公路隧道太阳能自然通风系统设计与实现

传统隧道在进行通风时存在效率低、自然通风负压不达标、机械风机通风能耗大等问题,为了解决这一问题,文章提出了太阳能自然通风系统,该系统通过对太阳能热效率进行利用实现空气的加热,进而对"热和光进行转换利用",达到节能减排的目的。本文首先对公路隧道太阳能自然通风系统设计的意义进行了分析,然后对公路隧道太阳能自然通风系统的基本原理进行了分析,最后对太阳能自然通风系统的设计与实现进行了探讨。     

独柱墩桥梁抗倾覆性能研究

对独柱墩桥梁上部结构横向倾覆进行了验算,然后采用静力非线性方法对桥梁的实际倾覆过程进行仿真分析,并对二者的验算结果进行深入探讨,提出了几点建议。     

天然气管线ECDA检测评价案例分析

为了减少管道事故及为后续的完整性评价提供依据,文中按照相关标准的要求,对天然气管道应用ECDA方法进行了检测,发现了许多管道异常点,对发现的严重异常点进行了开挖处理。建议对其他异常点进行监测,并有序进行开挖处理。     

新型沥青路面结构在山东省高速公路上的应用

对山东省沥青路面结构的发展和存在的问题进行了回顾,对新建道路的交通量和轴载水平进行了分析,在此基础上提出了新型路面结构的设计思路,并对不同路面结构组合进行了厚度设计对比.     

欧洲内河航运综合信息服务系统(RIS)发展现状与趋势

介绍了欧盟内河航运综合信息服务系统的发展现状,对欧盟RIS协同化发展进程与相关政策环境进行了系统阐述,并对欧盟RIS未来发展趋势进行了分析,最后对欧洲内河航运综合信息服务RIS协同化进行了简要评述。     

基于瞬态模型法的输气管道泄漏检测现场试验研究

对泄漏检测方法进行了介绍,特别对瞬态模型方法的泄漏检测原理做了详细阐述.在永京输气管线杜庄-通捷管段进行泄漏检测试验,应用实时瞬态模型方法进行泄漏检测和定位,得出了泄漏对管线流动参数的影响规律.实验结果表明:瞬态模型泄漏检测法能够准确、快速检测出泄漏,并能够对泄漏量的大小进行准确地判断,使用压力曲线相交的方法可以对泄漏...     

中型城市居民大站快车与常规公交出行差异性分析

文章结合潍坊市城市布局及公共交通发展特点,通过对潍坊市金马街沿线居民进行公交出行意愿调查,建立居民大站快车与常规公交出行选择意愿的BL模型。对模型进行标定,分析影响居民公交出行行为的显著性因素,并对大站快车与常规公交出行分担率进行预测,对接驳距离进行弹性分析,结果表明接驳距离在一定范围内对大站快车出行分担率影响较大。     

我国高速公路交通拥堵问题之规划反思

本文对我国高速公路拥堵状况进行了典型回顾,对拥堵特征进行了分析,并从公路网规划思想和规划方法体系两方面进行了反思。     

水泥土换填法在粉质砂土路基施工中的应用研究

文章依托某公路路基施工项目,以水泥土换填方案对原路基填土进行改良增强,针对水泥、施工项目现场素土以及掺配好的水泥土进行指标检测;对常规室内击实试验进行优化,通过击实试验对掺配完成0～4 h的水泥土进行对比试验分析,得出符合施工现场状态的水泥土最大干密度;对人工及机械水泥撒布方式的经济成本进行了对比与优选,确定了粉料机撒...     

交通运输现代化评价指标体系初探

本文在对交通运输现代化的内涵和基本特征进行分析的基础上,通过建立一个树状的多层次综合评价指标体系,从设施水平、服务水平、管理与技术水平、社会经济效益水平、资源利用与环境影响水平五大方面对交通运输现代化水平进行衡量,然后尝试用综合指标评价法对我国目前的交通运输现代化水平进行定量评价,最后对评价所反映的问题进行分析,并提出相应的政策建议。     

蒙内铁路长大下坡道制动试验及计算分析

蒙内铁路作为连接东非第一大港蒙巴萨和肯尼亚首都内罗毕的一条客货并行一级线路,对肯尼亚国内以及东非地区的经济发展和政治稳定具有重要的意义,也对于我国"一带一路"战略的实施以巩固中-肯关系和中非关系有着至关重要的意义.由于较为特殊的地理原因使得蒙内铁路全线长大坡道占比超过50％,使得长大坡道上的行车安全成为蒙内铁路安全运营的重中之重.本文以11.7‰的长大下坡道为研究对象,介绍了以DF11机车牵引,17辆编组时以118km/h进行紧急制动试验的情况,制动距离为890m;紧急制动前有充足的充风时间,制动实施至列车管压减为0用时3s,符合规定要求.基于试验结果验证了理论计算和软件计算的可靠性和准确性,计算结果指出为了使17辆编组列车在800m制动距离以内完成制动,当坡度为12‰时,建议其制动速度不高于112km/h,坡度6‰以下时,建议其制动速度不高于118km/h.本文的分析和计算结果旨在为蒙内铁路长大下坡道的安全行车运营以及整体安全控制体系的制定提供有效可靠的试验依据和理论支撑.     

