Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.     

深海超高压环境模拟容器裂纹评定

深海超高压环境模拟容器用于模拟水下压力环境,其容器壁上承受反复载荷,容易产生疲劳裂纹.疲劳裂纹扩展是影响其断裂的主要因素.本文旨在分析半椭圆裂纹在老化的深海超高压环境模拟容器中的扩展行为,评估容器的安全性,因此对材料20MnMoNb钢的裂纹扩展特性进行了试验研究,首先考虑三角形和梯形加载情况,通过比较两组实验结果,考察了其材料对保载时间的敏感性.采用基于统一的裂纹扩展率模型的三维有限元方法进行了疲劳裂纹扩展计算,并通过CT试样的一组数值和实验结果进行了验证,最后建立了不同初始尺寸、展弦比和倾角的裂纹有限元模型,并根据裂纹在容器内壁的容许深度准则,计算了容器的剩余寿命.其分析结果可为深海超高压环境模拟容器可靠性评估提供参考.     

船舶推进同步电机PTI/PTO模式研究

PTI/PTO推进是最近发展起来的一种新型船舶推进方式,兼具传统柴油机推进和电力推进优点,可实现多种船舶工作模式,具有操控性好、安全、经济等特点,将是未来船舶推进方式发展方向之一.本文介绍了PTI/PTO推进模式理论和工作模式,便于理解该推进方式,希望有助于船舶推进技术的发展.     

Is the intention to use public transport for different travel purposes determined by different factors?

The objective of this study was to examine the psychological predictors of the intention to use public transport for three travel purposes: work or study, shopping, and leisure. An expanded version of the theory of planned behaviour (TPB) which contains overall image and past behaviour is used. Data were gathered through the survey of 392 residents living in the central parts of Kuala Lumpur in Malaysia. These data were analysed using the partial least squares technique. The results indicate that attitude and perceived behavioural control are significant predictors of the intention to use public transportation for various purposes. Further, they explain between 34.6% and 49.8% of the intention variance. By adding the overall image and past behaviour to the original predictors in the TPB, the explained variance, with regard to work or study, shopping, and leisure purposes, increased by 5.6%, 5.1%, and 6.8%, respectively.     

基于多元回归方法的海洋执法船主尺度分析

为分析海洋执法船主尺度之间的变化规律,对海洋执法船的发展趋势进行了介绍,并对其相关船型数据资料进行整理分析。采用多元回归的数学方法,建立了基于多变量的海洋执法船主尺度数学模型,有利于对海洋执法船主尺度的变化规律进行分析与掌握,为海洋执法船的报价设计与初步设计提供一定的科学依据。     

Real-time trip purpose prediction using online location-based search and discovery services

The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.     

关于按ASME VIII-1设计的可移动罐柜的防脆断措施分析

可移动罐柜越来越多的被应用于国际危险货物运输[1],不同的设计标准和规范均定义了不同的关于防脆断措施的要求;ASME VIII-1[2]作为国际通用的压力容器设计和制造标准,详细介绍了金属受压材料的防脆断的措施,但是鉴于ASME章节过多和其排版的差异,很难全面理解。本文将结合目前国际主流的可移动罐柜规范和ASME VIII-1的要求,分析在设计和生产可移动罐柜时对金属材料防脆断要求的理解及措施。     

两种大型集装箱船绑扎桥结构强度的对比计算浅析

以大型集装箱船常见的剪力墙式绑扎桥和A型斜撑式绑扎桥为研究对象,基于通用大型有限元分析软件PATRAN/NASTRAN及CCS规范的相关要求,对两种绑扎桥的结构强度进行了对比计算分析。结果表明：两种型式的绑扎桥在结构强度方面相差不大,相比而言,剪力墙式绑扎桥略优于A型斜撑式。     

浅析西门子BDPC系统在CTV上的 可行性

本文主要目的是分析西门子Blue Drive Plus C（随后简称BDPC）系统及其配置是否能够应用在深水动力定位原油输送船（随后称CTV）上。通过供需关系对比法,首先实现CTV实现海上自由航行、跟随定位、连接FPSO与～30万吨油轮之间浮管、加油跟随定位、紧急脱离、绿色环保运行等工况下的正常与应急储备动力需求,随后分析西门子BDPC直流系统及其运用技术,针对本项目提供的特殊配置,与CTV提出需求进行对比,结论是BDPC依靠它的直流发电、配电、逆变、储能等技术,可以满足CTV的配置需求,论证了BDPC系统运用在性能优越的深水原油输送装置CTV是可行的。     

动力定位船总布置设计方法研究

文章介绍了动力定位船总布置的设计方法,通过解析归纳和分类总结法,从各组成系统和设备角度阐述了相应的总布置要求及设计要点,并根据常见的半潜平台、起重/铺管船、钻井船几种典型动力定位船舶的总布置特征,分析动力定位船,以供相近船型设计参考。