**5. Conclusions**

We presented a novel method for describing the dynamic circumstances around any given traffic links. Specifically, by using a new measure called traffic flow centrality, we were able to concisely express the dynamics of the traffic flow in and out of any given link. Through this measure, we can reflect on the inherently complex ways in the interconnected traffic links affecting each other. Combined with the information about the external conditions surrounding the links, e.g., climate and time of day, the new features and their temporal patterns are used for predicting traffic speed. According to the information available from TOPIS (Seoul Traffic Operation and Information service), training with the LSTM algorithm given our comprehensive spatio-temporal features yielded the lowest prediction error with an MAPE of 10.39. Our experiment also shows that our solution is easily applicable to large-scale traffic systems. We could predict the traffic speed on the road network with thousands of links, unlike the existing works without any efficient feature embedding approaches.

As future work, we plan to apply our solution to the nation-wide traffic system.

**Author Contributions:** Conceptualization, Y.Y.; methodology, C.L. and Y.Y.; software, C.L.; validation, C.L. and Y.Y.; formal analysis, C.L. and Y.Y.; investigation, C.L.; resources, C.L.; data curation, C.L.; writing, original draft preparation, C.L. and Y.Y.; writing, review and editing, C.L. and Y.Y.; visualization, C.L.; supervision, Y.Y.; project administration, Y.Y.; funding acquisition, Y.Y. All authors read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Korean Agency for Infrastructure Technology Advancement (KAIA) gran<sup>t</sup> funded by the Ministry of Land, Infrastructure, and Transport (Grant 20TLRP-B148970-03), by the Basic Science Research Programs through the National Research Foundation of Korea (NRF) funded by the Korea governmen<sup>t</sup> (MSIT) (2020R1F1A104826411), by the Ministry of Health & Welfare, Republic of Korea (Grant Number HI19C0542020020), by the Ministry of Trade, Industry & Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT), under Grants P0004602 and P0014268 Smart HVAC demonstration support, and by the 2020 Hongik University Research Fund.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
