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Qu, C. (author), Zhan, X. (author), Wang, Guanghui (author), Wu, Jianliang (author), Zhang, Zi-Ke (author)
Many systems are dynamic and time-varying in the real world. Discovering the vital nodes in temporal networks is more challenging than that in static networks. In this study, we proposed a temporal information gathering (TIG) process for temporal networks. The TIG-process, as a node's importance metric, can be used to do the node ranking. As...
journal article 2019
document
Lin, Q. (author), Zhang, Yihuan (author), Verwer, S.E. (author), Wang, Jun (author)
This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like...
journal article 2019