Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Travel time reliability, an essential factor in traveler route and departure time decisions, serves as an important quality of service measure for dynamic transportation systems. This article investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. A volume-to-capacity ratio-based travel time function and a point queue model are used to demonstrate how day-to-day travel time variability can be explained from the underlying demand and capacity variations. One important finding is that closed-form solutions can be derived to formulate travel time variations as a function of random demand/capacity distributions, but there are certain cases in which a closed-form expression does not exist and numerical approximation methods are required. This article also uses probabilistic capacity reduction information to estimate time-dependent travel time variability distributions under conditions of non-recurring traffic congestion. The proposed models provide theoretically rigorous and practically useful tools for understanding the causes of travel time unreliability and evaluating the system-wide benefit of reducing demand and capacity variability.     

Approximate optimal AUTOSAR software components deploying approach for automotive E/E system

The AUTOSAR has been developed as the worldwide standard for automotive E/E software systems, making the electronic components of different suppliers to be employed universally. However, as the number of component-based applications in modern automotive embedded systems grows rapidly and the hardware topology becomes increasingly complex, deploying such large number of components in automotive distributed system in manual way is over-dependent on experience of engineers which in turn is time consuming. Furthermore, the resource limitation and scheduling analysis make the problems more complex for developers to find out an approximate optimal deploying approach in system integration. In this paper, we propose a novel method to deploy the AUTOSAR components onto ECUs with the following features. First, a clustering algorithm is designed for deploying components automatically within relatively low time complexity. Second, a fitness function is designed to balance the ECUs load. The goal of our approach is to minimize the communication cost over all the runnable entities while meeting all corresponding timing constraints and balancing all the ECUs load. The experiment results show that our approach is efficient and has well performance by comparing with other existing methods in specific and synthetic data set.     

潮位变化对上海某越江盾构隧道变形的影响规律研究

为了探究潮位变化与越江盾构隧道的沉降和收敛变形之间的关联性,采用数据统计分析、相关性分析和周期性分析方法,对上海某越江盾构隧道的监测数据与同时期隧道上部的潮位变化进行研究分析。研究表明:1)江中段隧道在潮位变化的作用下产生均匀沉降,其波动性与潮位起伏具有近似的周期性;2)陆域段隧道沉降变化的波动性程度与河流的距离有关;3)潮位变化引起隧道管片断面的横径和竖径产生循环的收敛变形,其横径收敛变形大于竖径收敛变形。以上研究可为越江盾构隧道设计、施工和运维提供参考。     

柴油机气门导管断裂影响因素分析及改进

针对某柴油机气门导管在整机台架试验过程中发生的横断和纵裂失效故障,对气门导管的材料性能、结构应力、微动磨损、热变形、装配工艺等影响因素进行了分析。分析结果表明:材料力学性能差和装配过盈量较大造成的应力集中和微动磨损是气门导管横断的主要原因;液氮人工压装的装配工艺是造成气门导管纵裂的主要原因。据此提出新方案气门导管,并通过了整机台架试验验证。     

郑万高铁隧道大型机械化配套全断面法施工围岩-支护结构相互作用力学特性研究

为研究郑万高铁大型机械化配套全断面法施工围岩-支护结构相互作用力学特性,依托郑万高铁隧道现场实测数据,确定台阶法和大型机械化配套全断面法2种工法施工的围岩压力分布特征,并采用数值模拟的方法建立围岩-支护结构有限元模型,分析2种工法对围岩压力和结构内力的影响规律。研究结果表明： 1)台阶法多次施工扰动易导致支护结构和围岩之间产生空隙而发生脱空,大型机械化配套全断面法施工对围岩扰动次数少,初期支护快速封闭成环,可保证初期支护及围岩之间良好的密贴性; 2)在软弱围岩中,采用大型机械化配套全断面法施工比台阶法更有利于改善结构受力,减缓结构内部偏心受压的程度,能有效发挥拱形隧道结构承载力; 3)大型机械化配套全断面法在软弱围岩快速施工、控制支护结构变形、保证支护结构受力安全等方面具有综合优势。     

蒙华铁路阳城隧道土砂分界地层分界面位置对围岩稳定性影响探究

为了解决隧道穿越土砂分界地层的围岩稳定性问题,以蒙华铁路阳城隧道为研究对象,采用现场试验、室内试验及数值试验等研究方法对土砂分界地层围岩稳定性进行探究,重点探讨地层种类及其厚度变化对围岩变形、塑性区扩展及支护结构受力的影响。结果表明： 1）相对单一地层,土砂分界地层会影响洞周应力分布,分界面处围岩容易先出现塑性区,且土砂分界地层围岩变形性质与软弱的全砂岩地层更为接近; 2）在土砂分界地层分界面处会产生较大的水平收敛,易出现拉应力; 3）土砂分界地层中,随着砂层所占比例的增大,拱顶沉降和洞周收敛不断增大,且分界面所在台阶处支护结构的应力发展会提前达到更高的峰值; 4）当分界面出现在上台阶时,围岩会产生极大的塑性变形,支护结构承受相对较大的应力,是土砂分界地层中最不利的分界情形。     

