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 cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models.

Original languageEnglish
Pages (from-to)790-796
Number of pages7
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number2
Publication statusPublished - 2019

    Research areas

  • car-following behavior, Computational modeling, Data mining, Data models, Hybrid automaton, Learning automata, Numerical models, simulation and control., Time series analysis, Vehicles

ID: 45517217