先进出行信息下事故对行程时间可靠性的影响

定义路径行程时间可靠性为在交通事故期间内平均路径行驶时间小于事故前路径出行时间乘以可接受拥堵水平的概率,由此导出路网行程时间可靠性.假定事故持续时间服从正态分布并将研究时域划分成相同的时段,在先进出行信息下,利用元胞传输模型进行路段流量加载,给出了每一个时段内路径行程时间的递推式,并在每一个时段内更新1次路径出行时间,出行者根据更新的出行时间运用Logit模型进行路径决策,最后基于Monte-Carlo法模拟求解路网行程时间可靠性.算例结果表明,行程时间可靠性随事故持续时间和方差及需求的增加而减小;可靠性随可接受拥堵水平的增加而增加;在拥堵网络中,包含事故路段的OD间需求越高,可靠性越低.     

部队铁路输送路线优选模型

紧贴部队实际,结合现代铁路运输条件,分析部队铁路输送的有关要求,在此基础上建立部队铁路输送路线优选模型,从而实现部队铁路输送路线的优选,为拟制铁路输送计划提供决策依据。     

LNG运输船BOG处理技术发展与演化路径研究

LNG运输船具有良好的发展前景,BOG的妥善处理是LNG燃料运输船安全航行的重要环节。通过专利数据挖掘与数据分析技术,归纳全球LNG运输船BOG处理领域的焦点技术和前沿技术,并且结合专利文本信息梳理出BOG处理技术的演化路径。研究结果发现,BOG处理领域内专利申请集中在回收利用方式、再液化装置及再液化处理方式三个方面。企业在今后发展中应针对涡轮布雷顿制冷技术进行重点研发和专利布局。     

燃气轮机发电机组并联优化设计

本文分析了某船的燃气轮机发电机组的调压和调频调载原理,以及在双机并联运行时的无功分配和有功分配原理,针对两舷电站的发电机组不能长时间并联运行的局限性,提出发电机并联优化设计方案：通过增加一套均压/均功连接控制器以及修改少量接线,而无需修改原系统的软件,使任意并联发电机组之间均可进行有效的无功功率和有功功率平均分配,从而能够长时间并联运行,提高供电生命力。     

基于Q-learning 的定制公交跨区域路径规划研究

考虑城市大客流通勤者跨区域出行需求,结合城市公交线网中乘客出行密集、客流走向规律等特点,提出一种跨区域定制公交的搭乘方案. 通过改进的Q-learning 模型对公交线路进行优化,为城市通勤者提供更加便捷和高效的出行服务. 通过综合路段拥堵状态、乘客需求及居民小区位置,设定了Q-learning 强化学习的奖惩函数,提升定制公交区域路径的直线系数、满载率、通行时间. 结果表明,所提出的改进方法能够降低通勤者跨区域通行的旅行时间,有效提高髙峰时段定制公交线网的通行效率.     

逐日路径演化中出行者信息偏好的实验分析

在逐日出行过程中,出行者对各类信息的偏好是一个动态变化的过程,不同出行者对同类信息的偏好也不同.本研究搭建不同信息条件下逐日路径选择实验场景,用信息偏好系数表征不同类别信息对出行者感知时间影响的相对权重,研究出行者信息偏好的演化规律与分布特征.实验结果表明,出行个体信息偏好的演化存在3种类型,出行群体对感知时间的信息偏好系数先震荡增加后逐步稳定,出行个体对感知时间的信息偏好系数在出行群体中成正态分布.与只提供完全历史信息相比,同时提供基于指数平滑法的系统预测信息会降低出行者对感知时间的偏好.     

武汉市过长江交通流量特征分析

武汉市过长江交通是武汉市最主要的交通流向之一。根据流量观测数据分析长江交通流量特征,表明武汉市区过长江交通饱和,交通需求总量大,出行目的种类多。过长江交通没有明显、固定的高峰日和高峰小时,除通勤交通外。还有公务等大量非通勤交通。其中武昌与汉口之间交通是主流向,需要适当新增通道缓解过江难问题。     

基于动态网格模型的航线自动生成算法

为了充分利用电子海图信息,进行航线设计,提出了基于动态网格模型的航线自动生成算法。采用一个判断网格模型中方格可航性的快速算法,对方格的可航性进行分类;给出了一个有效地使用8个字节表示邻接方格8个方向的编码方法,使邻接方格的连通性与字节的逻辑操作相对应;提出了网格路径权序列均匀度概念,给出了距离等长路径的优选方法,分析了路径与所对应的实际航路长度的差异;基于二分查找法,求出航线上所有转向点的路径方格序列。研究结果表明:基于动态网格模型所生成的航线可避开危险的障碍物,是合理、安全的。     

Energy-aware control for automated container terminals using integrated flow shop scheduling and optimal control

The performance of container terminals needs to be improved to handle the growth of transported containers and maintain port sustainability. This paper provides a methodology for improving the handling capacity of an automated container terminal in an energy-efficient way. The behavior of a container terminal is considered as consisting of a higher level and a lower level represented by discrete-event dynamics and continuous-time dynamics, respectively. These dynamics represent the behavior of a large number of terminal equipment. The dynamics need to be controlled. For controlling the higher level dynamics, a minimal makespan problem is solved. For this, the minimal time required by equipment for performing an operation at the lower level is needed. The minimal time for performing an operation at the lower level is obtained using Pontryagin’s Minimum Principle. The actual operation time allowed by the higher level for processing an operation at the lower level is subsequently determined by a scheduling algorithm at the higher level. Given an actual operation time, the lower level dynamics are controlled using optimal control to achieve minimal energy consumption while respecting the time constraint. Simulation studies illustrate how energy-efficient management of equipment for the minimal makespan could be obtained using the proposed methodology.     

Behavior-consistent information-based network traffic control for evacuation operations

The evacuation operations problem aims to avoid or mitigate the potential loss of life in a region threatened or affected by a disaster. It is shaped to a large extent by the evolution of evacuation traffic resulting from the demand–supply interactions of the associated transportation network. Information-based control is a strategic tool for evacuation traffic operations as it can enable greater access to the affected population and more effective response. However, comparatively few studies have focused on the implementation of information-based control in evacuation operations. This study develops a control module for evacuation operations centered on addressing the demand–supply interactions by using behavior-consistent information strategies. These strategies incorporate the likely responses of evacuees to the information provided in the determination of route guidance information. The control module works as an iterative computational process involving an evacuee route choice model and a control model of information strategies to determine the route guidance information to direct evacuation traffic so as to approach a desired network traffic flow pattern. The problem is formulated as a fuzzy logic based optimization framework to explicitly incorporate practical concerns related to information dissemination characteristics and social equity in evacuation operations. Numerical experiments highlight the importance of accounting for the demand–supply interactions, as the use of behavior-consistent information strategies can lead evacuee route choices to approach the operator-desired proportions corresponding to the desired traffic pattern. The results also indicate that while a behavior-consistent information strategy can be effective, gaps with the desired route proportions can exist due to the discrete nature of the linguistic messages and the real-world difficulty in accurately modeling evacuees’ actual route choice behavior.