Sustainable station-level planning: An integrated transport and land use design model for transit-oriented development

被引:80
|
作者
Ma, Xiaolei [1 ]
Chen, Xi [1 ]
Li, Xiaopeng [2 ]
Ding, Chuan [1 ]
Wang, Yinhai [3 ]
机构
[1] Beihang Univ, Sch Transportat Sci & Engn, Beijing Key Lab Cooperat Vehicle Infrastruct Syst, Beijing 100191, Peoples R China
[2] Univ S Florida, Dept Civil & Environm Engn, Tampa, FL 33620 USA
[3] Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
Transit-oriented development; Land use; Sustainable development; Genetic algorithm; GENETIC LOCAL SEARCH; BUILT ENVIRONMENT; PROGRAMMING-MODEL; RAIL TRANSIT; CONSUMPTION; CONGESTION; ALGORITHMS; DEMAND; CHINA;
D O I
10.1016/j.jclepro.2017.09.182
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Urban rail transit system in China has been rapidly constructed in response to the effects of urbanization, such as severe urban congestion and excessive air pollution. The sustainable land use planning (i.e. transit-oriented development, TOD) around the subway stations is important for the rail transit system because of its long-term influence on travel demand. However, there are limited studies that focus on the station level TOD planning. In this context, the aim of this study is to propose a multi-objective programming model that integrates transport and land use design for station-level TOD planning. In this study, one subway station in Beijing City is taken as the case, considering the unique features of urban development (e.g. high density and diversity), five objectives are taken to account in our model, including rail transit ridership, compactness, accessibility, conflict degree, and environmental effects. Meanwhile, an improved immune-genetic based algorithm is designed to obtain the optimal solutions under alternative land use schemes. The model results show that the proposed algorithm is superior to conventional genetic algorithms. This study is hoped to provide sustainable station-level planning for urban planning decision-makers. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1052 / 1063
页数:12
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