单片机控制的简单计算器外文文献(3)

intersection to another one at an expected constant speed. The vehicles are grouped in platoons of varied sizes, determined by signal timings, which progress through the green wave at uniform speed.

Thus, effective adaptive models aim to coordinate signals of controlled intersections so as to improve performances and reduce congestion in urban networks areas.

Generally, adaptive traffic control systems are defined as the application of computing, information, and communications technologies to the real-time vehicles management.

The major researches are focused on Artificial Intelligence (AI) techniques. IA

provides to be a highly promising field for solving the traffic control problems. Indeed, fuzzy systems, artificial neural networks, multiagent systems, evolutionary computing and swarm intelligence are effective computing control tools in dealing with

complexity and dynamics of traffic situation. Implementation of these traffic lights controls is supposed respond to traffic network demand, adapt timing plans in time, and implement real-time control.

In this paper, an adaptive traffic lights control scheme in multiple intersections is proposed based on multi-agents framework. Multi-Agents Systems (MAS) provide an intelligent approach for addressing the realtime traffic signal control problem, given the distributed nature of traffic flow information. The data provided by the sensor detection equipment are assumed to be available to the proposed traffic control system.

First, prior researches in intelligent traffic signal control are reviewed in this paper. Adaptive traffic signal control problem is addressed. Our basic approach to modeling traffic flows and making traffic signal control decisions within each agent is described in Sections III. In Sections IV, experimental results that indicate the performance characteristics of the proposed approach are presented. Finally, in Section V, summarize different results of the present work and indicate guidelines for future research work.

2. Related works

The traffic congestion problem has motivated researchers to study new control strategies to efficiently manage the traffic movement in urban area. Artificial

intelligence techniques have been widely investigated urban traffic control in different areas. In this section, related works of control traffic lights and principally associated intelligent agent’s applications are presented.

2.1. Knowledge based traffic lights models

Initially, Fuzzy logic (FL) field is adopted to model expert’s knowledge by adjusting traffic signal control parameters such cycle length and phase sequence. FL signal controllers [1–5] use a set of rules to determine the preferred action based on a number of inputs. Although results have shown that these methods arecapable of effective control within small networks, the use of a static rule-base implies that further updates to the system may be required over time. Hence, it is difficult to generate an efficient model, especially for complex intersections.

Decision support systems (DSS) consists of computer technology solutions that can be used to support complex decision making and aims to assist human operator in selecting effective measures. Many DSS traffic control systems were developed such Hoogendoorn and Almejalli models [6, 7]. While it has been demonstrated that these

systems are capable of suggesting effective actions, they rely on large databases of historical traffic data and expert knowledge, which may be hard to acquire and maintain. In addition, the data-bases must be constantly updated with new traffic scenarios and control action.

2.2. Neural network traffic lights models

Artificial neural networks (ANNs) have been extensively explored as approaches for decision making. Thus, the authors in [8, 9] have successfully proposed ANN to train a traffic signal controller. ANN Models compute decisions by learning from

successfully solved examples. However, as the size of the road network increases, neural networks suffer significant performance degradation. Moreover, when traffic volumes change, they have to relearn an effective method of control.

2.3. Queue theory traffic lights models

The theory of queues is also a widely used approach when it comes to model the tails present at intersections [10, 11]. The queuing theory algorithm uses the law of “Little” in order to determine the lengths of queues. Using queues information? the model is able to develop a dynamic plan for traffic lights in order to reduce the queue average length as well as the average waiting time. However, this theory requires a number of measuring points to manage the intersection and cannot deal with minor conflicts.

2.4. Agent based traffic lights models

Multiagent Systems (MAS) is an IA tool that provides principles for modeling of complex systems involving multiple agents with centralized and/or decentralized coordination mechanisms. There are many researches in traffic lights modeling using MAS. Thus, Dresner and Stone [12] have tackled the problem using a reservation system for collision avoidance at an intersection. In this system, vehicles make a request to a central agent. If the request is accepted, the vehicle needs to follow the prescribed path, to be safe while crossing the intersection. Preliminary …… 此处隐藏：3311字，全部文档内容请下载后查看。喜欢就下载吧 ……