无缝隙视角下海事机构网上行政审批“全程网办”改革研究

近年来,公众在"互联网+政务服务"中体验到了人性化、便捷化、多元化的优质服务,但网上行政审批"全程网办"也出现了多重问题。以扬州地方海事为研究案例,以"无缝隙政府"为研究理论框架,提出了构建无缝隙的网上行政审批"全程网办"的路径。     

地铁车辆直线电机气隙在线检测系统结构

气隙在线检测系统是集数据采集、分析、处理与实时通信于一体的计算机测量系统。作为直线电机地铁车辆的维护设备之一,当车辆通过系统时,其可以实现自动辨别车号、计算车速、记录轴数、测量气隙高度等功能,并通过计算机内部设备的判别模型,对超限数据进行报警。     

铁路基层站段网络培训系统的构建

以洛阳火车站职工培训为背景,介绍了网络培训系统的总体结构和功能特点。该系统综合应用了网络通信技术、分布式技术、旁路采集技术和数据同步传送技术等,实现了企业职工网络培训,具有直播课堂、点播课件、网络考试、非正常行车计算机仿真模拟和教学管理等功能,大大提高了职工培训效率。     

1300MPa级在线热处理钢轨使用性能研究

对铺设在北京、太原、上海和郑州铁路局的半径为300~500 m曲线上的60 kg.m-1PG4在线热处理钢轨的使用性能进行跟踪观测,研究提高重载小半径曲线钢轨的使用寿命。研究结果表明,60 kg.m-1PG4在线热处理钢轨硬度高、耐磨性能好;在使用初期,钢轨磨耗少,钢轨表面容易出现剥离掉块,此时如果采用液体润滑,会加剧钢轨表面的剥离掉块;钢轨的磨耗主要出现在曲线的圆缓或缓圆部位,当钢轨磨耗到一定程度后,侧磨加快,如果采用正确的固体润滑方法,可使钢轨的侧磨得到有效地控制,且钢轨不会出现严重的剥离掉块;PG4在线热处理钢轨的综合使用性能良好,耐磨性能比U75V热处理钢轨提高1倍以上,适合在钢轨磨耗严重的重载铁路小半径曲线上使用;对钢轨实施预防性打磨、设置适当的欠超高、采用1∶20的轨底坡等改善轮轨匹配关系降低接触应力的措施,均对提高重载铁路曲线区段上钢轨的使用寿命具有重要的作用。     

智能润滑技术在船舶智能化发展中的应用

船舶智能化已成为船舶行业未来发展的必然趋势。针对传统船舶润滑系统的使用和维护现状,提出智能润滑技术,并以智能润滑技术为基础设计出智能润滑装置。通过"在线监测、智能净化、故障预警"的智能润滑技术替代"离线检测、定期换油、事后维修"的传统润滑模式,实现船舶润滑系统的智能管理,全天候保证润滑系统的高质量油品品质,降低设备故障率,从而保障船机安全性,提高船舶润滑系统的可靠性和智能化程度。     

Online license plate matching procedures using license-plate recognition machines and new weighted edit distance

License-plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring, and access control. Recently, there has been effort to exploit an LPR database for vehicle tracking using popular template matching procedures. Existing template matching procedures assume that the true reference string is always available. However, under a two-point LPR survey, a vehicle could have its plate misread at both locations generating a pair of misread strings (or templates) with no reference for matching. To compensate for LPR misreading problem, we propose a new weight function based on a probability model to match the observed outcomes of a dual LPR setup. Also, considering that reversal errors are never made in LPR machines, new editing constraints as a function of the string lengths are proposed to avoid compensation for reversal errors. These editing constraints are incorporated into the constraint edit distance formulation to improve the performance of the matching procedure. Finally, considering that previous template matching procedures do not take advantage of passage time information available in LPR databases, we present an online tracking procedure that considers the properties of probability distribution of vehicle journey times in order to increase the probability of correct matches. Experimental results show that our proposed procedure can improve the accuracy of LPR systems and achieve up to 97% of positive matches with no false matches. Further research is needed to extend the ideas proposed herein to plate-matching with multiple, i.e., more than two, LPR units.     

