resource conservation and ecological environmental protection strategies, in order to improve transportation efficiency, optimize transportation structure, and achieve environmental friendly and sustainable developments of transportation industry, the factors affecting the transportation structure efficiency are analyzed from the two aspects of traffic input and system output. By introducing the super-SBM (slack based measure) undesirable model, which considers the environmental benefits of the transportation structure system, the transportation structure efficiency of 30 provinces in China is systematically analyzed. Then, the Tobit regression and geographically weighted regression method are used to analyze the causes of transportation structure efficiency differences, the spatial differentiation of factors, and accordingly propose the adjustment strategies for the transportation structure. The results show that the comprehensive efficiency of transportation has obvious regional differences. The top five provinces are Shanghai (1.567), Guangdong (1.366), Yunnan (1.292), Jiangxi (1.181) and Anhui (1.160). The regression coefficients in terms of the proportion of secondary industry output in GDP, population density, and regional per capita GDP are 0.9513, 0.7659 and 0.5691 respectively, which have the most significant impact on the transportation structure efficiency. The spatial distribution of sub-variable coefficients shows that there is spatial heterogeneity for different regions in the influence level of socioeconomic factors on the transportation structure efficiency. To improve the overall transportation efficiency in China, it is necessary to transform the large-scale and rough development of transportation infrastructure into the refined design and planning of transportation layout, optimize resource allocation, and promote the development of public transportation.
-
表 1 回归变量定义
Table 1. Definitions of regression variables
变量类型 变量名 符号 因变量 交通结构效率 TSE 自变量 人口密度/（人•km−2） DP 人均地区生产总值/元 PGDP 全社会固定资产投资/亿元 TIFA 交通运输用地面积/千公顷 ALT 第二产业产值占 GDP 比重/% INP 环境污染治理投资总额/亿元 IEPC 每万人拥有公交车辆数/标台 TPB 
下载: 导出CSV
表 2 投入与产出的调整率
Table 2. Adjustment rate of input and output
% 省份 投入冗余率 期望产出不足率 非期望产出冗余率 城市道路网密度 轨道交通里程 公路里程 铁路里程 万人拥有公交车辆 能源消耗总量 从业人
员数城市客
运量货运量 氮氧化物 颗粒物PM 山西 25.28 0 0 39.27 0 33.93 33.31 0 0 28.60 22.35 江苏 13.72 1.82 0 0 0 0 22.64 9.95 0 43.55 44.49 河南 0 0 20.21 26.95 0 26.13 33.64 0 9.75 54.13 50.18 湖北 45.42 0 25.59 14.92 0 5.55 21.57 0 13.41 5.70 17.69 广西 35.32 0 0 35.64 0 15.05 0 90.62 0 6.95 2.23 海南 91.64 0 0 41.75 86.41 29.96 66.71 20.17 31.19 20.62 30.00 新疆 74.39 0 45.09 66.38 80.95 0 12.90 0 158.07 7.63 0
下载: 导出CSV
表 3 Tobit回归分析结果
Table 3. Results of Tobit regression
自变量 回归系数 标准差 Z 值 P 值 DP 0.7659③ 0.2779 2.7556 0.0059 PGDP 0.0488③ 0.0121 4.0231 0.0001 TIFA 0.5691② 0.2727 2.0873 0.0369 ALT 0.0014 0.0069 0.1952 0.8452 INP 0.9513③ 0.3064 3.1049 0.0019 IEPC −0.0346① 0.0204 −1.6940 0.0903 TPB 0.0139 0.0096 1.4489 0.1474 注：①、②、③分别表示10%、5%、1%水平上的显著性，后同.
