渤海波浪地理信息系统的建立

随着地理信息系统(GIS)的广泛应用,建立一个具有波浪预报或后报功能的信息系统为工程建设服务,不仅十分必要,而且随着波浪模拟技术的发展变得可能。文中论述了采用第三代波浪模型中的SWAN(SimulatingWAves Nearshore)来模拟渤海波浪场,在获得较长时间波浪模拟结果的基础上,对渤海波浪地理信息系统的建立进行了一些尝试和探索。这些波浪资料对于港口及海洋工程是十分必要的。     

尺度适应性二代小波的图像去噪方法

根据二代小波变换的基本理论和特点,研究二代小波对图像去噪的效果。提出基于二代小波的尺度适应性分解算法,并使用改进阈值函数进行阈值分析后再对噪声图像进行去噪处理。实验结果证明,使用尺度适应性二代小波对图像去噪比其他方法具有更好的效果,去噪后的图像信噪比大大提高。     

新一代调度集中（CTC）对无线移动通信的需求分析及实现方案探讨

本文简要介绍了新一代调度集中的主要特点，重点分析其对无线移动通信的要求，并分析了目前常见解决方案各自的利弊，就存在的问题提出了观点。     

光伏发电技术在轨道交通客车中的应用

轨道交通客车在载客运行的过程中消耗大量的电能。采用光伏发电技术后,蓄电池组将太阳能电池发出的电能存储,并随时与客车充电机进行电耦合,共同为客车供电。客车光伏电系统主要由光伏发电系统充电机、升降压斩波器、直流负载及供电控制系统组成。其中,光伏发电系统主要由光伏电池组件、发电控制器、蓄电池组及升降压斩波器组成。详细介绍了客车光伏供电系统的工作原理。     

基于分布式发电的单相双凸极直流发电机原理和时步有限元分析

分布式发电(DG)是指在公共电力系统中的关键部位或在用电负载处使用小型发电机(组)供电的模式,合理发展分布式供电有利于节约能源和保护环境。文章提出单相双凸极直流发电机作为小型清洁能源分布式直流电源系统的主发电机,其结构简单、功率重量比高、转速高,可以起到简化系统、降低成本、提高可靠性的作用。首先介绍了该型电机的原理和构成方法,然后运用时步有限元法对发电机进行电磁计算,分析、比较了电励磁、永磁单相4极/6极(定子极数/转子极数)双凸极直流发电机和三相电励磁6极/4极双凸极直流发电机的外特性和功率曲线,以及电励磁单相4极/6极双凸极直流发电机的3种发电方式的外特性和功率曲线,最后分析并仿真验证了减小输出电压脉动的方法。     

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.     

GSM-R中跨SGSN更新时源SGSN域名解析数据的智能生成方案设计

源SGSN域名解析数据是GSM-R网络编号方案数据中重要的需跨局制作的一类数据。由RAI编码构造生成,在跨SGSN路由区更新时,存储于DNS中,以解析源SGSN的IP地址。全路400多条G网线路,错综复杂的基站引用关系,导致数据复杂多样。针对该情况,利用聚类分析和关联规则分析算法,研究源SGSN解析数据的智能生成方案,提高数据的全面性、准确性,从而有效保障GSM-R分组域应用业务的正常运转。     

Path-constrained traffic assignment: A trip chain analysis under range anxiety

This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.     

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.     

The generators of paratransit trips by persons with disabilities

Identifying the generators of paratransit trips by persons with disabilities is important to comprehend the current demand patterns and forecast future demand. Only a handful of studies have been conducted so far to identify the generators of paratransit trips and most focused on the home end of the trips. Given some of the inconsistencies in past studies and the scarcity of studies on the generators of trips away from home, this study attempts to identify the generators of paratransit trips beginning and ending at clients’ homes and away from home. It uses an extremely large dataset consisting of 1.91 million trips made by NJ TRANSIT’s Access Link clients, socioeconomic data from the American Community Survey, employment data from the Longitudinal Employer-Household Dynamics, and establishment data from Dun and Bradstreet. The analytical methods include an ordinary least squares model (OLS) and several spatial generalized linear mixed models (GLMM) to identify the characteristics of census block groups associated with Access Link trip generation at home and away from home, Geographic Information System (GIS) analysis to identify the types of establishments located in the immediate vicinity of drop-offs, and a multinomial logit model (MNL) to examine the relationship between the characteristics of the establishments in the vicinity of drop-offs and the characteristics of the dropped-off clients. Together, the various analyses provide useful insights about paratransit trip generators at the macro and micro levels. Some implications of the findings are discussed.