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With the development of the national economy, the demand for all types of traffic increasing traffic problem has now become one of the countries in the world most important issues of concern. Traffic flow theory research goal is to establish a traffic flow model to describe the general characteristics of the actual traffic, in order to reveal the basic law of the movement of the traffic flow, in order to prevent and relieve traffic congestion, and provide a basic guarantee for the harmonious development of society. To carry out the study of the theory of traffic flow, not only has far-reaching scientific significance, but also has an important value in engineering. Nonlinear characteristics while emerging as a microscopic model of traffic flow, traffic flow model based on cellular automata retain complex transportation system, and also has many advantages of computer simulation speed, flexible rules, has been The traffic Scientists widely adopted. In this paper, based on cellular automata theory, cellular automaton traffic flow model, modeling, simulation and research directions of research, mainly include the following: First, in the NS model based on an improved the cellular automata model to simulate the bicycle trail traffic flow under periodic boundary conditions. Take into account the different speeds, different safety car should have asked the distance, reaction time and deceleration distance, according to the spacing between the speed of the vehicle, and the vehicle in front following vehicles to determine the movement of the car, so that you can indirectly reflect the front with Chi vehicle to the current vehicles. Moving at different speeds of the vehicle close to the front of the vehicle when the deceleration behavior can be described by introducing a different security spacing. Since the introduction of the different security spacing, and taking into account the differences in speed, and thus can describe different phenomena, in the traffic flow can be more reasonable description of the micro-movement of the vehicle. On the other hand, in order to make the traffic flow model based on cellular automata closer to reality, this paper explore the establishment of a cellular automata model based on fuzzy control. Taking into account the actual traffic conditions, driver information such as speed, distance perception is fuzzy, fuzzy inference mechanism can be introduced into the random vehicle slow process. Fuzzy control rules, the current vehicle-vehicle distance and speed of the vehicle and the driver's reaction time as fuzzy controller input elements. After a series of fuzzy reasoning, and ultimately figure out the probability of the current slow random vehicles this moment. Through the computer simulation can be found: Compared with NS model, the vehicle's random slow probability is no longer fixed, but are determined according to the spacing of the vehicle, the traveling speed of the vehicle and the driver's reaction time, so can better reflect the actual traffic environment impact on driver behavior.
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