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In order to relieve traffic pressure, reduce investment in road repair, reduce vehicle delays, save energy and reduce emissions, and build a harmonious traffic environment, designing intelligent traffic signal control is an inevitable choice for building a resource-saving society.
Existing traffic signal control systems are mainly divided into two categories: timing control and inductive control. Timing control cannot dynamically adjust the delay time adaptively according to the traffic flow of the vehicle, which may cause long delays and unnecessary congestion of the vehicle: Inductive control can adopt different control modes according to the state of the traffic flow, but the current research is large Some can only control a certain point individually, and cannot control real-time, multi-point, joint measurement, and linkage.
This paper designs an intelligent traffic control based on WSN’ target=”_blank”> wireless sensor network, which uses sensor nodes to collect traffic information. The intelligent traffic control terminal selects the appropriate intersection control mode and adjusts each intersection according to the collected traffic information. The green signal ratio of intersections, coordinate the determination of the cycle of each intersection of the main line and the phase difference between each intersection, and adaptively control the traffic time of vehicles to ensure the quality of vehicle traffic and realize the intelligent and networked traffic signal control.
1 intersection control mode
The traditional intersection control mode is timing control, and the advanced intersection control modes include fuzzy control, green wave band mode, night mode and emergency stop mode. Fuzzy control mode is to intelligently complete fuzzy increase and decrease of traffic signal control time based on random vehicle flow. During the peak period of one-way vehicles, the green wave belt mode delays the initial lighting time of the traffic lights between each intersection for a certain amount to ensure that the vehicles can run smoothly all the way. The night control mode can be used when the vehicle flow is at zero load at night. It only uses yellow lights to warn the driver, reducing energy and time consumption. The emergency stop mode can open up traffic space for emergency vehicles, turn on the green light in the direction of the emergency vehicle, and turn on the red light in other directions. This design proposes to adopt different control modes in different time periods, and adopt fuzzy control mode in the time periods of 9:00～11:30, 14:30～17:30 and 20:30～24:00; at 5:30～9: 00,11:30～14:30 and 17:30～20:30 time period adopts green wave band mode; during 0:00～5:30 time period adopts night control mode; when an emergency vehicle is detected, adopts emergency stop control mode. The setting of the specific time period can be reset or modified by the signal machine according to the specific area or vehicle flow. Choosing multiple control modes can realize the rationalization of traffic control and actually relieve the pressure on traffic intersections.
2 Intelligent traffic control design
2.1 Intelligent traffic control model based on multi-agent
Multi-Agent System (MAS) has always been a research hotspot in the field of artificial intelligence. MAS has the advantages of initiative, hierarchy, dynamics and maneuverability. In MAS, collaboration can not only improve the overall behavior of a single agent and the system formed by multiple agents, enhance the ability of the agent and multi-agent systems to solve problems, but also make the system more flexible. Studies at home and abroad show that. Compared with traditional modeling methods (such as reductionist methods, inductive reasoning methods, etc.), MAS modeling can better describe the characteristics of complex systems. MAS modeling is mainly used to represent complex situations (individuals have complex, different behaviors, and interactions). The modeling of multi-Agent complex adaptive systems is an important method of complex system modeling. Interaction and collaboration between multi-Agents It is the key for multiple Ag-ent individuals to achieve multiple goals in an open and dynamic environment with limited resources.
The traffic signal control system is a typical complex large-scale system with time-varying and nonlinear characteristics. It is composed of many closely related and complex subsystems in different fields and functions at different levels. At present, various transportation subsystems are operated in accordance with their own optimization goals, and integration and collaboration with other systems are not considered, making it difficult for the transportation system to achieve the optimal. Comprehensive analysis and coordination of various transportation subsystems is the development trend of intelligent transportation. In this paper, a multi-agent-based intelligent traffic control model is constructed. The control model is shown in Figure 1.
In Figure 1, the functions of each Agent in the intelligent traffic control model are as follows:
1) The traffic management agent helps to establish the communication link between other Agem, and is also responsible for the addition and deletion of the system management multi-agent.
2) Data Management Agent Data Collection Agent uses sensor nodes to collect state vector information such as vehicle flow on the road; Data Processing Agent calculates and processes the collected vehicle information data, and calculates various control vectors for traffic control at each intersection; The data transmission agent transmits data to the intelligent traffic control database and communicates with other data sources; the data backup and recovery agent backs up and restores the data in the intelligent traffic control database.
3) The intelligent traffic control agent determines the time period for the agent to obtain the control at all times; the mode selects the agent to generate the intersection control mode; the control method agent decomposes the control task and sends it to the green letter ratio agent, the phase difference agent, the cycle agent, and the integrated control agent to complete a single The individual control of the control vector or the comprehensive control of multiple control vectors. At the same time, it is also responsible for obtaining the final control result from the comprehensive control agent and outputting it to the corresponding user. The Green Letter Ratio Agent, Phase Difference Agent, and Periodic Agent are the main bodies to complete the control task. They control the green signal ratio, phase difference and period, and then send the results to the integrated control agent. The integrated control agent obtains the output results of the green signal ratio, phase difference and cycle control agent and uses the integrated control method to summarize the results to obtain the intelligent control value of each intersection, generate the intelligent control summary data report of the intersection, and send the final result Give the control execution agent. The control execution agent executes the intelligent control results of each intersection.
2.2 Traffic signal control based on wireless sensor network
Wireless sensor network is an emerging subject that integrates computers, communications, networks, intelligent computing, sensors, embedded systems, microelectronics and other fields. Network in one) to form an autonomous wireless network to realize dynamic collaborative perception of the physical world. It can obtain the sensory information of the physical world in real time and dynamically, and integrate relevant information with the communication backbone network to realize the extension of the existing computer network virtual world to the real physical world and change the way humans interact with nature.
The wireless sensor network used to construct the traffic information system has the following advantages: 1) Its wireless self-organization and ubiquitous coordination characteristics make the system deployment and maintenance very convenient, can reduce user costs, and will not affect the normal driving of the vehicle during deployment and maintenance. It is convenient to improve the scalability of the traffic information collection system; 2) The large-scale distributed monitoring and collaborative computing technology is superior to the traditional single-point or partial monitoring technology in terms of ability.
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