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Improvements of Traffic Flow Simulation Models Using Some Artificial Intelligence Techniques

利用一些人工智能技术改进交通流仿真模型

基本信息

批准号：
06650579
负责人：
NAKATSUJI Takashi
金额：
$ 1.22万
依托单位：
Hokkaido University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

This project aims to improve traffic flow simulation models for freeways and arterials with the aid of someartificial intelligent techniques. Itis diviede into three parts :1)Description of Macroscopic Relationships Among Traffic Flow Variables Using Neural Network Model.The relationships among traffic flow variables play important roles in traffic flow simulation models. A procedure was presented to describe the macroscopic relationships between traffic flow variables using some neuralnetwork models. First, a Kohonen Feature Map model was introduced to convert original observed data points into fewer, more uniformly distributed ones. This conversion improved regression precision and computational efficiency . Next, a multilayr neural network model was introduced to describe the two-andthree-dimensional relationships. The model was effective in describing the non-linear and discontinuous characteristics between traffic flow variables.2)A Neural-Kalman Filtering Method for Estimating Tr … More affic StatesBy integrating multilayr neural network models into a Kalman filtering technique, a procedure for estimating traffic ststes was proposed . That is, The Cremer model, which is a macroscopic traffic flow model combined with a Kalman filter, is revised using a neural network model. The observation equations that relate the state variables, such as density and space mean speed, to the observation variables, such as flow rate and time mean speed, were described accurately using a neural network model. The derivatives of both state and observation equations were easily obtained, too. This neural-kalman method was applied to a road section on the Metropolitan Expressway in Tokyo and it was examined how precisely the method could work as compared with the original Cremer model.3)Artificial Intelligence Approach for Optimizing Traffic Signal Timing on Urban Road NetworkUsing artificial intelligence techniques, a stepwise method was developed to optimize signal timing parameters, such as splits and offsets, on an urban street. The method is separated into two processes, a training process and an optimization process. In the training process, we used two neural network models, a multilayr model and Kohonen Feature Map model. The former modelbuilds an input-output relationship between the signal timing parameters and the objective variable. The latter model improves the computational efficiency and the estimation precision. In the optimization process, to avoid the entrapment into a local minimum, two artificial intelligence methods were used ; the Cauchy machine and a genetic algorithm . The timing parameters were adjusted so as to minimize the total weighted sum of delay time and stop frequencies . The solutions by both artificialintelligence methods were compared with those by a conventional method and confirmed that they were useful for establishing advanced traffic control systems in the future . Less

本项目旨在借助一些人工智能技术，改进高速公路和主干道的交通流仿真模型。本文分为三个部分：1)用神经网络模型描述交通流变量间的宏观关系。交通流变量之间的关系在交通流仿真模型中起着重要的作用。提出了一种用神经网络模型描述交通流变量间宏观关系的方法。首先，引入Kohonen Feature Map模型，将原始观测数据点转换为更少、更均匀分布的数据点；这种转换提高了回归精度和计算效率。其次，引入多层神经网络模型来描述二维和三维关系。该模型能有效地描述交通流变量间的非线性和不连续特征。将多层神经网络模型与卡尔曼滤波技术相结合，提出了一种交通状态估计方法。即利用神经网络模型对结合卡尔曼滤波的宏观交通流模型The Cremer模型进行修正。利用神经网络模型准确地描述了密度、空间平均速度等状态变量与流量、时间平均速度等观测变量之间的关系。状态方程和观测方程的导数也很容易得到。将这种神经卡尔曼方法应用于东京大都会高速公路的一个路段，并与原始的Cremer模型相比，检验了该方法的工作精度。利用人工智能技术，提出了一种逐步优化城市道路交通信号配时参数的方法。该方法分为两个过程，一个是训练过程，一个是优化过程。在训练过程中，我们使用了两种神经网络模型，多层模型和Kohonen Feature Map模型。前一种模型在信号时序参数与目标变量之间建立了输入输出关系。后一种模型提高了计算效率和估计精度。在优化过程中，为了避免陷入局部最小值，采用了两种人工智能方法；柯西机器和遗传算法。调整定时参数，使延时时间和停止频率的加权总和最小。将这两种人工智能方法的解决方案与传统方法的解决方案进行了比较，证实了它们对未来建立先进的交通控制系统是有用的。少