上海市公交柴油车碳烟排放的评估与分析

介绍了上海市道路交通以及公交汽车拥有量的发展；按照发动机购置年份、机型以及车队等统计上海市公交柴油车碳烟排放状况；追踪某一车队同一机型连续几年来的百公里油耗以及碳烟排放水平，分析了影响公交柴油车碳烟排放的各种因素；最后从管理方面提出加强I／M制度以降低公交柴油车碳烟排放。     

Extreme hawser tension assessment for FPSO vessel during offloading operation in Bohai bay

The Floating Production Storage and Offloading Unit (FPSO) is an offshore vessel that produces and stores crude oil prior to tanker transport.Robust prediction of extreme hawser tensions during Floating Production Storage and Offloading (FPSO) operation is an important safety concern. Excessive hawser tension may occur during offloading operations, posing an operational risk. In this paper, AQWA has been used to analyze vessel response due to hydrodynamic wave loads, acting on a specific FPSO vessel under actual sea conditions. Experimental validation of numerical results has been discussed as well.This paper advocates methodology for estimating extreme response statistics, based on simulations (or measurements). The modified ACER (averaged conditional exceedance rate) method is presented in brief detail. Proposed methodology provides an accurate extreme value prediction, utilizing all available data efficiently. In this study the estimated return level values, obtained by ACER method, are compared to the corresponding return level values obtained by Gumbel method. Based on the overall performance of the proposed method, it is concluded that the improved ACER method can provide more robust and accurate prediction of the extreme hawser tension.Data declustering issue has been addressed. Paper highlights ability of ACER method to account for a set of varying sea state probabilities, as required in engineering long term statistical analysis.Described approach may be well used at the vessel design stage, while defining optimal vessel parameters that would minimize potential FPSO hawser tension.     

用SPC统计技术控制机车弯管质量

介绍了一种预防机车管道弯制质量问题的方法。用SPC统计技术中的Xbar—R图对管道弯制角度进行现场控制,可以及时发现异常,在质量问题发生之前采取措施,减少质量事故。     

公路发展水平评价指标的研究

通过研究传统的反映公路发展水平的公路统计指标及其存在的问题,分析反映公路发展水平的因素,提出一套全面准确合理的反映公路发展水平的综合评价指标。     

提高固定资产投资价格统计数据质量的探讨

文章针对目前建筑安装工程固定资产价格统计工作的现状,分析影响施工企业固定资产投资价格统计数据质量的主要因素,从把握工作重点的原则和解决工作难点等四个方面,提出了提高施工企业固定资产投资价格统计数据质量的对策。     

沪昆高铁防灾系统改造与优化

通过统计告警分布,对高铁防灾设备电源类和网络类告警的原因、特点进行分析和判断,并给出处理和减少此两类告警的思路和实施方案。     

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.     

货物列车指标统计系统的设计与实现

分析货物列车指标的统计现状及重要性,介绍基于区段货物列车指标统计系统的目标和功能.     

铁路局(分局)级装车指标完成情况统计系统

介绍了一种铁路局(分局)级装车指标完成情况统计系统,此方法在定义统计范围时具有非常灵活的特性,可维护性强,解决了以往铁路局(分局)级装车指标完成情况统计系统统计范围定义过死,通用性不好的问题.     

Predicting travel time reliability using mobile phone GPS data

Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.