MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

Qin Lin, Yihuan Zhang, Sicco Verwer, Jun Wang

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
192 Downloads (Pure)


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

Bibliographical note

Accepted author manuscript


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


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