基于粗集和神经网络耦合的短时交通流预测

比较分析神经网络和粗糙集在数据处理过程中的各自优缺点,提出一种基于二者强耦合集成方式的短时交通流预测模型。首先利用粗集对获取的交通流数据进行预处理,简化神经网络训练样本数据集并通过粗集属性约简提取决策规则;其次,利用所提取的规则直接确定神经网络的隐层数、隐层节点数及节点的相互关系;最后训练神经网络用于短时交通流预测。通过与单纯利用神经网络预测的结果进行比较,发现该模型降低了网络训练时间,提高了预测精度。     

港口照明智能控制系统设计

开发港口照明系统的智能调光系统,并通过网络对港口照明系统进行集中网络控制与管理可实现港口照明综合节能。文中对港口照明智能控制系统进行分析,构建了无线传感网络与局域网组合的港口照明智能控制系统构架,同时解决了港口照明系统关键技术问题：港口照明控制单元的主控电路、检测电路以及通信电路等,结合港口GIS技术,设计了港口照明智能控制软件的架构,并对系统的功能进行分解与描述。     

基于改进均值模型的汽油机瞬态空燃比控制研究

结合平均值模型(MVEM)和BP网络的优点,对平均值模型进行改进,将BP网络替代均值模型中经典X-τ油膜模型,对喷油脉宽进行辨识。将改进后的均值模型和原均值模型在SIMULNK中建模,研究结果表明,采用改进后的均值模型,发动机瞬态空燃比最大偏离值和偏离时间明显得到改善,具有精度高、跟踪快速的特点,为基于模型的发动机瞬态工况空燃比的控制提供参考。     

基于首次超越机制的小规模路网可靠性模型研究

可靠性可用于评价路网性能。传统的路网可靠性指标基于路段可靠性推导而得,因数据获取难度大等诸多缺点导致难以实践应用。分析了传统路网可靠性指标的不足,基于路网行程时间,通过构造合理的单输入单输出路网结构模型,获得表征路网结构的功能函数,并得到一般路网的可靠性模型。在此基础上,基于首次超越机制,在泊松假设的前提下构造了评价路网可靠性的模型。基于Vissim 仿真平台介绍了模型的应用过程,仿真结果与路网运行状态相符。     

城市中心区交通拥堵定价模型及其仿真算法

为改善城市中心区的交通拥堵,以国内城市中心区区域路网为研究对象,以路网车辆整体延误时间最短为优化目标,拥堵收费价格为决策变量,路网饱和度和信号配时为约束条件,建立了拥堵定价优化模型,并给出了基于VISSIM软件的仿真求解方法和步骤.实例仿真结果表明:在城市中心区区域路网采用该定价模型可以找到最优定价及合理的信号配时方案;计算机仿真方法可大幅度降低模型求解难度,并可直观地为交通管理部门制订拥堵定价方案提供理论依据.     

Bayesian network for red-light-running prediction at signalized intersections

Abstract

Red-light-running (RLR) is an important reason for the large number of intersection-related fatalities, injuries, and other losses. The accurate RLR prediction can effectively reduce crashes caused by RLR behavior. The RLR prediction is usually composed of two parts: the vehicle’s stop-or-go behavior and the arrival time when the vehicle reaches the stop line. Previous stop-or-go prediction models are usually based on embedded traffic sensors using machine learning algorithms. While based on the continuous trajectories collected by radar sensors, RLR prediction can be conducted more effectively. In this paper, a probabilistic stop-or-go prediction model based on the Bayesian network (BN) is proposed for RLR prediction. We extend the deterministic output into the probabilistic output, which provides decision-makers with greater autonomy. The causality of BN improves the interpretability of the prediction model. The BN model is calibrated and tested by the continuous trajectories data measured by radar sensors installed at a signalized intersection. We not only consider the movement measurements of individual vehicles (e.g., speed and acceleration), but also take into account the car-following behavior. As a comparison, different machine learning models and the model based on the inductive loop detection (ILD) are adopted. The results show that the proposed BN model has a high prediction accuracy and performs better in the feature interpretation. This paper provides a new way for probabilistic RLR prediction based on continuous trajectories, which will significantly improve traffic safety of signalized intersections.     

Asynchronous n-step Q-learning adaptive traffic signal control

Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.     

基于RFID电子车牌数据的公交行程时间预测方法

为了给公交优先信号配时系统提供足够的"思考"时间和准确的控制依据,基于重庆市RFID电子车牌数据提出了一种采用自适应渐消卡尔曼滤波和小波神经网络组合模型动态预测公交行程时间的方法。综合分析公交行程时间的动态和静态影响因素,选取的模型输入参量为标准车流量、路段车辆平均行程时间、平均车速离散性和前班次公交行程时间。利用RFID电子车牌系统采集重庆市鹅公岩大桥路段车辆行驶数据,选取3 000组实际运行数据完成公交行程时间预测模型的训练,另筛选50组数据验证模型的有效性和准确性。研究结果表明：组合模型可动态自适应预测公交行程时间,预测值平均相对误差为3.23%,绝对误差集中在8 s左右,明显优于2种单一模型和基于传统GPS数据的公交行程时间预测模型,可认为选择RFID电子车牌数据作为组合模型的输入,能够明显改善模型预测精度;组合模型预测值的残差分布更为集中、鲁棒性较好,泛化能力强。选择平均绝对误差值、均方根误差值和平均绝对百分比误差作为模型评价指标,结果进一步表明,组合模型的综合预测效果明显优于单一的自适应渐消卡尔曼滤波和小波神经网络。研究方案可为先进公交信息化系统提供良好的技术支撑。     

西安市大寨路道路工程建设及管理

针对城市道路路网功能完善、交通功能定位、交叉节点处理方案分析论证、设计重点及难点等方面问题进行探究,较为全面系统地总结了创新设计理念、提升建设管理水平等方面的技术处理措施,为今后同类道路网建设及解决问题的方法和思路提供依据。     

基于大规模浮动车数据的城市道路网复杂度分析

针对现有浮动车技术应用研究中缺乏道路网络复杂度的定量分析,引入置信点的概念,提出了一种基于大规模浮动车数据的城市路网复杂度分析方法.利用该方法统计了路网中各路段的置信点分布情况和置信点的平均匹配距离,建立了城市路网复杂度模型.结合浮动车数据的预处理,提出了城市路网复杂度的分析流程.以广州市为例,分析了广州市全局路网和4类典型城市道路代表路段的复杂度.对于地图匹配算法实证研究中的指定路径,具体分析了其各组成路段的复杂度及其主要影响因素.