人工冻结法进行地铁隧道修复施工的数值分析

文章依托京沪铁路某段铁路桥隧道修复工程实例,根据热力数值分析基本理论,利用最小势能原理建立有限元方程,分析了隧道人工冻结施工过程中的温度场分布规律,并确定了开挖时的人工冻结壁厚度。最后,对冻结壁和其周围土体进行施工过程的力学模拟。结果表明,采用人工冻结法作为一种辅助的施工方法,完全能满足施工过程中结构的强度要求。     

TDCS的D/D结合及交叉互控方案

不同制式TDCS的结合有2种方式：D／D结合方式和交叉互控方式。就这2种结合方式的目的、结构、原理、技术要点进行了分析和讨论，并根据在成都铁路局应用的经验，实际比较了其优缺点，旨在为同类系统的实现提供参考。     

铁路地理信息系统(RGIS)元数据问题研究

分析铁路办公数据在存储、使用和维护等方面的特点后,根据元数据的一般概念和作用,提出铁路地理信息系统(RGIS)元数据在RGIS中的主要作用,包括作为RGIS数据目录、描述RGIS数据集内容、数据质量和历史信息以及提升RGIS数据可读性等5个方面。探讨RGIS元数据的具体构成内容,为RGIS中元数据的建设提供了思路。     

船舶动力装置故障诊断专家系统

详细叙述了船舶动力装置故障诊断专家系统的设计过程和特点.将成熟的数据库技术引入到系统中,即以Access2000数据库为平台,构建系统知识库.推理机是以基于可信度的不确定推理模型设计的,系统的运行更加接近专家的思维,也更能反映实际的设备故障环境.采用回溯和启发式搜索策略,可找出故障的多个原因,减少了搜索的路径.系统还能向用户提供维修建议和方法,同时能准确给出故障的位置.     

Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical.     

Urban traffic state estimation: Fusing point and zone based data

Loop detectors are the oldest and widely used traffic data source. On urban arterials, they are mainly installed for signal control. Recently state-of-the art Bluetooth MAC Scanners (BMS) has significantly captured the interest of stakeholders for exploiting it for area-wide traffic monitoring. Loop detectors provide flow – a fundamental traffic parameter; whereas BMS provides individual vehicle travel time between BMS stations. Hence, these two data sources complement each other, and if integrated should increase the accuracy and reliability of the traffic state estimation.This paper proposed a model that integrates loops and BMS data for seamless travel time and density estimation for urban signalised network. The proposed model is validated using both real and simulated data and the results indicate that the accuracy of the proposed model is over 90%.     

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

The Macroscopic Fundamental Diagram (MFD) has been recognized as a powerful framework to develop network-wide control strategies. Recently, the concept has been extended to the three-dimensional MFD, used to investigate traffic dynamics of multi-modal urban cities, where different transport modes compete for, and share the limited road infrastructure. In most cases, the macroscopic traffic variables are estimated using either loop detector data (LDD) or floating car data (FCD). Taking into account that none of these data sources might be available, in this study we propose novel estimation methods for the space-mean speed of cars based on: (i) the automatic vehicle location (AVL) data of public transport where no FCD is available; and (ii) the fused FCD and AVL data sources where both are available, but FCD is not complete. Both methods account for the network configuration layout and the configuration of the public transport system. The first method allows one to derive either uni-modal or bi-modal macroscopic fundamental relationships, even in the extreme cases where no LDD nor FCD exist. The second method does not require a priori knowledge about FCD penetration rates and can significantly improve the estimation accuracy of the macroscopic fundamental relationships. Using empirical data from the city of Zurich, we demonstrate the applicability and validate the accuracy of the proposed methods in real-life traffic scenarios, providing a cross-comparison with the existing estimation methods. Such empirical comparison is, to the best of our knowledge, the first of its kind. The findings show that the proposed AVL-based estimation method can provide a good approximation of the average speed of cars at the network level. On the other hand, by fusing the FCD and AVL data, especially in case of sparse FCD, it is possible to obtain a more representative outcome regarding the performance of multi-modal traffic.     

Recent applications of big data analytics in railway transportation systems: A survey

Big data analytics (BDA) has increasingly attracted a strong attention of analysts, researchers and practitioners in railway transportation and engineering. This urges the necessity for a review of recent research development in this field. This survey aims to provide a comprehensive review of the recent applications of big data in the context of railway engineering and transportation by a novel taxonomy framework, proposed by Mayring (2003). The survey covers three areas of railway transportation where BDA has been applied, namely operations, maintenance and safety. Also, the level of big data analytics, types of big data models and a variety of big data techniques have been reviewed and summarized. The results of this study identify the existing research gaps and thereby directions of future research in BDA in railway transportation systems.     

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.