面向交通控制的多智能体设计的理论与模型研究

VIP免费

3.0 赵德峰 2024-11-19 4 4 5.39MB 129 页 15积分

侵权投诉

摘要

城市交通的飞速发展带来了城市交通拥挤、交通延误、交通事故和交通污染

等一系列问题。解决城市交通问题的最直接的办法就是修建更多的道路和桥梁以

提高路网的通行能力。但是由于资金和城市空间限制，这一方法往往不是十分可

行。智能交通系统（ITS）的发展为解决城市交通问题提供了新思路。智能交通系

统包括城市道路交通信息采集、处理、发布、决策，它运用各种先进的技术和科

学方法，实现城市交通管理的自动化、现代化和智能化。智能交通系统不仅能提

高路网的利用率、交通的安全性，而且关系到土地资源和能源的合理利用、环境

污染和噪声的改善，乃至国民经济的持续发展和社会经济效益的全面提高。城市

交通信号控制系统作为智能交通系统中的一个重要子系统，已经成为当前研究的

热点课题之一。本文通过对城市交通信号控制问题的研究，寻求更有效的城市交

通控制方法，为缓解城市交通的拥堵问题，改善交通状况提供科学依据。

针对城市交通路网的实际情况，本文研究借鉴多智能体技术的概念和方法，建

立了多智能体城市交通信号控制系统（AUTCS）。系统包括三类信号控制智能体：

交叉口交通控制智能体、干线协调交通控制智能体和区域交通控制智能体。正常

交通流量下，各路口的主要控制工作由交叉口Agent给出控制策略；一般堵塞情况

下，由交叉口Agent和上游交叉口Agent协商解决；中等堵塞情况下，交叉口Agent

和干线控制Agent通信，将控制权交给干线控制Agent；严重堵塞情况下，由区域控

制Agent来进行整体控制。由于智能体都包含知识库和推理机制，使得这种具有自

主性、主动性、交互性的智能体交通控制不仅充分考虑了相关车队信息和周边路

口的交通状况，兼顾到路网交通控制整体优化的实现，而且能通过自学习不断增

加自身的控制经验，逐渐达到较优的控制效果。因此，对于大城市、特大城市的

大规模城市交通网，这种多智能体的城市交通信号控制系统，在实时控制效果方

面具有一定的优势。

本论文从单路口交通信号控制入手，将具有感知和反应特点的信号控制智能体

作为交叉口信号控制器，通过对到达车辆的模糊聚类处理形成对路口交通状态的

定量描述，根据交通控制规律和经验建立面向各种交通状态的信号控制规则集，

对交叉口车流进行实时控制。同时以车辆总延迟时间作为交通控制的优化指标，

采用PSO算法对信号控制规则进行优化，在整个信号过程中使用不同的规则组合进

行信号控制，淘汰控制指标较差的控制方案，对信号控制规则集进行持续的改进，

建立了一种具有学习能力的单交叉口交通信号控制模型。本文设计了基于博弈论

的智能体协调模型，当路口车辆较多，单个交叉口控制不能明显改善控制效果时，

交叉口智能体可和相邻交叉口进行协调，通过协调控制来改善控制效果。

本文系统地分析了干线信号协调控制的最佳适用条件，设计了干线控制智能

体，给出了干线智能体信号控制模型和自学习模型。当路口出现中等堵塞时，启

动干线智能体生成线控方案。干线智能体采用模糊聚类对交通状态进行了识别，

针对不同的交通状况采用不同的干线控制策略。非高峰期时，以车辆延误为控制

目标，上行高峰期或下行高峰期时，采用单向绿波控制，双向高峰期时，采用双

向绿波控制。线控模型的公共周期采用模糊算法确定，相位差优化模型采用Q-学

习算法确定。

本文研究还包括当干线智能体控制效果不佳，路口出现严重堵塞时，系统启动

区域智能体进行区域协调控制，实现整个控制子区域交通网络的最优控制。本文

设计了控制子区的动态划分方法，区域智能体以控制子区为优化对象，将控制子

区划分为若干干线，逐条干线进行控制。区域协调模型以平均延误最小、车辆平

均停车次数最小和路口排队长度最小等性能指标中的一项或几项构造目标函数，

进行优化。控制子区内各路口的周期和相位差由区域智能体采用模糊控制算法获

得，发送给干线智能体，再由干线智能体发送给路口智能体执行。

为了验证多智能体的城市交通信号控制系统的可行性和有效性，编制交叉口、

干线和区域控制智能体、结合AIMSUN仿真软件，模拟智能体交通控制，并对一

个由9个路口组成的交通网络进行仿真实验，模拟感应控制和多智能体交通信号控

制方法，分析比较不同控制方法的控制效果。仿真结果表明，在相同的条件下，

多智能体信号控制模型的路网平均延迟时间比感应控制方式平均减少15.7%，车辆

的平均速度提高了10.4%。

关键词：城市交通控制，多智能体系统，分布式控制，模糊算法，粒

子群算法，绿波控制，Q-学习算法，交通控制微观仿真

iii

ABSTRACT

With the rapid development of urban traffic, a series of problems are coming such

as urban traffic congestion, traffic delay, traffic accident and traffic pollution, and so on.

Obviously, building more roads and bridges are direct solution to resolve these urban

traffic problems, but due to the funds and the urban room, those methods are not

feasible. Development of intelligent transportation system (ITS) provides new solution

for urban transport problems. Intelligent transportation systems, including traffic

information collection, processing, publishing, decision-making process, uses a variety

of advanced technology and scientific methods to achieve automation, modernization

and intelligent of urban traffic management. The application of ITS will not only

improves the transportation utilization and safety, but also connects with land resources

and energies exploitation, environment improvement, national economic and social

revenue development. Urban traffic signal control system is one of the most important

subsystems of the ITS, is hot problem to study. By the research urban traffic signal

control, appropriate urban traffic signal control methods are seeking, for solving the