柴油机SCR系统控制策略研究与软件设计

为有效地降低柴油机排气污染物NO_x,结合模块化设计思想,在Matlab/Simulink环境下完成SCR控制软件的开发。软件主要包括数据管理模块、SCR状态模块、尿素量计算模块和任务模块,其中尿素量计算模块采用基本尿素喷射量计算、尿素量修正和闭环反馈调节的组合方式提高尿素量计算精度。软件经代码生成后刷写至SCR控制器DCU中,在试验台架上完成ESC和WHTC循环测试。试验结果表明:在ESC和WHTC试验循环下,NO_x转换效率分别高于79.09%和72.35%,经催化还原后排放值分别低于1.836g/(kW·h)和2.147g/(kW·h),满足国Ⅴ法规的限值要求。     

Bolster spring fault detection strategy for heavy haul wagons

An on-board health monitoring system is proposed for heavy haul wagons in this paper including a signal-based fault detection and isolation (FDI) method and an on-line fault diagnose strategy. Such a system, to be feasible on freight wagons, must be sufficiently cheap and robust, hence the design assumes the constraint of using only two accelerometers mounted on the front left and right rear part of each carbody in a heavy haul train. This paper focuses on the detection of bolster spring fault conditions. The problem is made more complex by the modes of failure which might be expected in bolster spring nests. Types of spring failure are firstly identified and discussed covering situations of broken (shortening springs) and softening (individual spring loss from a nest or cross-section loss through corrosion). The effects of these faults and their detectability were investigated using simulations on straight and curved track and using a fully detailed model of a typical 40?t axle-load heavy haul wagon. The simulation results were then examined and compared using cross-correlation analysis and an FDI system was proposed. The FDI system introduced five possible fault indicators. Initial results indicated that it was possible to detect changes in bolster stiffness of ±25%. An on-line fault diagnoses strategy is proposed for bolster spring faults which only requires data from wagon monitoring during travel around sharp curves to detect and the occurrence of confirm faults. The functionality envisaged needs only a ‘once per trip’ monitoring site, such as a sharper curve, and is aimed at condition monitoring rather than the provision of alarms or comprehensive monitoring of all events.     

Development of analytical procedure for selection of control measures to reduce congestions at various freeway bottlenecks

Variable speed limit (VSL) and ramp metering (RM) affect freeway traffic operations in different ways and, accordingly, result in different effects on system travel time. The primary objective of this study is to propose an analytical procedure to help determine which control measure should be selected given different freeway bottlenecks and traffic conditions. The bottlenecks considered included an isolated merge bottleneck, a merge bottleneck with a closely spaced upstream off-ramp, and a diverge bottleneck with a closely spaced upstream on-ramp. Two RM and a VSL control strategies were considered, including the ALINEA, ALINEA/Q and feedback based VSL. The maximum achievable improvements in system efficiency by various control measures were calculated and the results were tested using modified cell transmission models. A coordinated control strategy that combined ALINEA/Q and VSL control was also proposed. The effects of VSL and RM control on system travel time at different freeway bottleneck areas were compared to identify the applicable conditions of different control measures. The analytical procedure was proposed for the selection of control measures at different bottlenecks and the ex-ante estimation of control effects were also discussed.     

A stochastic analysis of highway capacity: Empirical evidence and implications

This paper presents a stochastic characterization of highway capacity and explores its implications on ramp metering control at the corridor level. The stochastic variation of highway capacity is captured through a Space–Time Autoregressive Integrated Moving Average (STARIMA) model. It is identified following a Seasonal STARIMA model (0, 0, 2₃) × (0, 1, 0)₂, which indicates that the capacities of adjacent locations are spatially–temporally correlated. Hourly capacity patterns further verify the stochastic nature of highway capacity. The goal of this paper is to study (1) how to take advantage of the extra information, such as capacity variation, and (2) what benefits can be gained from stochastic capacity modeling. The implication of stochastic capacity is investigated through a ramp metering case study. A mean–standard deviation formulation of capacity is proposed to achieve the trade-off between traffic operation efficiency and robustness. Following that, a modified stochastic capacity-constraint ZONE ramp metering scheme embedded cell transmission model algorithm is introduced. The numerical experiment suggests that considering capacity variation information would alleviate the spillback effect and improve throughput. Monte Carlo simulation further supports this argument. This study helps verify and characterize the stochastic nature of capacity, validates the benefits of using capacity variation information, and thus enhances the necessity of implementing stochastic capacity in traffic operation.