FXD3-J型动力集中动车组动力车网络控制系统

文章详细介绍了FXD3-J型动力集中动车组动力车网络控制系统的结构与组成,对网络设备、系统控制功能、显示界面、冗余特性等进行了阐述。运用考核试验表明,该网络控制系统性能稳定、可靠。     

基于可靠性方框图与蒙特卡洛仿真的船舶配电网可靠性分析方法

国船舶工业发展迅速,尤其在近几年迎来了跨越式的发展。船舶建设工业的发展能充分体现一个国家的经济和国防建设水平。但船舶配电网拓扑结构与陆上电网不尽相同,其运行环境非常恶劣,供电连续性和可靠性成为衡量船舶生命力的重要指标,同时也深刻影响大型船舶的平均寿命。因此,可靠性研究在船舶建设中发挥着越来越重要的作用。本研究以典型大型船舶中压配电网结构为研究对象,在全系统元件正常运行时相互独立的情况下,重点研究不可维修与可维修下的系统可靠度、MTBF、可用度、停运时间和失效数等,并进行详细的分析与讨论。首先,本文综合考察各种可靠性分析方法的适用性和准确性,选择利用可靠性框图、最小路集分析法计算并分析不可维修情况下系统的可靠度、平均故障间隔时间等可靠性指标；接着,本文简述了配电网的多种维修任务类型,并采用最小路集法与蒙特卡洛仿真法相结合的方法计算并分析可维修系统的可用度、停电时间、失效数等指标。本文研究结果对船舶配电网的设计、建造、运行和维修等具有积极的意义。     

地铁临时支援供电中压环网方案研究

因征地拆迁等原因,地铁主变电所及电缆通道常因工期滞后而需临时支援供电。针对临时支援供电问题研究其中压环网方案,提出了系统运行方式及可靠性、压降要求、倒接可行性和经济性4方面评价标准。以武汉地铁2号线南延线为例,提出3种临时支援供电方案。分别阐述了每种方案的运行方式,计算了环网压降,研究倒接方案的可行性和经济性。通过综合比较,总结出各方案的优缺点,提出注意事项,为后续工程提供思路。     

基于多维网络的增开列车条件下高速铁路列车运行图调整

研究增开列车条件下高速铁路列车运行图的调整问题,并综合考虑列车车站进路的影响。通过构建Time-Station-Track三维时空扩展网络,刻画列车对铁路时空资源的占用。将原多目标优化问题转化为求解列车占用网络弧段最小费用单目标问题,并构建基于Time-Station-Track网络的0-1整数规划模型。针对模型特点,设计拉格朗日松弛算法,将问题进一步分解为求解单列车网络最短路径子问题,由于问题被松弛后求得的解可能不可行。因此,提出基于列车优先序列的启发式策略对对偶解进行可行化。最后以宝兰客运专线为例,验证模型的正确性和算法的可行性。     

基于时延特性建模的多断面短时交通流预测

针对现有交通流预测方法未充分考虑多断面车流演变规律，提出基于时延特性建模的时空相关性计算方法. 该方法采用对不同断面、不同时刻交通流的分布相似性度量，对输入的车辆到达数据序列进行切割构建时空相似度矩阵，得到相邻断面之间的时延参数. 基于时延特性建模，将多断面之间的流量信息进行融合，使用长短时记忆(LSTM)网络进行流量预测. 通过对实际路段数据的预测和结果分析，验证所提方法的有效性和实用性.     

Key criteria influencing the choice of Arctic shipping: a fuzzy analytic hierarchy process model

As Arctic sea ice shrinks due to global warming, the Northern Sea Route (NSR) and the Northwest Passage (NWP) offer a substantial reduction in shipping distance between Asia and the European and North American continents, respectively, when compared to conventional routes through the Suez and Panama Canals. However, Arctic shipping routes have many problems associated with their use. The main objective of this paper is to identify the key criteria that influence the decisions of shipping operators with respect to using Arctic shipping routes. A multi-criteria decision-making methodology, the Fuzzy Analytic Hierarchy Process, is applied to rank four potential categories of criteria (‘economic’, ‘technical’, ‘political’ and ‘safety’ factors) and their sub-criteria.

The results of the analysis suggest that, on aggregate, ‘economic’ is the most important category of influential factors, followed by ‘safety’, ‘technical’ and ‘political’ factors. The paper concludes, however, that the most influential specific sub-criteria relate to risks that lie mainly within the ‘safety’ and ‘political’ domains and that, especially in combination, these overwhelm the importance which is attached to ‘economic’ factors such as reduced fuel use. Finally, the implications of these findings for the future development of Arctic shipping are addressed at a strategic level.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

An efficient and exact event-based algorithm for solving simplified first order dynamic network loading problems in continuous time

In this paper a novel solution algorithm is proposed for exactly solving simplified first order dynamic network loading (DNL) problems for any generalised network. This DNL solution algorithm, termed eLTM (event-based Link Transmission Model), is based on the seminal Lighthill–Witham–Richards (LWR) model, adopts a triangular fundamental diagram and includes a generalised first order node model formulation. Unlike virtually all DNL solution algorithms, eLTM does not rely on time discretisation, but instead adopts an event based approach. The main advantage of this approach is the possibility of yielding exact results. Furthermore, an approximate version of the same algorithm is introduced. The user can configure an a-priori threshold that dictates the approximation error (measurable a-posteriori). Using this approximation the computational effort required decreases significantly, making it especially suitable for large scale applications. The computational complexity is investigated and results are demonstrated via theoretical and real world case studies. Fixed periods of stationary demands are included adopting a matrix demand profile to mimic basic departure time demand fluctuations. Finally, the information loss of the approximate solution is assessed under different configurations.     

Indicators of reliability and vulnerability: Similarities and differences in ranking links of a complex road system

This article investigates two performance attributes of road networks, reliability and vulnerability, analyzing their similarities as well as the differences that justify distinct definitions, based on consolidation of recent studies. We also discuss the indicators found in the literature for these two performance attributes. Since various authors treat vulnerability as an aspect of reliability instead of a specific attribute, we carried out an application to a complex road network representative of the city of Rio de Janeiro to check the suitability of this approach. The results show that the vulnerability indicators are more strongly affected by the characteristics of alternative routes while the reliability metrics are more sensitive to the congestion level. The conclusion is that reliability and vulnerability should be treated distinctly for evaluating the performance of road network links.