Abstract
This paper describes continuation of the authors’ work in the field of traffic flow mathematical models based on the cellular automata theory. The refactored representation of the multifactorial traffic flow model based on the cellular automata theory is used for a representation of an adaptive deceleration step implementation. The adaptive deceleration step in the case of a leader deceleration allows slowing down smoothly but not instantly. Concepts of the number of time steps without conflicts and deceleration aggressiveness coefficient are introduced. Also in this paper a new step type for models based on the cellular automata is formulated on the example of a stop signal. The new step type unites notification and signalization steps. The new step type extends the concept of the three-stepped unified representation of the traffic flow models based on the cellular automata that was formulated in the previous authors’ work.
Highlights
In the paper [1] the three-stepped unified representation of the traffic flow models based on the cellular automata was introduced
In the paper [2] the way how the refactoring approach could be applied for several cellular automata traffic flow models was demonstrated
On the example of this step which notify other drivers about the velocity decreasing there could be introduced a new common step type for the traffic flow models based on the cellular automata theory
Summary
In the paper [1] the three-stepped unified representation of the traffic flow models based on the cellular automata was introduced. This unified representation included three step types of the cellular automata work: velocity changing, validation and driving. In the paper [2] the way how the refactoring approach could be applied for several cellular automata traffic flow models was demonstrated. The work algorithm of this model is much easier to analyze for the refactored representation than for the original one This simplified analysis allows to identify steps which behavior could be improved to achieve more realistic results of the modeling process. – probability of the slow-to-start rule triggering; – limit distance when the slow-to-start rule is still applicably; – probability of the spatial anticipation rule triggering; – limit distance when the spatial anticipation rule is still applicably; – sign that the stop-light is on; – determines if a full stop of the vehicles is allowed
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