文章中文名称：大都市圈外围城镇职住空间与交通模式研究——以京东燕郊镇为例

文章英文名称：Research on the Job-housing Space and Traffic Pattern of Peripheral Towns in Metropolitan Area: A Case Study on Yanjiao Town

作者信息

黄建中  同济大学建筑与城市规划学院教授

马煜箫  同济大学建筑与城市规划学院硕士研究生

胡刚钰  同济大学建筑与城市规划学院博士研究生，工程师 ( 通讯作者 )

张  乔  上海同济城市规划设计研究院有限公司主任规划师，高级工程师

邓  晶  上海云策规划建筑设计有限公司规划师

方文彦  上海同济城市规划设计研究院有限公司副主任规划师，工程师

摘要  随着大都市拓展以及区域一体化发展，职住空间的变迁对于大都市及外围城镇的发展有着深远的影响，交通一体化发展助推了都市圈空间结构的重塑。大都市圈核心区与外围城镇职住空间与交通模式的关系越来越受到关注，大数据的使用给研究提供了新的思路和视角。本文以京东燕郊镇为例，结合使用 LBS 大数据与传统调查数据，利用 VISUM 软件构建交通模型，采用“空间结构—交通模式” 多情景耦合分析方法，定量分析北京与燕郊之间的职住空间结构与交通模式的关系。研究建议通过设定与燕郊发展进程相匹配的“阈值”，制定职住空间和交通模式互动发展的政策，通过鼓励公共交通出行和限制个体交通使用的交通管理方式，从交通模式上反过来约束外围城镇的独立性，致力于形成大都市圈核心区与外围城镇之间良好的职住空间结构。

关键词 LBS 大数据，大都市圈，外围城镇，职住空间，交通模式

Abstract With the development of metropolis, the change of job-housing space has a far-reaching impact on the metropolitan area and peripheral towns. The development of traffic integration has promoted the remodeling of spatial structure of metropolitan area. More and more attention has been paid to the relationship between job-housing space and the traffic pattern in the metropolitan core area and peripheral towns. The use of big data provides new ideas and perspectives for research. Taking Yanjiao at the east of Beijing City as an example, this paper combines LBS big data with traditional survey data, builds a traffic model by VISUM software, and, based on “spatial structure-traffic mode”, uses the multi-scenario coupling analysis method to quantitatively analyze the relationship between the job-housing space and the traffic pattern between Beijing City and Yanjiao Town. This study suggests that “a threshold” matching the development process of Yanjiao Town should be set up to formulate the development policy of job-housing space and traffic pattern. By encouraging public transportation and restricting individual traffic use, traffic demand management can constrain the independence of peripheral towns, and strive to form a good spatial structure of job-housing space between the core of the metropolitan area and peripheral towns.

Keywords LBS big data, metropolitan area, peripheral towns, job-housing space, traffic pattern