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

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.

K EY P OLICY H IGHLIGHTS

• Road connectivity was visualized considering topological and transport connections simultaneously.

• Three types of road connectivity under traffic influence were revealed using CiteSpace.

• Association rules predicted relationships of traffic characteristics to congestion.

KEYWORDS:

• CiteSpace
• visualization
• connectivity
• traffic pattern
• road network

Acknowledgments

The authors express gratitude to the editor and the anonymous reviewers for their helpful comments on an earlier draft of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The participants of this study did not provide written consent for their data to be shared publicly; hence, due to the sensitive nature of the research, supporting data are not available.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2024.2310884.

Additional information

Funding

This work was supported by the National Key Research & Development Program of China [Grant No. 2022YFC3800803] and the National Natural Science Foundation of China [Grant Nos. 42071436, 42101431].