城市能耗占全球能耗的比重随着城市化率的不断提高而增大,交通能耗作为城市能耗的重要构成部分,已有较多研究证明城市形态对其具有显著影响,这些研究多属于城市间层次,而少有城市内的研究对城市形态与交通能耗、环境影响的关系进行定量识别。本文拟建立城市形态、交通能耗和环境的集成模型,对单一城市内的不同空间组织(即城市形态),如土地使用方式、开发密度、就业中心的数量和分布等,对潜在的通勤交通能耗和环境影响的关系进行定量识别。该模型采用多智能体(multi-agent) 方法,一方面针对同一假想空间采用蒙特卡洛方法根据约束条件生成多个城市形态,并采用就业地斑块数目、平均斑块分形指数、香农多样性和平均近邻距离等14 个指标表征城市形态。另一方面,固定数量的居民agent 在所生成的每个城市形态内,选择居住区位和就业区位,根据通勤距离和社会经济特征选择交通方式,进而计算通勤交通能耗和环境影响,在城市层面统计通勤交通能耗和环境影响总和。最后分析城市形态与通勤交通能耗和环境影响的定量关系,主要得到以下结论,① 对于不同的城市空间布局和密度分布,通勤交通能耗的弹性范围约为3 倍；② 城市形态评价指数中,就业中心斑块的数量是对通勤交通能耗影响最大的变量；③ 多种城市形态所对应的通勤交通能耗基本呈正态分布。此外,还对城市形状对通勤交通能耗的影响进行了识别,并针对假想空间的多个典型城市形态(如紧凑与分散、单中心与多中心、TOD政策、绿隔政策),进行了通勤交通总量的计算,进而对典型规划理念进行了定量对比。本模型不仅可以用于识别城市形态与通勤交通能耗和环境影响的定量关系,定量对比典型的规划理念,还可以用于空间规划方案的能耗和环境影响评价。
Cities are consuming more energy with increasing urbanization process. The urban transportation energy is the primary part of urban energy consumption. Extensive researches found that it has strong relationship with the urban form, which fall into intra-cities level. However, little attention was paid to the relationship between urban form, transportation energy consumption, and environmental impact in the inner-city level. This paper aims to investigate the impact of urban form, namely land use pattern, development density distribution, on the residential commuting energy consumption (RCEC). We developed a multi-agent model for the urban form, transportation energy consumption and environment interaction simulation (FEE-MAS). Numerous urban forms with distinguished urban land use pattern and development density distribution are generated using the Monte Carlo approach in the hypothetical space. On one hand, the RCEC for each urban form is calculated using the proposed FEE-MAS. On the other hand, we select 14 indicators (e.g. Shape Index, Shannon's Diversity Index, Euclidean Nearest Neighbor Distance) to evaluate each generated urban form using FRAGSTATS, which is loosely coupled with the FEE-MAS model. Then, the quantitative relationship between the urban form and RCEC is identified based on 14 indicators and RCEC of each urban form. Several conclusions are drawn from simulations conducted in the hypothetical space. (1) RCEC may vary three times for the same space with various spatial layouts and density distribution. (2) Among selected 14 indicators for evaluating the urban form, the patch number of job parcels is the most significant variable influencing the RCEC. (3) The RCECs of all urban forms generated obey a normal distribution. (4) The shape of urban form also exerts influences on the RCEC. In addition, we evaluated several typical urban forms, e.g. compact/sprawl, single center/ multi-centers, traffic oriented development, green belt, in terms of the RCEC indicator using our proposed model to quantify those popular planning theories. The FEE-MAS model can also be applied for evaluating urban spatial alternatives in terms of energy consumption and environmental impact.
[1] Shen Qingji. Study on urban energy sustainable development: A view from urban planning. Urban Planning Forum, 2005,(6): 41-47. [沈清基. 中国城市能源可持续发展研究: 一种城市规划的视角城市规划学刊, 2005, (6): 41-47.]
