论文标题
通过结合道路网络抽象模型和出租车轨迹数据，通过探索性分析来揭示城市内空间结构
Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data
论文作者
论文摘要
中国空前的城市化已大大改变了城市的城市空间结构。随着个体地理空间大数据的扩散，先前的研究已广泛使用网络抽象模型来揭示潜在的城市空间结构。但是，网络抽象模型的构建主要集中在道路网络的拓扑上，而无需考虑与道路网络一起进行单独的旅行流。单个旅行流反映了城市动态，这可以进一步帮助理解潜在的空间结构。因此，这项研究旨在通过整合道路网络抽象模型和单个旅行流来揭示城市内空间结构。为了实现这一目标，我们1）使用大量出租车旅行数据量化了基于Word2Vec模型的道路段的空间相互作用相关性，然后2）根据已确定的空间交互相关性来表征道路抽象网络模型，3）实施一个社区检测算法以揭示城市的子区域。我们的结果揭示了武汉大都市地区的三个层次层次空间结构。这项研究通过确定道路网络上的交通交互模式，为城市空间结构进行调查提供了一种数据驱动的方法，为城市规划实践和运输管理提供了见解。
The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management.
