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城市交通信号多目标自适应控制

Urban Traffic Signal Multi-Object Adaptive Control

作者: 专业:计算机应用技术 导师:谭国真 年度:2010  院校: 大连理工大学

Keywords

        随着城市化进程的加快,机动车保有量快速提升,城市交通拥挤、阻塞日趋恶化,导致了巨大的环境污染、经济浪费、能源消耗,交通问题已经成为困扰世界各国的难题。为了解决交通问题,应在扩建城市道路基础设施的同时,发展先进有效的交通信号控制系统,充分发挥现有交通网络的通信能力。目前世界上使用最广泛的传统的城市交通信号控制系统有SCATS、SCOOT,由美国亚利桑那大学研发的RHODES则是新一代自适应控制系统的典范。本文通过对这些典型系统的研究,发现目前交通控制系统存在的不足:一、大多控制系统并非自适应控制,不能主动控制交通,都是被动、响应式控制;二、缺乏多目标自适应控制方法,如缺乏针对特殊地理结构路口的通用且有效的控制方法,缺乏对多个交通行为参与者利益相互协调的有效控制。本文首先从控制原理上,对传统和新一代控制系统进行深入研究,对比了SCOOT、SCATS、RHODES系统控制原理,并对交通控制方法进行了探讨,阐明自适应控制需要具备的两大特点;然后,深入分析交通控制中的多目标问题,分析特定地理结构的交通道路的多目标控制问题,以及不同交通行为参与者的多目标控制问题;进而提出了多目标自适应相位优化控制方法,对单个路口的不同优化指标(延误时间、排队长度)进行建模,应用自适应控制模型,采用多目标粒子群智能算法求解,给出能满足单个路口多个目标优化需求的优化相序。最后,本文以支路较短的单路口控制以及考虑对社会车辆影响的公交优先为例,应用多目标自适应相位优化控制方法,在VISSIM仿真平台上验证了该方法的有效性。
    With the rapid development of cities and the prevalence of cars, the situation of urban traffic congestion is getting worse, which leads to massive pollution, economic waste and energy consumption. Traffic has become a general difficult problem which puzzles the whole world. In order to solve the traffic problem, we should not only rely on the building of urban road and basic infrastructure, but also develop advanced and effective traffic signal control system so as to fully release the vast potential of the current transportation network.While SCATS and SCOOT are the world’s most widely used among the traditional urban traffic signal control systems, RHODES developed by the University of Arizona is a typical example of the new generation of adaptive control system. Based on the study of the typical systems all mentioned above, we found two deficiencies of the current traffic control systems: firstly, most of the control systems are not adaptive but reactive and responsive instead; secondly, most of the control systems lack the multi-objective adaptive control method targeting at the junction with special geographical structure and coordinating the interest between different traffic participants.This article first made an in-depth study of the traditional and new-generation control systems from the perspective of control principle by comparing the control principle of SCOOT, SCATS and RHODES. Then the methods of traffic control were discussed and the two major characteristics of adaptive control were clarified. What followed was the analysis of the multi-objective problem in traffic signal control, such as the control of intersection with particular structure and the control of different traffic behavior participants. Hence we proposed the multi-objective adaptive control method, built the delay and queue model of the intersection and finally got the optimal phase sequence for the a single junction using multi-objective particle swarm intelligence algorithm.At the end, via the multi-object adaptive phase optimization control method successfully solving the single intersection with particular geography structure problem and the bus signal priority considered the impact to private cars, the simulation experiments on the VISSIM simulation platform verified the effectiveness of the method.
        

城市交通信号多目标自适应控制

摘要4-5
Abstract5
1 绪论8-14
    1.1 研究背景8-9
    1.2 研究意义与目的9-10
    1.3 国内外研究现状10-12
    1.4 研究思路与结构框架12-14
2 城市交通信号控制原理14-24
    2.1 交通信号控制基本概念14-15
        2.1.1 交通信号控制的范围14
        2.1.2 交通信号控制的原理14-15
    2.2 交通信号控制优化问题建模理论15-19
        2.2.1 离线优化数学模型15-17
        2.2.2 动态模型17-19
    2.3 主流城市交通信号控制系统介绍19-21
    2.4 城市交通信号自适应控制21-23
        2.4.1 自适应控制21
        2.4.2 城市交通控制系统控制原理分析21-23
    2.5 本章小结23-24
3 城市交通多目标优化控制24-32
    3.1 多目标优化问题24-25
        3.1.1 多目标优化问题的数学描述24
        3.1.2 多目标优化问题的求解方法24-25
        3.1.3 进化多目标算法25
    3.2 道路路口交通质量评价指标体系25-28
        3.2.1 交通控制系统评价体系25-26
        3.2.2 评价指标的选取与获得方法26-28
    3.3 交通控制中的多目标问题28-29
        3.3.1 特殊地理结构的路口控制问题28-29
        3.3.2 考虑社会车辆影响的公交优先问题29
    3.4 多目标自适应控制模型29-31
    3.5 本章小结31-32
4 多目标自适应控制建模与求解32-47
    4.1 模型建立32-38
        4.1.1 相位优化组合32
        4.1.2 固定相序模型与可变相序模型32-33
        4.1.3 基于实时预测的排队模型与车辆延误模型33-35
        4.1.4 评价指标模型35-38
    4.2 多目标粒子群优化算法38-41
        4.2.1 基本的粒子群优化算法38-40
        4.2.2 基于拥挤距离的多目标粒子群优化算法40-41
    4.3 基于多目标粒子群的相序优化求解方法41-46
        4.3.1 直观的解表示方法41-42
        4.3.2 基于划分的解表示方法42-44
        4.3.3 解空间合并与选解44-46
    4.4 本章小结46-47
5 应用实例与仿真实验47-57
    5.1 微观交通仿真平台VISSIM47
    5.2 特殊地理结构路口的多目标自适应控制47-53
        5.2.1 问题描述48-49
        5.2.2 实验结果分析49-53
    5.3 考虑社会车辆影响的公交优先53-56
        5.3.1 问题描述53-54
        5.3.2 实验结果分析54-56
    5.4 本章小结56-57
结论57-59
参考文献59-63
攻读硕士学位期间发表学术论文情况63-64
致谢64-66
        下载全文需66


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