Performability evaluation of the ERTMS/ETCS – Level 3

Level 3 of the ERTMS/ETCS improves the capacity of railways by replacing fixed-block signalling, which prevents a train to enter a block occupied by another train, with moving block signalling, which allows a train to proceed as long as it receives radio messages ensuring that the track ahead is clear of other trains. If messages are lost, a train must stop for safety reasons within a given deadline, even though the track ahead is clear, making the availability of the communication link crucial for successful operation.We combine analytic evaluation of failures due to burst noise and connection losses with numerical solution of a non-Markovian model representing also failures due to handovers between radio stations. In so doing, we show that handovers experienced by a pair of chasing trains periodically affect the availability of the radio link, making behavior of the overall communication system recurrent over the hyper-period of periodic message releases and periodic arrivals at cell borders. As a notable aspect, non-Markovian transient analysis within two hyper-periods is sufficient to derive an upper bound on the first-passage time distribution to an emergency brake, permitting to achieve a trade-off between railway throughput and stop probability. A sensitivity analysis is performed with respect to train speed and headway distance, permitting to gain insight into the consequences of system-level design choices.     

高速铁路桥上无缝线路制动附加力研究

制动荷载是高速铁路桥梁的重要荷载，但高速列车在双线高速铁路桥梁上单向或双线对向制动时，制动荷载并不具有对称性，而双线高速铁路整孔箱形梁具有很强的空间性，平面力学模型不能很好反映上述工况，有着很大的局限性。本文在吸收专家研究成果的基础上，建立了梁轨相互作用三维有限元空间力学模型，并用该模型对高速列车在桥上不同制动工况下引...     

Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions

Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.     

利用SPS模拟川气东送管道压气站失效问题

为研究压气站失效与单台机组失效对输气管道的影响程度,采用SPS软件对压气站失效和单台机组失效进行了详细模拟。模拟结果显示:压气站失效与单台机组失效相比对管输系统影响较大;对于压气站失效,越靠近末站的压气站失效对管输系统影响越大;对于单台机组失效,越靠近首站的压缩机失效对管输系统影响越大。分析结果可为事故状态下的供气和抢修提供参考。     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Does driving behavior matter? An analysis of fuel consumption data from heavy-duty trucks

In a case study of a Norwegian heavy-duty truck transport company, we analyzed data generated by the online fleet management system Dynafleet. The objective was to find out what influenced fuel consumption. We used a set of driving indicators as explanatory variables: load weight, trailer type, route, brake horsepower, average speed, automatic gearshift use, cruise-control use, use of more than 90% of maximum torque, a dummy variable for seasonal variation, use of running idle, use of driving in highest gear, brake applications, number of stops and rolling without engine load. We found, via multivariate regression analysis and corresponding mean elasticity analysis, that with driving on narrow mountainous roads, variables associated with infrastructure and vehicle properties have a larger influence than driver-influenced variables do. However, we found that even under these challenging infrastructure conditions, driving behavior matters. Our findings and analysis could help transport companies decide how to use fleet management data to reduce fuel consumption by choosing the right vehicle for each transportation task and identifying environmentally and economically benign ways of driving.     

Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

在役天然气管道抢修技术适应性评价

天然气管道在运行过程中会发生腐蚀穿孔、开裂、第三方破坏等事故,可用于管道抢修抢险技术方法包括焊接、夹具、带压封堵、复合材料补强等。基于不同管线工况条件、环境状况、地形条件、抢修技术特点、抢修设备与人员素质的具体情况,采用层次分析方法分析天然气管道不同抢修方案的可行性。通过应用实例分析了不同抢修技术方案的适应性,得到了不同方案的优先等级,可用于指导天然气管道抢修作业和应急响应决策的制定。     

HGM-D型高摩合成闸瓦在肯尼亚蒙内铁路的运用

介绍了肯尼亚蒙内铁路概况及线路特点,HGM-D型高摩合成闸瓦性能参数以及在蒙内铁路C70E、X70、NX70型货车上运用验证情况.验证表明HGM-D型高摩合成闸瓦有效降低车轮和闸瓦磨耗量以及闸瓦金属镶嵌物,提高了车辆运行品质和制动效能.