Explore the relationship between online shopping and shopping trips: An analysis with the 2009 NHTS data

The rapid growth of ecommerce brings great changes to the transportation system. However, most existing studies focus on the impact of ecommerce on freight system. Its impact on personal trips is relatively less studied. It is reasonable to argue that online shopping reduces the need of shopping trips by making goods accessible via door-to-door deliveries. On the other hand, online shopping may also create more shopping trips as online shoppers travel to stores to experience, compare or pick up the goods. Understanding the connections between online shopping and shopping trips is critical for transportation planners to prepare for changes that information technology will continue to bring to this nation in the future. Using the 2009 National Household Travel Survey (NHTS) data and a structural equation model (SEM), this paper disentangles the bidirectional connections between online shopping and shopping trips. Results show that online shopping encourages shopping trips while shopping trips tend to suppress the online shopping propensity. Besides, both online shopping and shopping trips are influenced by exogenous factors such as shoppers’ demographic features, regional specific factors and household attributes. A closer examination at the state level further confirms model validity while disclosing spatial variation in their relationship.     

Enhancing the performance of existing urban traffic light control through extremum-seeking

Urban traffic light controllers are responsible for maintaining good performance within the transport network. Most existing and proposed controllers have design parameters that require some degree of tuning, with the sensitivity of the performance measure to the parameter often high. To date, tuning has been largely treated as a manual calibration exercise but ignores the effects of changes in traffic condition, such as demand profile evolution due to urban population growth. To address this potential shortcoming, we seek to use a newly developed extremum-seeker to calibrate the parameters of existing urban traffic light controllers in real-time such that a certain performance measure is optimised. The results are demonstrated for three categories of traffic controllers on a microscopic urban traffic simulation. It is demonstrated that the extremum-seeking scheme is able to seek the optimal parameters, with respect to a certain performance measure, for each of these traffic light controllers in an urban, uni-modal traffic environment.     

Analyzing high speed rail passengers’ train choices based on new online booking data in China

This study explores two nonparametric machine learning methods, namely support vector regression (SVR) and artificial neural networks (ANN), for understanding and predicting high-speed rail (HSR) travelers’ choices of ticket purchase timings, train types, and travel classes, using ticket sales data. In the train choice literature, discrete choice analysis is the predominant approach and many variants of logit models have been developed. Alternatively, emerging travel choice studies adopt non-utility-based methods, especially nonparametric machine learning methods including SVR and ANN, because (1) those methods do not rely on assumptions on the relations between choices and explanatory variables or any prior knowledge of the underlying relations; (2) they have superb capabilities of iteratively identifying patterns and extracting rules from data. This paper thus contributes to the HSR train choice literature by applying and comparing SVR and ANN with a real-world case study of the Shanghai-Beijing HSR market in China. A new normalized metric capturing both the load factor and the booking lead time is proposed as the target variable and several train service attributes, such as day of week, departure time, travel time, fare, are identified as input variables. Computational results demonstrate that both SVR and ANN can predict the train choice behavior with high accuracy, outperforming the linear regression approach. Potential applications of this study, such as rail pricing reform, have also been identified.     

The impact of advance purchase deadlines on airline consumers’ search and purchase behaviors

Airlines frequently use advance purchase ticket deadlines to segment consumers. Few empirical studies have investigated how individuals respond to advance purchase deadlines and price uncertainties induced by these deadlines. We model the number of searches (and purchases) for specific search and departure dates using an instrumental variable approach that corrects for price endogeneity. Results show that search and purchase behaviors vary by search day of week, days from departure, lowest offered fares, variation in lowest offered fares across competitors, and market distance. After controlling for the presence of web bots, we find that the number of consumer searches increases just prior to an advance purchase deadline. This increase can be explained by consumers switching their desired departure dates by one or two days to avoid higher fares that occur immediately after an advance purchase deadline has passed. This reallocation of demand has significant practical implications for the airline industry because the majority of revenue management and scheduling decision support systems currently do not incorporate these behaviors.