Fast matching pursuit for traffic images using differential evolution

To obtain the sparse decomposition and flexible representation of traffic images,this paper proposes a fast matching pursuit for traffic images using differential evolution. According to the structural features of traffic images,the introduced algorithm selects the image atoms in a fast and flexible way from an over-complete image dictionary to adaptively match the local structures of traffic images and therefore to implement the sparse decomposition. As compared with the traditional method and a genetic algorithm of matching pursuit by using extensive experiments,the differential evolution achieves much higher quality of traffic images with much less computational time,which indicates the effectiveness of the proposed algorithm.     

智慧主动型交通控制系统及实验

限于交通信息采集内容及方式、控制实现手段和优化模型性能等因素,传统交通控制系统难以满足实际的交通控制需求。大数据和车联网及车路协同系统等技术的发展,为提升交通控制系统的控制基础与条件、系统适应能力和响应能力创造了前所未有的条件。本文首先分析了信息、数据和计算技术等变革对交通系统运行环境、交通控制对象特征和通信与实现手段的影响;进而面向多模式交通流,提出了以更安全、更快速、更绿色、更人性化等为基本特征的下一代智慧主动型交通控制系统及其逻辑架构与物理架构。系统原型测试表明,快速发展的信息通信等技术及基于此的下一代交通控制系统具有出色的潜在性能和广阔的前景。     

Maritime piracy situation modelling with dynamic Bayesian networks

A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a larger DBN. The application of synthetic data fabrication of maritime vessel behaviour is considered. Behaviour of various vessels in a maritime piracy situation is simulated. A means to integrate information from context based external factors that influence behaviour is provided. Simulated observations of the vessels kinematic states are generated. The generated data may be used for the purpose of developing and evaluating counter-piracy methods and algorithms. A novel methodology for evaluating and optimising behavioural models such as the proposed model is presented. The log-likelihood, cross entropy, Bayes factor and the Bhattacharyya distance measures are applied for evaluation. The results demonstrate that the generative model is able to model both spatial and temporal datasets.     

结合Q学习和模糊逻辑的单路口交通信号自学习控制方法*

针对城市交通系统的动态性和不确定性,提出了基于强化学习的信号交叉口智能控制系统结构,对单交叉口动态实时控制进行了研究。将BP神经网络与Q学习算法相结合实现了路口的在线学习。同时,针对交通信号控制的多目标评价特征,采用基于模糊逻辑的Q学习奖惩信号设计方法,实施对交通信号的优化控制。最后,在三种交通场景下,应用Paramics微观交通仿真软件对典型十字路口进行仿真实验。结果表明,该方法对不同交通场景下的突变仍可保持较高的控制效率,控制效果明显优于定时控制。