短时交通流预测方法研究进展

实时准确高效的交通流预测是实现交通流诱导和交通控制的关键技术之一,近年来它在智能运输领域受到广泛关注。本文首先介绍了短时交通流的属性和预测要求,接着将现有预测方法分成4类：基于线性理论的方法、基于非线性理论的方法、基于混合理论的方法和基于交通流理论的方法,并且总结评述了现有各种预测模型的优缺点,之后探讨了当今短时交通流预测领域的研究热点,最后指出了其未来研究方向．     

Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.     

Vehicle classification from low-frequency GPS data with recurrent neural networks

The categorization of the type of vehicles on a road network is typically achieved using external sensors, like weight sensors, or from images captured by surveillance cameras. In this paper, we leverage the nowadays widespread adoption of Global Positioning System (GPS) trackers and investigate the use of sequences of GPS points to recognize the type of vehicle producing them (namely, small-duty, medium-duty and heavy-duty vehicles). The few works which already exploited GPS data for vehicle classification rely on hand-crafted features and traditional machine learning algorithms like Support Vector Machines. In this work, we study how performance can be improved by deploying deep learning methods, which are recently achieving state of the art results in the classification of signals from various domains. In particular, we propose an approach based on Long Short-Term Memory (LSTM) recurrent neural networks that are able to learn effective hierarchical and stateful representations for temporal sequences. We provide several insights on what the network learns when trained with GPS data and contextual information, and report experiments on a very large dataset of GPS tracks, where we show how the proposed model significantly improves upon state-of-the-art results.     

松河特大桥盘县岸山体工程地质稳定分析与治理评价

根据勘察资料,应用CSMR分类、采空区矿柱稳定性计算等方法,对下伏煤矿采空区的松河特大桥桥台山体进行稳定性评价,并对山体治理成本进行分析。     

货运代理人收据的法律性质以及与提单的异同

结合有关案例,讨论货运代理人收据的法律性质及与提单的异同,认为货运代理人收据既不是物权凭证,也不是承运人保证据以交付货物的单证,并提醒货物卖方应充分注意FCR运输方式的风险。     

基于数据分类的高速铁路运营事故预测方法

高速铁路运营事故预测方法是度量铁路安全管理水平的重要指标.为提高高速铁路的安全运营水平,引入工业数据分类方法,分析反向传播(BP)神经网络和灰色模型在高速铁路安全运营事故预测过程中的适应性.首先,运用事故次数、事故联动系数、月均事故率3个参数对高速铁路安全运营水平进行度量;然后,根据工业数据分类方法判别高速铁路运营事故数据属于块状型,据此建立反向传播(BP)神经网络运营事故预测模型;针对运营事故数据具有波动大的特点,利用均值聚类方法建立K-GM(1,3)预测模型.以近年来高速铁路运营事故数据为样本对模型进行训练和分析,结果表明:BP神经网络、K-GM(1,3)、GM(1,3)预测模型的预测误差分别为8.92%,13.68%,345.25%,BP神经网络在高速铁路安全运营事故预测过程中的适应性要优于灰度模型.     

Improved vehicle classification from dual-loop detectors in congested traffic

Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Length-based vehicle classification from dual loop detectors is among the lowest cost technologies commonly used for collecting these data. Like many vehicle classification technologies, the dual loop approach works well in free flow traffic. Effective vehicle lengths are measured from the quotient of the detector dwell time and vehicle traversal time between the paired loops. This approach implicitly assumes that vehicle acceleration is negligible, but unfortunately at low speeds this assumption is invalid and length-based classification performance degrades in congestion.To addresses this problem, we seek a solution that relies strictly on the measured effective vehicle length and measured speed. We analytically evaluate the feasible range of true effective vehicle lengths that could underlie a given combination of measured effective vehicle length, measured speed, and unobserved acceleration at a dual loop detector. From this analysis we find that there are small uncertainty zones where the measured length class can differ from the true length class, depending on the unobserved acceleration. In other words, a given combination of measured speed and measured effective vehicle length falling in the uncertainty zones could arise from vehicles with different true length classes. Outside of the uncertainty zones, any error in the measured effective vehicle length due to acceleration will not lead to an error in the measured length class. Thus, by mapping these uncertainty zones, most vehicles can be accurately sorted to a single length class, while the few vehicles that fall within the uncertainty zones are assigned to two or more classes. We find that these uncertainty zones remain small down to about 10 mph and then grow exponentially as speeds drop further.Using empirical data from stop-and-go traffic at a well-tuned loop detector station the best conventional approach does surprisingly well; however, our new approach does even better, reducing the classification error rate due to acceleration by at least a factor of four relative to the best conventional method. Meanwhile, our approach still assigns over 98% of the vehicles to a single class.     

A novel classification method for driving simulators based on existing flight simulator classification standards

As technology has advanced and costs have fallen, the advantages of using simulators to train for safe, economical, and environmentally friendly driving have become more apparent. The need for a driving simulator classification arises from understanding and comparing simulator capabilities and options; however, only a limited number of studies have been conducted related to classification, calling for determination of methods and criteria. In this study, a classification method for driving simulators is proposed by adapting criteria for helicopter flight simulation training devices in which established methods of classification are defined by international and national regulators such as the Joint Aviation Authorities and Federal Aviation Administration. In the proposed method, the level of a simulator is determined by taking general characteristics under consideration, such as motion, visual, and sound systems. Through a case study, the method was applied to determine the class of a specific truck simulator.     

软件测试与测试方法浅析

就软件开发与软件测试的关系、软件测试策略、软件测试的分类、软件测试的主要方法进行广泛而深入的介绍     

作业场所粉尘危害程度不同方法的研究比较

采用密切值、秩和比，同时依据ＧＢ５８１７－８６分级标准对作业场所粉尘的危害程度进行了研究比较。结果表明：３种方法的Ｒｉｄｉｔ值分别为０．５２４５、０．４７５４、０．４９９９，经χ＾２检验３中方法之间差别无统计学意义（χ＾２＝０．３２８７）＇Ｐ＞０．０５）。