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.     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

Projected prevalence of car-sharing in four Asian-Pacific countries in 2030: What the experts think

This paper presents a “big-picture view” for policymakers and related stakeholders regarding the future development of car-sharing services. Car-sharing has the potential to significantly disrupt the personal mobility market. Thus, understanding their market penetration and implications is urgently needed. Previous studies in this domain have predominantly focused on the views, opinions, and preferences of consumers. In this study, we complement the current demand modelling research on car-sharing by applying an expert elicitation and aggregation technique that relies on transport experts’ opinions to investigate the role of car-sharing in the future. Specifically, based on the opinions of mobility suppliers, this research elicits experts’ judgment from across government, industry, and academia to gain insights into the future of car-sharing markets in four countries – Australia, Malaysia, Indonesia, and Thailand. The analysis reveals that, from a mobility supplier’s perspective, energy and vehicle prices will not have a statistically significant impact on the future adoption of car-sharing. The results also show that the more knowledgeable an expert is, the more pessimistic they are about the market penetration of car-sharing in 2016, and the more optimistic they are about the prevalence of car-sharing in 2030.     

A new model for temporal–spatial stochastic analysis of vehicle–track coupled systems

The vehicle–track coupled system has a random nature in the time–space domain. This paper proposes a computational model to analyse the temporal–spatial stochastic vibrations of vehicle–track systems, where the vehicle–track system is divided into a vehicle subsystem, track subsystem, and interfacial subsystem between the wheel and rail. In this model, the time-varying randomicity of dynamical parameters of the vehicle system, correlation, and randomness of the track structural parameters in the time–space joint dimensions, and randomness of the track random irregularities are considered. A probability dimension-reduction method was used to randomly combine different random variables. Furthermore, the probability density evolution method was applied to solve the delivery problem of probabilities between excitation inputs and response outputs. The temporal–spatial stochastic vibrations of the vehicle–track system with different coefficients of variation were studied, in which we assumed that the dynamic parameters obeyed the normal distribution, and the stochastic simulation method of the track random irregularities is probed into. The calculated results from this model are consistent with the actual measured results and physical conceptions. Thus, the temporal–spatial stochastic evolutionary mechanism can be explored, and the limits of dynamic indices can be formulated by using this developed model.     

某型潜艇尾流自导鱼雷命中概率方法研究*

尾流自导鱼雷命中概率是考核鱼雷作战效能的重要指标之一,它同鱼雷自身性能和目标尾流特性有直接关系。论文在研究水面舰艇尾流特性的基础上,建立了直航目标和机动目标的尾流区域模型,分析了鱼雷的攻击态势和射击方法,提出了鱼雷捕获概率和追踪概率的计算方法。该方法对计算鱼雷武器系统命中概率有一定的指导作用。     

基于贝叶斯网络的车辆目标功能毁伤评估模型

应用贝叶斯网络在解决不确定性事件方面的推理优势,根据车辆目标的毁伤特征分析建立了贝叶斯网络功能毁伤评估模型。在目标物理毁伤信息分析的基础上,通过实例演示了评估过程,验证了用贝叶斯网络进行功能毁伤评估的可行性与有效性。     

带延迟的双险种复合Poisson风险模型

由于保险公司风险经营规模的不断扩大,考虑到单险种风险模型的局限性,研究了带延迟的双险种复合Poisson风险模型,给出了该模型破产概率的表达式及有限时间生存概率等重要指标.     

一类离散双险种风险模型的破产概率

研究了一类离散双险种风险模型,对此模型得到了最终破产概率的一般表达式,Lundberg不等式,及当险种Ⅱ的保费收取随机序列与两险种的个体索赔额均服从指数分布时的有限时间破产概率的上界估计.     

鱼雷攻击潜艇的毁伤效果评估模型研究

客观、准确地评价鱼雷攻击潜艇的毁伤效果是鱼雷与潜艇对抗的最后一环,也是评价鱼雷武器系统作战效能的重要指标。本文在现有毁伤概率计算方法的基础上引进了舱段的毁伤程度、要害指数、命中概率、毁伤效果累积系数和齐射毁伤系数等参数,建立了一种比较全面的用来评价鱼雷攻击潜艇毁伤效果的数学模型,并给出了典型应用。     

高地应力千枚岩隧道二次衬砌施作时机研究

二次衬砌施作时机一直是高地应力软岩隧道工程设计与施工过程中面临的关键技术难题之一。为此,依托在建成都-兰州铁路典型千枚岩隧道工程,基于隧道变形长期监测结果,分析高地应力软岩隧道变形时程特点,考虑软岩隧道荷载特点,确定了二次衬砌施作时机原则;考虑隧道测量丢失变形,提出软岩隧道第1稳定阶段变形量确定方法;通过现场实测变形数据统计回归,基于一定保证率确定不同大变形等级和不同断面下的软岩隧道二次衬砌施作时机,并进行现场试验验证。研究结果表明:适当刚度的初期支护可以实现高地应力软岩隧道前期变形稳定,但无法保持围岩长期稳定,二次衬砌应该在初期支护变形达到第1稳定阶段后施作,既可以减少二次衬砌荷载,又可以控制围岩变形;采用指数函数拟合软岩隧道变形具有较好的相关性,但参数差异性较大,同时在确定隧道第1稳定阶段变形量时应考虑测量丢失变形;轻微、中等大变形段拱顶下沉变形速率小于0.1~0.2mm·d^-1,边墙收敛速率小于0.5mm·d^-1,严重、极严重大变形段拱顶下沉变形速率小于0.4mm·d^-1,边墙收敛小于0.6mm·d^-1,即可进行二次衬砌施作;轻微大变形段、中等大变形段和严重大变形段分别在隧道开挖45~55 d,55~60 d和80~90 d后达到二次衬砌施作标准。