Finite State Machines and Timed Automata: A Hierarchical Approach for Integrated Traffic Microsimulations

Microscopic traffic simulations capture the trajectories of individual drivers as responses to stimuli from their surroundings (i.e. other vehicles or road conditions). Mathematically, these models are usually designed with differential equations or as sets of integerbased rules. Since both approaches have disadvantages, we propose an in-between approach built with Timed Automata and Finite State Machines (FSM) to reproduce the human behaviour. The fundamental idea is to model the switches between a limited set of discrete acceleration levels with a FSM and derive all other trajectory features from there. The duration for which this constant acceleration is maintained is not fixed and is modelled by a (probabilistic) Timed Automaton (TA). With this arrangement, the complexity of CF behaviour can be represented with high computational efficiency in large-scale simulations without sacrificing model fidelity. It also captures the intrinsic randomness in human driving and enables the incorporation of directly observably statistical CF properties. This paper identifies the best-correlated stimulus-response factors, analyses state machine properties of certain trajectory features and finally shows how several state machines can be hierarchically organised with the subsumption architecture. 