urban traffic congestion and congestion problems.

According to the actuality of traffic network, using the concepts and approaches of

multi-agent system, agent oriented urban traffic signal control system (AUTCS) is

established. It includes three types of agent, intersection control agent, trunk control

agent and area control agent. Under normal traffic flow, intersection agent gives

intersection control strategy; under light congestion, intersection agent and upstream

intersection agent negotiates control strategy; under middle congestion, intersection

agent communicates with trunk agent, hands over control to trunk agent; under serious

congestion, area agent performs overall control. Since agent contains knowledge base

and inference mechanism, the use of such autonomous, proactive, interactive agent in

signal control, not only gives full consideration to the vehicle query information and

traffic conditions around the intersection, taking into account optimization of overall

network traffic, but also greatly reduces the complexity of control algorithms. Therefore,

for large cities, large-scale urban transportation network, this multi-agent-based urban

traffic signal control system, has a considerable advantage in real-time traffic control.

This dissertation starts with the signal control of isolated intersection. It makes the

signal control agent with perception and reaction characters as the intersection signal

controller. It forms quantitative description for traffic states of intersection through

fuzzy classifying the arrived vehicles, constructs the signal control rules according to

the disciplines and the experience of traffic control for all traffic states, and performs

real-time control on intersection vehicles. Vehicle delay as the optimizing goal of traffic

control, Particle Swarm Optimization (PSO) algorithm is applied to optimize signal

control rules. During signal control process, different combinations of control rules is

applied and eliminates several schemes with worse results, thus the signal control rules

can be improved continuously. These measures form a signal control model with

self-learn ability for single intersection. This dissertation also puts forward an agent

coordination model based on game theory. Under the large traffic flow, single

intersection control can not improve the control effect obviously, intersection agent

negotiates with upstream intersection agent, realizes of the overall coordination control

to improve control effect.

This dissertation systematically analyzes the optimal application conditions of

trunk coordination control, designs trunk control agent, and gives control model and

self-learning model of trunk agent. Under middle congestion, trunk agent starts to make

line control strategies. Trunk agent identifies traffic state with fuzzy classifying methods,

applies different control strategies in different traffic conditions. In off-peak periods,

vehicle delay is control target; in up line or down line peak period, one-way green wave

control is applied; in bi-peak period, two-way green wave control is used. Common

signal control cycle in trunk model determined by fuzzy algorithm, phase offset

optimization uses Q-learning algorithm.

