城市建成环境绿色交通系统优化方法研究综述
Review on Optimization Methods of Green Transportation System in Urban Built Environment
王月涛
山东建筑大学建筑城规学院 副教授，博士
田昭源
山东建筑大学建筑城规学院 硕士研究生
薛滨夏
哈尔滨工业大学建筑学院 副教授，博士
李洪晶
山东建筑大学信息与电气工程学院 科员
马 涛
哈尔滨工业大学经济与管理学院 教授，博士生导师
摘要: 城市绿色交通系统包括轨道交通、公共汽车运输、共享单车和慢行步道等多层次复杂系统，具备实用性、便捷性、经济性及可持续性等多重特性，对于当前城市建成环境的低碳节能具有十分重要的意义。目前国内外相关领域的学者从多个角度和侧面对绿色交通体系优化进行了研究，形成了针对某些具体问题的分析和优化方法。以供需关系为基本线索，将城市绿色交通系统优化归结为交通承载力、交通需求量、耦合分析、多目标优化4个主要方面，归纳有关的研究与实践取得的成果。从宏观和微观两个视角评价现有研究存在的问题，并指出未来研究发展趋势。最后综合各技术的特点与优势，提出基于供需关系的城市建成环境下绿色交通系统优化框架。
Abstract: Urban green transportation system is a multi-level complex system including rail transportation, bus transportation, shared bicycles, and walkways. It has multiple characteristics such as practicality, convenience, economy and sustainability, which is of great significance to the current low-carbon and energy-saving built environment of the city. At present, scholars in related fields at home and abroad have studied the optimization of green transportation systems from multiple perspectives, and formed analysis and optimization methods for some specific problems. Taking the supply and demand relationship as the basic clue, the article attributes the optimization of urban green transportation systems to four main aspects, namely, traffic carrying capacity, traffic demand, coupling analysis, and multi-objective optimization, and summarizes the results achieved by the relevant research and practice. The article evaluates the problems of existing research from both macro and micro perspectives, and points out the development trend of future research. Finally, the article synthesizes the characteristics and advantages of each technique and proposes a framework for optimizing green transportation systems in urban built environment based on supply and demand.
关键词：城市绿色交通系统；城市建成环境；供需匹配；优化方法
Keyword: urban green transportation system; urban built environment; matching supply and demand; optimization method
中图分类号：TU981
文献标识码: A
资金资助
国家自然科学基金面上项目 水资源与能源约束下主体功能核算及实现机制研究 71974046
哈尔滨工业大学2021年学生未来技术创新团队建设项目 城市能源系统‘双碳’管理与建模学生未来科技创新团队 21650F
山东省自然科学基金面上项目 基于GIS空间分析技术的乡土聚落单元层进式保护研究 ZR2020ME213
VALDES J. Participation, equity and access in global energy security provision: towards a comprehensive perspective[J]. Energy Research & Social Science, 2021, 78: 102090.
NORDHAUS W D. The spirit of green the economics of collisions and contagions in a crowded world[M]. Princeton: Princeton University Press, 2021.
International Energy Agency. A tale of renewed cities: a policy guide on how to transform cities by improving energy efficiency in urban transport systems[R]. 2013.
European Environment Agency. Greenhouse gas emissions from transport in Europe[R]. 2019.
中国交通低碳转型发展战略与路径研究课题组. 碳达峰碳中和目标下中国交通低碳转型发展战略与路径研究[M]. 北京：人民交通出版社，2021.
Research Group on Low Carbon Transition Development Strategies and Pathways for China's Transportation. China's transportation low-carbon transformation development strategy and pathway under the target of emission peak and carbon neutrality[M]. Beijing: China's Communications Press, 2021.
张陶新. 绿色低碳交通[M]. 北京：中国环境出版社，2016.
ZHANG Taoxin. Green low-carbon transport[M]. Beijing: China Environment Publishing Group, 2016.
曹国华，夏胜国. 绿色交通规划关键问题研究——以中新生态科技岛为例[J]. 上海城市规划，2011（5）：103-108.
CAO Guohua, XIA Shengguo. Study on the key issues of green transportation planning: the case of Sino-Singapore Eco-Technology Island[J]. Shanghai Urban Planning Review, 2011(5): 103-108.
张润朋，周春山，明立波. 紧凑城市与绿色交通体系构建[J]. 规划师，2010，26（9）：5.
ZHANG Runpeng, ZHOU Chunshan, MING Libo. Compact cities and green transportation system construction[J]. Planners, 2010, 26(9): 5.
殷广涛，黎晴. 绿色交通系统规划实践——以中新天津生态城为例[J]. 城市交通，2009，7（4）：58-65.
YIN Guangtao, LI Qing. Green transportation system planning practice: a case study of Sino-Singapore Tianjin Eco-City[J]. Urban Transport of China, 2009, 7(4): 58-65.
ANSELIN L. What is special about spatial data[R]. 1989.
李旭宏，田锋，顾政华. 城市道路网供求分析技术[J]. 交通运输工程学报，2002（2）：88-90.
LI Xuhong, TIAN Feng, GU Zhenghua. Analysis technology of supply and demand of urban roadnetwork[J]. Journal of Traffic and Transportation Engineering, 2002(2): 88-90.
李小静，王花兰，范媛媛，等. 城市道路交通承载力研究综述[J]. 交通运输系统工程与信息，2022，22（6）：15-25.
LI Xiaojing, WANG Hualan, FAN Yuanyuan, et al. Urban road traffic capacity review[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 15-25.
LI D, TANG Y, CHEN Q. Multi-mode traffic demand analysis based on multi-source transportation data[J]. IEEE Access, 2020, 8: 65005-65019.
WANG Shiguang, YU Dexin, MA Xiaogang, et al.Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs[J].European Transport Research Review, 2018, 10: 50.
BELOKUROV V, BELOKUROV S V, KORABLEV R, et al. Models of expert assessments and their study in problems of choice and decision-making in management of motor transport processes[J]. Journal of Physics: Conference Series, 2018, 1015(3): 032132.
EVTIUKOV S, GOLOV E, GINZBURG G. Finite element method for reconstruction of road traffic accidents[J]. Transportation Research Procedia, 2018, 36: 157-165.
BELOKUROV V, SPODAREV R, BELOKUROV S. Determining passenger traffic as important factor in urban public transport system[J]. Transportation Research Procedia, 2020, 50: 52-58.
ZHENG Y, CAPRA L, WOLFSON O, et al. Urban computing: concepts, methodologies, and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2014, 5(3): 1-55.
WANG J, ZHANG M. Identifying the service areas and travel demand of the commuter customized bus based on mobile phone signaling data[J]. Journal of Advanced Transportation, 2021(s): 6934998.
张海林. 基于百度热力图的人口活动数量提取与规划应用[J]. 城市交通，2021，19（3）：103-111.
ZHANG Hailin. Extracting active population data based on Baidu Heat Maps for transportation planning applications[J]. Urban Transport of China, 2021, 19(3): 103-111.
FIEREK S, ZAK J. Planning of an integrated urban transportation system based on macro–simulation and MCDM/A methods[J]. Procedia - Social and Behavioral Sciences, 2012, 54: 567-579.
MENEGUETTE R I, DE GRANDE R E, LOUREIRO A A F. Intelligent transport system in smart cities: aspects and challenges of vehicular networks and cloud[M]. Cham: Springer International Publishing, 2018.
