Visualizing road connectivity under traffic influence: a CiteSpace-based method

Authors:
raw_author_name: Xiaohan Liu
raw_affiliation_string: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Tianyu Liu
raw_affiliation_string: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Xi Wang
raw_affiliation_string: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Meng Gao
raw_affiliation_string: College of Earth and Environmental Sciences, Lanzhou University, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
raw_author_name: Tao Pei
raw_affiliation_string: Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China

Abstract: The connectivity of roads under the influence of traffic is determined by the topological connections established by the road network and the transport connections formed by moving vehicles between two roads simultaneously. A thorough examination of this connectivity is crucial for comprehending traffic patterns and providing guidance to address transportation issues such as traffic congestion. However, existing methods ignore the intuitive representation and explanation of this connectivity, limiting the understanding of actual traffic patterns. This study aimed to intuitively visualize the connectivity of roads under the influence of traffic using CiteSpace, a bibliometric tool for analyzing the relationship between literature keywords. Here, the literature was replaced by trajectory, and keywords were replaced by roads. We then presented the connectivity of roads under the influence of traffic in a network, where nodes represented roads and edges represented the topological and transport connections between roads. A case study was conducted in Beijing, demonstrating the dynamic connectivity of roads under the influence of traffic in the visualized network. By comparing the visualized network with the topological road network, we identified three types of road connectivity under the influence of traffic: roads with topological connections but lacking transport connections, roads with transport connections but lacking topological connections, and roads with both topological and transport connections. This finding further revealed the traffic relationship between roads. Additionally, we simultaneously presented the multidimensional traffic characteristics in the visualized network, aiding in predicting traffic states.