Based On The Modeling Of Flow Stochastic Petri Net Traffic Flow Guidance System And Path Optimization

Now, traffic condition is becoming one of the important factors that restrict the national economic development as society improvement and technology advancing. So, it is necessary to improve the efficiency of the traffic systems in being by controlling the traffic systems reasonably and scientificly. Traffic flow guiding system imposed in this thesis is just a kind of intelligentized method to improve the traffic condition effectively. The modeling of the traffic system and the route optimizing, which are basis of the traffic guiding system, are still very difficult. Although there have been many results, it is very difficult to make some major breakthroughs in dealing with the probability and fuzzy of the traffic network, practicality and accuracy of the road impedance function, and the optimizing algorithm based on route.To solve problems mentioned above in traffic flow modeling, this thesis presents a new kind of traffic flow modeling method. By imposing fluid stochastic Petri net on traffic flow model and considering the probability, dynamics and fuzzy of the traffic flow, and gives (1) Dynamic stochastic fluid Petri net model which is a kind of fluid stochastic Petri net model considering the dynamic property of the system. It can solve the real time and stochastic problem via the dynamic and stochastic property of this model. (2) Fuzzy dynamic stochastic fluid Petri net model, which adds fuzzy to the dynamic stochastic fluid Petri net model. In addition, it can deal with fuzzy factor such as behavior and psychology of the driver in traffic flow modeling.To solve problems mentioned above in route optimizing, this thesis firstly does some research on key issue in route optimizing, i, e. determining road impedance function. Considering the accuracy and practicality of road impedance sufficiently, this thesis proposes road impedance function of dynamic and stochastic property, which gives theory to optimize the route. In terms of optimizing aigorithm, we adopt genetic algorithm to optimize the route in order to improve the efficiency of the optimizing algorithm. Finally, we constructed a simulation platform of the traffic flow system, with which we demonstrated the efficency of the genetic algorithm by comparing it with the Dijkstra algorithm. This evaluates the advantage and drawback of the traffic flow modeling and the route optimizing method adopted in this thesis.