8XC196KC用于ABS轮速测量方法的改进研究

对ABS中速度测量的过程进行仔细的分析,提出了一种能够将精度和实时性相结合的速度测量方案,该方案能够较为有效地平衡精度和实时性之间的矛盾,并通过实际程序的执行验证了这一结论。这一测量方法不仅可用于ABS的速度测量,也可推广用于其他的速度或周期测量方案中。     

Evaluation of detection sensitivity and count performance of advanced vehicle detection technologies at a signalized intersection

This article evaluates the stop-bar detection and count performance of three advanced vehicle detection sensors under various environmental conditions at a signalized intersection. Continuing advancements to vehicle detection technologies and improvements to their detection capabilities to overcome issues from impacting conditions necessitates testing the performance of new and upgraded sensor products to evaluate their performance and identify the most suitable products for various climates and weather conditions. The three evaluated sensors were Autoscope Encore video, Iteris Vantage Edge 2 video, and Wavetronix SmartSensor Matrix microwave sensors. The three sensors performed with high detection sensitivity during ideal environmental conditions with up to 99.9% detection accuracy levels and are suitable for traffic monitoring centers that rely on remote access to the monitored sites and the collected data. However, they were affected by some extreme adverse weather conditions, mainly daytime and nighttime snow, daytime fog, dawn lighting, and strong winds (for high mounted devices). The selection of a sensor product will depend on the type of application and the priority given to the type of traffic data being collected. Overall, the Iteris video sensor performed with the highest detection sensitivity levels, with the Wavetronix Matrix microwave sensor performing similarly under most conditions (14 of 19 evaluated conditions). Autoscope video provided the highest count accuracies and also provides a much broader data collection capability. The results of this study will help transportation agencies in selecting suitable vehicle detection sensor technologies for future installations within their jurisdiction and improved data collection.     

GSM-R中FAS话务量计算

介绍了GSM-R系统的结构,FAS系统及子系统在整个网络中的位置以及系统话务量的计算方法.分别按不同站型计算各站话务量,以此来确定网络的组网方式.     

从交通肇事对进站信号机的损坏看合理设置进站信号机位置的重要性

本文主要探讨了合理设置进站信号机位置的重要性、必要性和可行性。     

船舶与海洋工程中的焊接变形预测方法研究概述

文章介绍了焊接变形预测理论的发展及现状,并将其归纳为基于实验和理论相结合的经验计算阶段、基于计算机数值模拟的模型分析阶段、基于新兴数据挖掘方法的研究应用阶段三个标志性阶段,详细论述了每个阶段预测技术在实际中应用的优劣.着重以BP、RBF人工神经网络为例阐述了神经网络等数据挖掘方法在焊接变形预测中的应用前景,并指出了目前存在的不足及未来亟待研究的方向.     

基于尺寸精度的铝合金减震器塔结构设计及优化

铝合金作为一种优质轻量化材料在轻量化车身上的应用越来越广泛,为了确保铝合金零部件的使用功能和性能,设计人员在设计前期就需要对零部件的精度进行设计分析和优化。文章基于一种压铸铝合金减震器塔的零部件,首先对该铝合金零部件精度进行分析摸底并识别出影响该铝合金零部件精度的两个关键要素,再分别针对两个关键要素进行优化设计,重点一是优化加强筋结构,以增加减震器塔本体的刚度和抗变形能力,另外是增加热处理工艺中夹具支撑块结构,通过外力支撑来增加抗变形能力。结果表明:合理的加强筋布置能有效提升减震器塔本体的刚度、强度和尺寸精度;热处理时需设计专用支撑夹具,且支撑夹具要保证所有重要安装面的支撑,避免铝合金减震器塔局部出现悬臂,通过外力支撑来防止变形。     

Location planning for transit-based evacuation under the risk of service disruptions

The effectiveness of transit-based emergency evacuation highly depends on the location of pick-up facilities, resource allocation, and management. These facilities themselves are often subject to service disruptions during or after the emergency. This paper proposes a reliable emergency facility location model that determines both pre-emergency facility location planning and the evacuation operations afterwards, while facilities are subject to the risk of disruptions. We analyze how evacuation resource availability leverages individual evacuees’ response to service disruptions, and show how equilibrium of the evacuee arrival process could be reached at a functioning pick-up facility. Based on this equilibrium, an optimal resource allocation strategy is found to balance the tradeoff between the evacuees’ risks and the evacuation agency’s operation costs. This leads to the development of a compact polynomial-size linear integer programming formulation that minimizes the total expected system cost from both pre-emergency planning (e.g., facility set-up) and the evacuation operations (e.g., fleet management, transportation, and exposure to hazardous surroundings) across an exponential number of possible disruption scenarios. We also show how the model can be flexibly used to plan not only pre-disaster evacuation but also post-disaster rescue actions. Numerical experiments and an empirical case study for three coastal cities in the State of Mississippi (Biloxi, Gulfport, and D’lberville) are conducted to study the performance of the proposed models and to draw managerial insights.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory

In this paper, we explore the diurnal dynamics of joint activity participation in a small city in Pennsylvania, USA, using behavioral data and an inventory of business establishments. We account for the variation caused by the collective impact of social, temporal and spatial choices of individuals to produce predicted space–time visualizations of activity participation. The focus is on how social contexts of an activity impact the temporal and spatial decisions regarding the activity locations and how this impact varies depending on activity types. A comparison across activity types and social interaction types is made among spatial patterns during a day. The CentreSIM dataset, which is a household-based activity diary survey collected in Centre County (Pennsylvania, USA) in 2003, provides very detailed social interaction information enabling the analysis of social, spatial and temporal aspects of activity participation. In this paper we use this information to develop a spatio-temporal interpolation method and demonstration based on kriging. In this way, we extract the dynamic social taxonomy of places from the behavioral information in the dataset and suggest how urban and transportation models can be informed from the dynamics of places by observing “what is taking place” (activities being pursued in the context of this paper) combined with “what exists” (business establishments) or “what is available” (businesses that are open). The method here can also be used to improve the design of urban environments (e.g., filling gaps in desired activity locations), manage specific places (e.g., extending the opening and closing times of businesses), study transportation policies that are sensitive to time of day (e.g., pricing of parking to discourage crowding and traffic congestion), and modeling of spatio-temporal decisions of social activities in travel demand models (e.g., to guide the development of model specification and representation of the space in which behavioral models are applied).     

移动智能网在GSM—R铁路专网中的应用

GSM-R是一种专门应用于铁路的综合数字移动通信系统。智能网是在原有通信网的基础上设置的一层叠加网络。本文对GSM-R移动智能网做了简要的描述,并详细介绍了GSM-R的3种特殊的铁路业务,提出了基于移动智能网的业务流程。