Highway traffic flow cellular automaton simulation method

A technology of flow cell machine and simulation method, which is applied in design optimization/simulation, special data processing applications, instruments, etc. It can solve the problem that the simulation of vehicle initialization does not take into account the relationship between real flow and density, and achieve accurate simulation results Effect

Active Publication Date: 2017-07-28
SOUTHEAST UNIV
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AI Technical Summary

Problems solved by technology

[0003] The current cellular machine model does not take into account the corresponding relationship between real traffic and density, the number of lanes in the case of multiple road sections, and the impact of vehicle density changes in the simulation of vehicle initialization; how to use real traffic road data, To reflect as realistically as possible into the cellular machine model is a problem in the current existing technology

Method used

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  • Highway traffic flow cellular automaton simulation method
  • Highway traffic flow cellular automaton simulation method
  • Highway traffic flow cellular automaton simulation method

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Embodiment Construction

[0029] The invention discloses a method for simulating expressway traffic flow cellular machine that integrates changes in vehicle density and lane number in multiple road sections. First, vehicle data and road data are initialized, and then iterated in the cellular machine. Finally, the The results are visualized. The following is the specific execution process that we extract the vehicle information of the US Highway 405 as input data, such as Figure 5 It is the system flow chart of the cellular automata:

[0030] (1) Initialize the vehicle specification parameter data and the parameter data of the real road section:

[0031] (1-1) Setting the vehicle specification

[0032] Parameter data, as shown in Table 1 below:

[0033] Table 1

[0034] parameter value length car length

4.5m+1.5m (safety distance) maximum driving speed car max_velocity

60 miles per hour (miles / h) stochastic slowdown probability car slow

0.1 Slow start...

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Abstract

The invention discloses a highway traffic flow cellular automaton simulation method. Multi-segment vehicle densities and lane number change are fused, and a simulation model of a cellular automaton established based on the real highway flow condition, multi-segment different road characteristics and vehicle conditions is established. Detected flow is mapped to road densities for road state simulation, and real measured data and the vehicle number of the cellular automaton are mapped. A novel boundary condition of the cellular automaton is provided, the lane number change of a highway is considered, the densities of multiple segments are different, work of vehicle information transfer between lanes is involved, the traffic flow simulation result is more precise through inspection, and actual traffic flow data can be simulated.

Description

technical field [0001] The invention relates to the technical field of traffic system planning, in particular to a cell machine simulation method for expressway traffic flow. Background technique [0002] The research goal of traffic flow theory is to establish a set of traffic flow models that can describe the general characteristics of traffic, find the basic laws of traffic flow, and reveal the mechanism of traffic congestion. According to different research methods, various traffic flow models can be divided into microscopic car-following models and cellular machine models, as well as macroscopic continuous models and mesoscopic aerodynamic theoretical models. The cellular machine was proposed in the 1980s and developed rapidly in the 1990s. People apply the theory of cellular machine to the research of traffic flow, adopt discrete space-time and state variables, stipulate the evolution rules of vehicle movement, and reveal the law of traffic operation through the avera...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 陈雄辉伍家松
Owner SOUTHEAST UNIV
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