下载: 导出CSV
表 4 地理加权回归分析结果
Table 4. Results of geographically weighted regression
自变量 回归系数 t 值 回归系数最小值 回归系数最大值 Sig DP 0.4426③ 3.2750 0.4424 0.4428 0 PGDP 0.4742③ 5.6080 0.4742 0.4743 0 TIFA 0.2075③ 2.7550 0.2069 0.2082 0.0058 ALT 0.1418 0.1940 0.1415 0.1422 0.8432 INP 0.4056③ 3.2140 0.4054 0.4058 0 IEPC −0.3193① −1.7030 −0.3196 −0.3191 0.0872 TPB 0.3717 1.4280 0.3716 0.3718 0.1464
下载: 导出CSV
-
李涛,曹小曙,杨文越,等. 中国区域综合运输效率的测度及其时空演化[J]. 地理科学,2015,35(2): 168-175.LI Tao, CAO Xiaoshu, YANG Wenyue, et al. Comprehensive measurement and evolution of regional integreated transport efficiency in China[J]. Scientia Geographica Sinica, 2015, 35(2): 168-175. 交通运输部. 2018年交通运输行业发展统计公报[N]. 中国交通报, 2019-04-12（2）. 国家统计局, 中国统计年鉴[M]. 北京: 中国统计出版社, 2020: 275-293. 中华人民共和国生态环境部. 2018年中国机动车环境管理年报[EB/OL]. (2019-04-09)[2020-03-16]. http://www.gov.cn/guoqing/2019-04/09/content_5380744.htm. BARNUM D T, KARLAFTIS M G, TANDON S. Improving the efficiency of metropolitan area transit by joint analysis of its multiple providers[J]. Transportation Research Part E:Logistics and Transportation Review, 2011, 47(6): 1170-1176. 龙科军,李磊,肖向良,等. 城市路网交通运行效率评价[J]. 中南大学学报(自然科学版),2013,44(增刊1): 430-435.LONG Kejun, LI Lei, XIAO Xiangliang, et al. Operating efficiency evaluation for urban road network[J]. Journal of Central South University (Science and Technology), 2013, 44(S1): 430-435. 陆化普. 解析城市交通[M]. 北京: 中国水利水电出版社, 2001. 窦凌凌. 城市交通运输效率评价研究[D]. 西安: 长安大学, 2010. 胡文婷. 城市道路交通系统效率指标体系与多方式路网优化设计[D]. 南京: 东南大学, 2015. 方国华, 黄显峰. 多目标决策理论、方法及其应用[M]. 北京: 科学出版社, 2011. 王倩倩. 城市交通运行效率评价指标体系研究[D]. 青岛: 中国海洋大学, 2012. 马李京. 基于效率的城市交通结构优化研究[D]. 成都: 西南交通大学, 2015. LEVINSON D. Perspectives on efficiency in transportation[J]. International Journal of Transport Management, 2003, 1(3): 145-155. doi: 10.1016/j.ijtm.2004.01.002 SAMI J,PASCAL F,YOUNES B. 道路公共交通效率的随机前沿分析方法[J]. 交通运输系统工程与信息,2013,13(5): 64-71. doi: 10.3969/j.issn.1009-6744.2013.05.010SAMI J, PASCAL F, YOUNES B. Public road transport efficiency:a stochastic frontier analysis[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(5): 64-71. doi: 10.3969/j.issn.1009-6744.2013.05.010 段新,岑晏青,路敖青. 基于DEA模型的31省份公路运输效率分析[J]. 交通运输系统工程与信息,2011,11(6): 25-29. doi: 10.3969/j.issn.1009-6744.2011.06.004DUAN Xin, CEN Yanqing, LU Aoqing. DEA model-based efficiency analysis for road transport of China’s 31 provinces[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(6): 25-29. doi: 10.3969/j.issn.1009-6744.2011.06.004 BARROS C P, DIEKE P U C. Performance evaluation of Italian airports:a data envelopment analysis[J]. Journal of Air Transport Management, 2007, 13(4): 184-191. doi: 10.1016/j.jairtraman.2007.03.001 魏方庆. 基于非径向距离函数DEA模型的效率评价方法研究[D]. 合肥: 中国科学技术大学, 2018. 季珏,高晓路,刘星辰. 城市交通效率的多目标评价方法研究——以北京市城六区为例[J]. 地理科学进展,2016,35(1): 118-125. doi: 10.18306/dlkxjz.2016.01.013JI Jue, GAO Xiaolu, LIU Xingchen. Multi-objective evaluation of urban transportation efficiency:take Beijing as an example[J]. Progress in Geography, 2016, 35(1): 118-125. doi: 10.18306/dlkxjz.2016.01.013 向活跃,李永乐,廖海黎. 基于DEA的铁路桥梁风屏障防风效果评价[J]. 西南交通大学学报,2012,47(4): 546-550,566. doi: 10.3969/j.issn.0258-2724.2012.04.002XIANG Huoyue, LI Yongle, LIAO Haili. DEA-based evaluation of wind shielding effect of wind barrier for railway bridges[J]. Journal of Southwest Jiaotong University, 2012, 47(4): 546-550,566. doi: 10.3969/j.issn.0258-2724.2012.04.002 王白雪,郭琨. 北京市公共交通碳排放效率研究——基于超效率SBM模型和ML指数[J]. 系统科学与数学,2018,38(4): 456-467.WANG Baixue, GUO Kun. The efficient of carbon emissions efficiency of Beijing public transportation system:based on super-efficiency SBM model using Malmquist-Luenberger index[J]. Journal of Systems Science and Mathematical Science, 2018, 38(4): 456-467. 宋京妮,吴群琪,孙启鹏,等. 基于SBM-undesirable模型的综合运输效率评价[J]. 交通运输系统工程与信息,2015,15(5): 32-38. doi: 10.3969/j.issn.1009-6744.2015.05.005SONG Jingni, WU Qunqi, SUN Qipeng, et al. SBM-undesirable model-based efficiency evaluation for integrated transportation[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 32-38. doi: 10.3969/j.issn.1009-6744.2015.05.005 杨冕. 基于生态环境的城市交通效率研究[D]. 合肥: 安徽财经大学, 2017. TONE K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2001, 130(3): 498-509. doi: 10.1016/S0377-2217(99)00407-5 钱振华,成刚. 数据包络分析SBM超效率模型无可行解问题的两阶段求解法[J]. 数学的实践与认识,2013,43(5): 171-178. doi: 10.3969/j.issn.1000-0984.2013.05.026QIAN Zenhua, CHENG Gang. A two-stage approach to overcoming the infeasibility of SBM super-efficiency model in data envelopment analysis[J]. Mathematics in Practice and Theory, 2013, 43(5): 171-178. doi: 10.3969/j.issn.1000-0984.2013.05.026 任国平,刘黎明,付永虎,等. 基于GWR模型的都市城郊村域农户生计资本空间差异分析——以上海市青浦区为例[J]. 资源科学,2016,38(8): 1594-1608.REN Guoping, LIU Liming, FU Yonghu, et al. Spatial differentiation of rural household livelihood capital in metropolitan suburbs based on GWR model: a case study of Qingpu District in Shanghai[J]. Resources Science, 2016, 38(8): 1594-1608. 叶显严. 基于地理加权回归的城市出租车客流影响因素分析及建模[D]. 成都: 西南交通大学, 2017. 程利莎,王士君,杨冉,等. 中国省际交通运输业能源效率测度及时空分异研究[J]. 东北师大学报(自然科学版),2019,51(1): 145-153.CHENG Lisha, WANG Shijun, YANG Ran, et al. Research on energy efficiency and its spatio-temporal differentiation of China's transportation industry[J]. Journal of Northeast Normal University (Natural Science Edition), 2019, 51(1): 145-153. 国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2018: 553-603. 国家统计局. 中国城市统计年鉴[M]. 北京: 中国统计出版社, 2018: 12-20. 中华人民共和国住房和城乡建设部. 中国城市建设统计年鉴[M]. 北京: 中国统计出版社, 2018: 42-419. 国家统计局. 中国能源统计年鉴[M]. 北京: 中国统计出版社, 2018: 142-322. -
下载:
点击查看大图
图(1) / 表(4)
计量
- 文章访问数: 309
- HTML全文浏览量: 143
- PDF下载量: 26
- 被引次数: 0