前簧弯耳平销断裂失效分析

通过化学成分分析、硬度测试、渗碳层深度检测、断口形貌分析以及微观组织检查等实验分析,确定了重型卡车前簧弯耳平销断裂原因。分析结果表明：碳浓度过高,致使零件表层析出大量断续网状碳化物,增大了材料局部脆性,弱化了晶界,导致零件在装配时发生断裂。     

基于数据仓库的交通信息发布系统

针对交通管理系统中普遍存在的信息资源孤立和浪费问题,提出了基于数据仓库的信息整合和数据挖掘的交通信息发布系统,重点阐述了系统目标、系统构架方案,以及整个系统实现和功能分析。     

多体系统动力学分析的两大软件——ADAMS和DADS

对多体系统分析(机械系统仿真分析)领域中最广泛使用的两个软件ADAMS和DADS，从理论基础、组成模块、分析功能和建模元素等几个方面进行了对比，从而对二的特点有进一步的了解，便于选用最为适合的软件。最后，给出了二相应的应用实例。     

铁路货运大数据应用策略探讨

铁路货运大数据作为铁路重要的信息资产,集数据类型多、数据增长速度快、数据精确度高等主要特征于一身,具有巨大的发展潜力及应用价值。在分析铁路货运大数据特征的基础上,针对目前铁路货运大数据存在的内部数据共享不足、外部数据融合不够、数据分析能力不足等问题,从货运生产、货运营销和安全管理3个方面提出铁路货运大数据应用策略,达到促进铁路货运生产、经营和安全管理水平提升的效果。     

RV减速器摆线轮关键误差软件制作技术及数据处理

为方便快速地进行应用程序的可视化开发,文章选取了基于.NET Framework框架的Visual Studio 2019作为开发平台,以C#为编程语言,对软件的窗体、菜单、数据处理等交互功能进行了界面设计,准确效率地完成了多个典型零件的参数传递;后台采用链接技术调用Fortran语言进行误差计算,并将计算结果通过St...     

Exploring public transport sustainability with neutrosophic logic

Sustainable transportation is a significant component of overall sustainable development. Increasingly, evaluation and decision-making with respect to major complex projects (for example, transportation and land use projects), a multiple attribute perspective is taken. This paper illustrates a multiple attribute decision-making approach for selecting sustainable public transportation systems under uncertainty, that is, with partial or incomplete information represented by single-valued neutrosophic sets (SVNSs). A SVNS is a generalization of a classical set, a fuzzy set, and an intuitionistic fuzzy set. Here, SVNSs and SVNS connectives are illustrated in the context of a ‘Public Transit Sustainable Mobility Analysis Tool’ (PTSMAT) which involves a composite (multiple attribute) sustainability index. A case study of PTSMAT is provided for the UBC Corridor study in Vancouver, Canada. As expected, similar results are obtained to the original study, though the neutrosophic formalism opens a wide range of possibilities for recognition of uncertainty in sustainability assessment.     

Individual trip rate transferability analysis based on a decision tree approach

Transferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.     

Density/Flow reconstruction via heterogeneous sources and Optimal Sensor Placement in road networks

This paper addresses the two problems of flow and density reconstruction in Road Transportation Networks with heterogeneous information sources and cost effective sensor placement. Following a standard modeling approach, the network is partitioned in cells, whose vehicle densities change dynamically in time according to first order conservation laws. The first problem is to estimate flow and the density of vehicles using as sources of information standard fixed sensors, precise but expensive, and Floating Car Data, less precise due to low penetration rates, but already available on most of main roads. A data fusion algorithm is proposed to merge the two sources of information to estimate the network state. The second problem is to place sensors by trading off between cost and performance. A relaxation of the problem, based on the concept of Virtual Variances, is proposed and solved using convex optimization tools. The efficiency of the designed strategies is shown on a regular grid and in the real world scenario of Rocade Sud in Grenoble, France, a ring road 10.5 km long.     

Extracting accurate location information from a highly inaccurate traffic accident dataset: A methodology based on a string matching technique

The objective of this research was to develop a model for validating traffic accident locations that would be applicable worldwide, regardless of linguistic or cultural differences. In order to achieve this, a Volunteered Geographic Information (VGI) dataset was used, the OpenStreetMap (OSM) project. To test the developed model, a total of 8550 accidents with fatal or non-fatal injuries that occurred in the City of Zagreb from 2010 to 2014 were evaluated. Traffic accident data was collected using the pen-and-paper method while the traffic accident locations were determined using Global Positioning System (GPS) receivers embedded within police vehicles. This form of data entry invariably introduces errors in both geometric and contextual attributes. To fully counteract these errors, the developed model consists of two key concepts: the Jaro–Winkler string matching technique and the Inverse Distance Weighting method. Over 66% of traffic accident locations were validated, which is an increase of 15% when compared to the classical approach. The model outlined in this paper shows a significant improvement in estimating the correct location of traffic accidents. This in turn results in a drastic decrease in resources needed to estimate the quality of accident locations.     

Capturing the conditions that introduce systematic variation in bike-sharing travel behavior using data mining techniques

The potential of smart-card transactions within bike-sharing systems (BSS) is still to be explored. This research proposes an original offline data mining procedure that takes advantage of the quality of these data to analyze the bike usage casuistry within a sharing scheme. A difference is made between usage and travel behavior: the usage is described by the actual trip-chaining gathered with every smart-card transaction and is directly influenced by the limitations of the BSS as a public renting service, while the travel behavior relates to the spatio-temporal distribution, the travel time and trip purpose. The proposed approach is based on the hypothesis that there are systematic usage types which can be described through a set of conditions that permit to classify the rentals and reduce the heterogeneity in travel patterns. Hence, the proposed algorithm is a powerful tool to characterize the actual demand for bike-sharing systems. Furthermore, the results show that its potential goes well beyond that since service deficiencies rapidly arise and their impacts can be measured in terms of demand. Consequently, this research contributes to the state of knowledge on cycling behavior within public systems and it is also a key instrument beneficial to both decision makers and operators assisting the demand analysis, the service redesign and its optimization.