[2] Wang Z W, Chen J. Achieving low-carbon economy by disruptive innovation in China. 2008 IEEE International Conferenceon Management of Innovation and Technology, 2008, (1-3): 687-692.
[3] Zhuang G Y. How will China move towards becoming a low carbon economy? China &World Economy, 2008, 16: 93-105.
[4] Hourcade J C, Crassous R. Low-carbon societies: A challenging transition for an attractive future. Climate Policy, 2008,(8): 607-612, doi: 10.3763/cpol.2008.0566.
[5] Remme U, Blesl M. A global perspective to achieve a low-carbon society (LCS): Scenario analysis with the ETSAP-TIAMmodel. Climate Policy, 2008, 8: 60-75.
[6] Shukla P R, Dhar S, Mahapatra D. Low-carbon society scenarios for India. Climate Policy, 2008, 8: 156-176.
[7] Johnson R A, Mccoy M C. Assessment of integrated transportation/land use models. Information Center for theEnvironment, Department of Environmental Science & Policy, University of California, Davis, 2006.
[8] Wang Jingwen, Mao Qizhi, Yang Dongfeng. Energy influences of urban planning: An urban transportation energyconsumption model. China Planning Annual Conference (Dalian), 2008. [王静文, 毛其智, 杨东峰. 城市规划的能源影响探讨: 规划视角中的城市交通能耗模型. 中国城市规划年会(大连), 2008.]
[9] Owens S. Energy, Planning and Urban Form. London: Pion, 1987
[10] Anderson W P, Kanaroglou P S, Miller E J. Urban form, energy and the environment: A review of issues, evidence andpolicy. Urban Studies, 1996, 33: 7-35, doi: 10.1080/00420989650012095.
[11] Newman P W G, Kenworthy J R. Gasoline consumption and cities: A comparison of US cities with a global survey.Journal of American Planning Association, 1989, 55: 24-37, doi: 10.1080/01944368908975398.
[12] Holden E, Norland I T. Three challenges for the compact city as a sustainable urban form: Household consumption ofenergy and transport in eight residential areas in the greater Oslo region. Urban Studies, 2005, 42: 2145-2166, doi:10.1080/00420980500332064.
[13] Shim G E, Rhee S M, Ahn K H et al. The relationship between the characteristics of transportation energy consumptionand urban form. Annals of Regional Science, 2006, 40: 351-367, doi: 10.1007/s00168-005-0051-5.
[14] Alford G, Whiteman J. Macro-urban form and transport energy outcomes: Investigations for Melbourne. Road &Transport Research, 2009, 18: 53-67.
[15] Dieleman F M, Dijst M, Burghouwt G. Urban form and travel behaviour: Micro-level household attributes and residentialcontext. Urban Studies, 2002, 39: 507-527, doi: 10.1080/00420980220112801.
[16] Giuliano G, Narayan D. Another look at travel patterns and urban form: The US and Great Britain. Urban Studies, 2003,40: 2295-2312, doi: 10.1080/0042098032000123303.
[17] Horner M W. A multi-scale analysis of urban form and commuting change in a small metropolitan area (1990-2000).Annals of Regional Science, 2007, 41: 315-332, doi: 10.1007/s00168-006-0098-y.
[18] Maat K, Timmermans H J P. A causal model relating urban form with daily travel distance through activity/traveldecisions. Transportation Planning and Technology, 2009, 32: 115-134.
[19] McMillan T E. The relative influence of urban form on a child's travel mode to school. Transportation Research Part A:Policy and Practice, 2007, 41: 69-79, doi: 10.1016/j.tra.2006.05.011.
[20] Pan H X, Shen Q, Zhang M. Influence of urban form on travel behaviour in four neighbourhoods of Shanghai. UrbanStudies, 2009, 46: 275-294, doi: 10.1177/0042098008099355.
[21] Schlossberg M, Greene J, Phillips P P et al. School trips: Effects of urban form and distance on travel mode. Journal of theAmerican Planning Association, 2006, 72: 337-346, doi: 10.1080/01944360608976755.
[22] Zhang M. Exploring the relationship between urban form and nonwork travel through time use analysis. Landscape andUrban Planning, 2005, 73: 244-261, doi: 1016/j.landurbplan.2004.11.008.
[23] Krizek K J. Residential relocation and changes in urban travel: Does neighborhood-scale urban form matter? Journal ofthe American Planning Association, 2003, 69: 265-281, doi: 10.1080/01944360308978019.
[24] Zhang Honghui, Zeng Yongnian, Jin Xiaobin et al. Urban land expansion model based on multi-agent system andapplication. Acta Geographica Sinica, 2008, 63(8): 869-881. [张鸿辉, 曾永年, 金晓斌等. 多智能体城市土地扩张模型及其应用. 地理学报, 2008, 63(8): 869-881.]
[25] Li Xia, Li Dan, Liu Xiaoping et al. Geographical simulation and optimization system (GeoSOS) and its cutting-edgeresearches. Advances of Earth Science, 2009, 24(8): 899-907. [黎夏, 李丹, 刘小平等. 地理模拟优化系统GeoSOS 及前沿研究. 地球科学进展, 2009, 24(8): 899-907.]
[26] Benenson I, Omer I, Hatna E. "Agent-based modeling of householders' migration behavior and its consequences"//BillariF C P A.Workshop on Agent based Computational Demography. Rostock, Germany, 2001: 97-115.
[27] Brown D G, Robinson D T. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl.Ecology and Society, 2006, 11(1), http://www.ecologyandsociety.org/vol11/iss1/art46/.
[28] Tao Haiyan, Li Xia, Chen Xiaoxiang et al. Method exploration of geographical spatial differentiation based onmulti-agent: A case study of urban residential simulations. Acta Geographica Sinica, 2007, 62(6): 579-588. [陶海燕, 黎夏,陈晓翔等. 基于多智能体的地理空间分异现象模拟: 以城市居住空间演变为例. 地理学报, 2007, 62(6): 579-588.]
[29] Yi C, Li Q, Zheng G. Commercial facility site selection simulating based on MAS//Proceedings of the SPIE: TheInternational Society for Optical Engineering, 2008: 71431N (71438 pp.)
[30] Kii M, Doi K. Multiagent land-use and transport model for the policy evaluation of a compact city. Environment andPlanning B: Planning and Design, 2008, 32: 485-504, doi: 101068/b3081.
[31] Zellner M L, Theis T L, Karunanithi A T et al. A new framework for urban sustainability assessments: Linking complexity,information and policy. Computers, Environment and Urban Systems, 2008, 32: 474-488, doi: 10.1016/j.compenvurbsys.2008.08.003.
[32] Long Ying, Shen Zhenjiang, Mao Qizhi. Retrieving individual attributes from aggregated dataset for urbanmicro-simulation: A preliminary exploration. Acta Geographica Sinica, 2011, 66(3): 416-426. [龙瀛, 沈振江, 毛其智. 城市系统微观模拟中的个体数据获取新方法. 地理学报, 2011, 66(3): 416-426.]
[33] Beijing Fifth Population Census Office, Beijing Statistical Bureau. Beijing Population Census of 2000. Beijing: ChineseStatistic Press, 2002. [北京市第五次人口普查办公室, 北京市统计局. 北京市2000 年人口普查资料. 北京: 中国统计出版社, 2002.]
[34] McGarigal K, Cushman S A, Neel M C et al. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps.Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at www.umass.edu/landeco/research/fragstats/fragstats.html, 2002.
[35] Waddell P. Modeling urban development for land use, transportation, and environmental planning. Journal of theAmerican Planning Association, 2002, 68: 297-314, doi: 10.1018/01944360208976274.
[36] Putman S H. Urban landuse and transportation models: A state of the art summary. Transportation Research, 1975, 9:187-202.