When trunk control results less effect, and serious congestion occurs, area control

agent performs overall coordination control. It optimizes whole traffic flow, and realizes

optimal control of regional transport network. The dissertation gives traffic area

dynamic partition method, area agent regards traffic area as optimizing object, divided

the area into a number of trunk lines, and control trunk one by one. Area coordination

model constructs objective function with one or several performance parameters,

including minimum travel delay time, minimum vehicle stop times, and minimum

vehicle query length, and performs optimization. Signal cycle and phase offset of

intersections in control area are obtained by fuzzy control algorithm, and sent to trunk

agent, then sent to intersection agent for implementation.For testing the feasibility and

effect of multi-agent oriented urban traffic signal control system, program of

intersection, trunk and area control algorithm is made, applies to AIMSUN simulation

system, and performs simulation in 9-intersection traffic network. AIMSUN simulates

control process of inductive control method and agent-oriented control method, analysis

and compares control effect of different methods. Simulation shows AUTCS signal

control approach reduces 15.7% average vehicle delay than the actuated method and

increases 10.4% average vehicle speed in the same condition.

Key Word: Urban Traffic Control, Multi-Agent System, Distributed

Control, Fuzzy Algorithm, Particle Swarm Optimization, Green Wave

Control, Q-learning, Microscopic Simulation of Traffic Control

中文摘要

ABSTRACT

第一章绪论··············································································· 1

§1.1 选题目的与意义·································································1

§1.2 城市交通控制研究的发展·····················································3

§1.2.1 交通信号控制的历史····················································· 3

§1.2.2 国外交通信号控制系统的发展········································· 3

§1.2.3 国内交通信号控制系统的发展········································· 5

§1.2.4 交通信号控制系统发展方向············································ 6

§1.2.5 Agent 技术在交通控制系统的研究现状······························ 6

§1.3 本文研究内容····································································8

§1.4 本文的特色与创新······························································9

第二章多智能体城市交通信号控制系统建模······································ 11

§2.1 智能体和多智能体系统······················································ 11

§2.1.1 智能体（Agent）的定义················································11

§2.1.2 多智能体系统（Multi-Agent System,MAS）·······················12

§2.1.3 多智能体交通控制系统的可行性和优越性·························12

§2.1.4 多智能体的城市交通信号控制系统的优势·························13

§2.2 AUTCS 的Agent 的设计····················································· 14

§2.2.1 Agent 的结构类型························································ 14

§2.2.2 Agent 的结构设计························································ 15

§2.2.3 Agent 的智能决策功能··················································16

§2.2.4 Agent 的自学习功能····················································· 17

§2.3 Agent 交通控制系统设计···················································· 18

§2.3.1 交通控制系统设计目标················································· 18

§2.3.2 交通控制系统的个体智能体···········································18

§2.3.3 Agent 交通控制系统结构···············································19

§2.4 交通控制 Agent 的通信······················································ 27

§2.4.1 Agent 的通信方式························································ 27

§2.4.2 交通控制 Agent 的通信设计··········································· 28

§2.4.3 交通控制 Agent 的拓扑结构··········································· 29

§2.5 交通控制 Agent 的实现······················································ 29

§2.5.1 CORBA 结构······························································ 29

§2.5.2 基于 CORBA 的Agent 开发步骤····································· 30

§2.5.3 基于 CORBA 的Agent 开发过程····································· 31

§2.6 本章小节········································································ 34

第三章单交叉口交通控制智能体模型··············································· 35

§3.1 单交叉口控制基本理论······················································ 35

§3.1.1 交叉口控制基本参数···················································· 35

§3.1.2 单交叉口信号控制模型················································· 35

§3.2 交叉口 Agent 的设计························································· 38

文档加载中……请稍候！
如果长时间未打开，您也可以点击刷新试试。

下载文档到电脑，查找使用更方便

15 积分 4人已下载

立即下载 VIP免费下载

摘要：

i摘要城市交通的飞速发展带来了城市交通拥挤、交通延误、交通事故和交通污染等一系列问题。解决城市交通问题的最直接的办法就是修建更多的道路和桥梁以提高路网的通行能力。但是由于资金和城市空间限制，这一方法往往不是十分可行。智能交通系统（ITS）的发展为解决城市交通问题提供了新思路。智能交通系统包括城市道路交通信息采集、处理、发布、决策，它运用各种先进的技术和科学方法，实现城市交通管理的自动化、现代化和智能化。智能交通系统不仅能提高路网的利用率、交通的安全性，而且关系到土地资源和能源的合理利用、环境污染和噪声的改善，乃至国民经济的持续发展和社会经济效益的全面提高。城市交通信号控制系统作为智能交通系统中...

展开>> 收起<<

面向交通控制的多智能体设计的理论与模型研究.pdf

共129页,预览10页

还剩页未读，继续阅读

面向交通控制的多智能体设计的理论与模型研究

相关推荐

开通VIP享超值会员特权

作者详情

相关内容

推荐作者

热门标签

举报选择: