Potential Application of iRap for Road Safety Assessment under Mixed Traffic Condition Case Study: Le Van Viet Street in Ho Chi Minh City

Road transport safety policies have emphasized road infrastructure safety design and engineering as a core function.However,in developing countries like Vietnam,this approach has been slower to adopt,resulting in substandard roads.In-depth studies of accident locations indicate that road environment factors contribute significantly to road accidents in Vietnam and road design features are associated with specific accident types and hazards.Proactive and reactive approaches,such as road safety audit,inspection,assessment,and treatment of hazardous locations,are necessary to ensure that the road and its environment are safe.This paper provides an overview of road safety in Vietnam in general,and Ho Chi Minh in particular,including its factors and characteristics,as well as road infrastructure safety improvements.The iRap tool for road safety inspection and assessment is highlighted as a potential method for systematically analyzing road infrastructure deficiencies and providing targeted countermeasures to improve road safety under mixed traffic conditions.

Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.

Traffic condition estimation with pre-selection space time model

A pre-selection space time model was proposed to estimate the traffic condition at poor-data-detector,especially non-detector locations.The space time model is better to integrate the spatial and temporal information comprehensibly.Firstly,the influencing factors of the "cause nodes" were studied,and then the pre-selection "cause nodes" procedure which utilizes the Pearson correlation coefficient to evaluate the relevancy of the traffic data was introduced.Finally,only the most relevant data were collected to compose the space time model.The experimental results with the actual data demonstrate that the model performs better than other three models.

Cell transmission model based traffic signal timing in oversaturated conditions

In order to investigate enhancements to cell transmission model (CTM) as a tool for traffic signal timing in oversaturated conditions, randomly distributed saturation flow rates and arrival rates were used instead of constant values to simulate traffic flow movement, estimate the average delay of the network and search for an optimal traffic signal timing plan. A case study was given to demonstrate that the proposed methodology can capture unique phenomena in oversaturated conditions such as forward wave, spillback and lane entrance blockage. The results show that CTM underestimates travel time by 25% when compared to Simtraffic, while the enhanced CTM underestimates by only 3%. A second case study shows that a dynamic signal timing plan is superior to a fixed signal timing plan in the term of average delay.

Improving Traffic Conditions by Using Neural Networks

The paper promotes the implementation of a neural network approach to improve one of the most transcendent traffic conditions: the mobility of the cars in any particular junction. Neural networks have proven to be an effective paradigm of modern computing, providing extensive benefits in a wide range of applications. In this sense, the paper uses a BPNN (backpropagation neural network) model. The three input nodes are related to: n1: the amount of cars in the road; n2: the green light interval; and n3:the distance (taking into account the quantity of cars) between the first car in the intersection and the last car in the longest line in front of it. In particular, the paper promotes that each traffic light signal will be capable of offering a new green light interval according to the requirement and constrains of the vitality, ensuring a vehicular mobility level greater than 65%. To assess this idea, the paper presents two experiments confronting the real world data versus experimental results. For example, in the first experiment, the BPNN improves the performance of the real data about vehicular mobility in almost 30%. Finally, some conclusions and future work are presented.

Traffic Conflict Technique Development for Traffic Safety Evaluation under Mixed Traffic Conditions of Developing Countries

In developing countries, the numbers of traffic accidents, injuries and fatalities are very high and tend to increase atsignalized intersections. For example, in Ho Chi Minh City （HCMC） of Vietnam, the number of accidents at signalized intersectionsaccounted for 45% of the total accidents at all the intersections. This fact leads to strong necessity for analyzing traffic safety atsignalized intersections. Nevertheless, the historical accident data in HCMC is not available for deep analysis, this study uses videocameras to capture and analyze conflicts that potentially lead to accidents using TCT （traffic conflict technique）. Conflict severityidentification is one of the most significant steps to evaluate traffic safety at signalized intersections using TCT. Six zones （seriousconflict, common conflict, non-conflict, highest potential serious conflict, potential serious conflict, and potential common conflict） areexplored in this study to clarify conflict severity. This result is based on being the cut off value between serious conflicts and commonconflicts, according to 85% cumulative frequency of TTC （time to collision） and CS （conflict speed） under 3,050 samples size whichwere observed at 10 signalized intersections during August-November, 2014. Such a deep understanding is a scientific basis to studyhow to apply TCT to evaluate traffic safety at signalized intersections under mixed traffic conditions.

Countermeasures for traffic accidents due to road conditions in China

Regarding the postulate of traffic infrastructure and vehicles, much attention should be given to the effect of road conditions on accidents. With large numbers of traffic accidents on Shenda Freeway, Liaoning Province, Harbin City and others in P. R. China, parameters and the effect of accidents caused by horizontal alignment, vertical alignment, cross section and intersection are studied systematically The disciplinary analysis of these effects are presented in this paper. The viewpoint is acknowledged that high sub grade and steep slopes are against traffic safety, which is common and ignored in high-usage highways in China. Design parameters of the current design criteria and the corresponding countermeasures are suggested for safety on our highways.

Effects of Periodic Boundary Condition on Traffic Waves

在这份报纸，我们使用江等建议的速度坡度模型。[Transp。物件。B 36 (2002 ) 405 ] 飘动在吃惊和变成稀薄上学习边界状况的效果。我们的数字结果证明这个模型能复制二个交通波浪的进化，它进一步证明这个模型能被用来完美地探索各种各样的边界条件引起的后果。

A novel conditional cell transmission model for oversaturated arterials

The objective of this work is to develop a novel feature for traffic flow models,when traffic queues on two-way arterials periodically extend until then they block an upstream signal in oversaturated conditions.The new model,proposed as conditional cell transmission model(CCTM) has been developed with two improvements.First,cell transmission model(CTM) is expanded for two-way arterials by taking account of all diverging and merging activities at intersections.Second,a conditional cell is added to simulate periodic spillback and blockages at an intersection.The results of experiments for a multilane,two-way,three-signal sample network demonstrate that CCTM can accommodate various traffic demands and accurate representation of blockages at intersections.The delay of left turns is underestimated by 40 % in moderate conditions and by 58% in oversaturated condition when using the CTM rather than CCTM.Finally,the consistency between HCS 2000 and CCTM shows that CCTM is a reliable methodology of modeling traffic flow in oversaturated condition.

An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.

基于生成对抗网络的路况监控视频能见度检测方法

传统路况监控视频能见度检测方法直接对人眼可分辨最远点进行检测,未进行车道线检测及兴趣域提取,造成传统方法检测效果较差。对此,提出基于生成对抗网络的路况监控视频能见度检测方法。在车道线检测及兴趣域提取的基础上,基于生成对抗网络检测人眼可分辨最远点,进一步与计算机视觉和图形领域相结合,实现路况监控视频能见度检测。实验结果表明,该方法具有更好的检测效果,可推广应用。

^(99)Tc在模拟地质条件下的迁移行为研究概况

介绍了^(99)Tc在模拟地质条件下迁移行为研究概况,主要阐明了^(99)Tc在模拟地质条件下迁移行为的研究意义、^(99)Tc的水溶液化学性质、影响^(99)Tc在地质介质中迁移的因素及^(99)Tc在近场和远场的释放和迁移,并就国内外的研究概况进行了概要评述。

纤维素降解菌哈茨木霉TC10-13产酶条件优化

【目的】优化纤维素降解菌哈茨木霉TC10-13产酶条件,为该菌株在烟草中的应用提供技术支持。【方法】通过单因素试验和正交试验对TC10-13菌株发酵条件进行优化,根据纤维素酶活力大小确定初始p H、最佳碳源、氮源、温度、碳氮源浓度及表面活性剂浓度。【结果】TC10-13菌株产纤维素酶的最佳初始p H为5.0,最佳碳源为秸秆,最佳氮源为酵母浸膏;最优正交组合为秸秆15.0 g/L,酵母浸膏2.0 g/L,表面活性剂2.0 m L/L,温度28℃;在最佳产酶条件下,TC10-13菌株的CMCase和FPase活力分别为15.97和6.38 U/m L。将TC10-13菌株发酵液应用到烟丝后,烟气香气量和浓度分值有所增加,但香气质及刺激性等指标无明显变化。【结论】TC10-13菌株有提升烟草品质的潜质,具有较好的应用前景。

基于工况序列寻优的列车节能操纵策略优化

传统“三阶段”“四阶段”工况序列的列车操纵模式难以满足城市轨道交通复杂线路条件下的列车节能运行需求,本文提出一种基于工况序列寻优的列车节能操纵策略优化方法。将物理区间离散为多个等距离子区间,建立工况序列与子区间之间的映射关系,以区间总牵引能耗和运行时间最小化为目标构建列车节能操纵策略多目标优化模型。为提高模型求解效率,改进非支配排序遗传算法的交叉算子和距离算子,通过离散仿真求解列车节能操纵策略集。以福州地铁1号线的两个典型区间为对象进行案例分析,结果表明,相比传统操纵模式,优化后列车牵引能耗平均降低约19%。本文方法通过合理选择算法参数能够有效构建适应不同线路条件的运行工况序列,生成秒级运行时间划分下的列车节能操纵策略集。

SMS 受体显像剂^(99)Tc^m-DTPA-octreotide 的研制──Ⅰ . 标记条件研究

研究ｐＨ、ＤＴＰＡ－ｏｃｔｒｅｏｔｉｄｅ与ＳｎＣｌ２·２Ｈ２Ｏ摩尔比、反应温度、反应时间、反应物浓度等条件对标记产物的影响。实验表明９９Ｔｃｍ标记ＤＴＰＡ－ｏｃｔｒｅｏｔｉｄｅ时，其标记率可达到９０％；纯化后，可以用作下一步肿瘤ＳＭＳ受体显像。建立了９９Ｔｃｍ－ＤＴＰＡ－ｏｃｔｒｅｏｔｉｄｅ放化纯度纸色层分析方法和反相ＨＰＬＣ法，这二种方法能够快速、准确地测定标记物的放射化学纯度。另外，ＨＰＬＣ分析表明，标记物为几种络合物的混合物，在不同的标记条件下，络合物的种类、相互比例不同。

Linux流控工具TC的原理及实用案例分析

Linux系统下的开源软件TC是一个功能强大而且性能优越的流量控制工具,可以媲美专业的流控设备,本文介绍TC的主要组件的概念、用途、组件相互之间的关系和结构以及它们是如何协同工作的,然后使用实例的方式讲解一个方案的设计过程,通过该实例可以了解TC高度的灵活性以及强大的功能。

顾及上下文语境的交通命名实体自动识别方法

网络文本中蕴含着大量的交通信息,对交通命名实体识别是地情变化监测、交通实体更新的重要前提。针对交通命名实体识别任务中缺少专业标注数据和有效识别方法导致识别效果无法满足需求的问题,文中提出一种融合深度学习模型的交通命名实体自动提取方法。该方法首先利用语言表征模型充分融合语境来提取文本特征,然后通过结合双向神经网络模型来学习上下文语境,最终由条件随机场模型对输出进行约束得到全局最优标记序列实现交通实体的识别。实验证明,该方法准确率可以达到90%以上,能够实现对交通实体的有效识别。

京昆高速(山西段)交通事故特征及其与气象条件的关系

气象条件会对高速公路行车安全带来影响,尤其是在恶劣的天气条件下。以京昆高速(山西段)为研究对象,分析交通事故时空分布特征,探究事故发生数量与气象条件的关系。结果表明:(1)除晴朗天气条件外,多云和降雨对应的交通事故数量最多,降雪和起雾对应的交通事故数量相对较少。(2)春、秋季是京昆高速(山西段)交通事故发生的高峰时段,夏季次之。(3)京昆高速(山西段)交通事故的发生以太原市和临汾市最多,晋中市、阳泉市和运城市发生的交通事故数量次之,吕梁市发生的交通事故数量最少。(4)春季空气湿度较大或者大风天气条件下容易发生交通事故,夏季和秋季在降水天气条件下容易发生交通事故,冬季降雪(雨)、大风及能见度低时交通事故最容易发生。

交通出行方式改变对构建乡村生活圈的影响探讨

随着城镇化的不断推进,在我国广大乡村地区出现了一系列人口、产业等方面的变化,农村生产、生活发生了巨大的变革,其中一个非常重要的方面是农村交通条件的改善提升带来的农村出行方式的转变。文章从对农村交通现状特征及问题的梳理和分析入手,进行乡村居民出行特征与乡村生活圈构建五个方面的影响关系的探讨,寻找农村生活圈可持续发展之路。文章认为现代化乡村交通基础设施深刻改变了农村的生产、生活方式,交通条件的全面改善将为乡村生活圈的构建及乡村发展带来深远影响。

基于多源数据融合的改进HMM拥堵评估模型

针对交通流复杂的时空关联性以及自身的不确定性,为了准确评估路网通行能力并缓解交通拥堵问题,提出融合多源数据的改进隐马尔科夫模型,对交通拥堵态势进行评估。首先,引入多源数据观察特征,获得道路特征状态变量;然后,确定道路的状态参数,将交通流划分为4个状态;最后,使用改进韦尔奇算法考虑前n个时刻的历史数据,对隐马尔可夫模型进行参数估计和状态推断,获得改进后的模型。以深圳市某路段所在片区为例,对模型的有效性、适用性进行验证。结果表明:该方法准确性达到97.1%,相较于原始模型提高了4.7%,能对路网状态进行有效评估。随着采样频率的改变,改进后的模型与基准模型最低准确率分别相差9.8%、9.4%、9.7%。