结合概率搜索定界的入度统计最短路径算法

Dijkstra 经典最短路径算法包括大量的排序运算,且需要对图中所有顶点进行计算,效率较低.本文针对有向网络,提出了与概率搜索定界结合的入度统计最短路径算法.该算法通过按概率搜索得到一条较短路径,依据路径长度和有向网络结构特征确定和顶点序号相关的节点阻抗最大值;采用入度统计算法代替经典的标号算法,在计算过程中根据节点阻抗最大值,采取一定方式剔除无效顶点(不在最短路径内的顶点),简化网络结构.本文提出的算法不需要进行排序运算,简化了运算过程,并且可以剔除大量的无效顶点,降低了网络复杂度.算例分析表明,相对于Dijkstra算法,结合概率搜索定界的入度统计算法大幅度提高了运算效率,具有实用性.     

交通流非线性回归模型的变点统计分析

以往的交通流状态辨识主要集中在交通流状态预辨识（即交通流预测）和实时辨识（即事件检测或交通流突变检测）上,在一定程度上忽略了对交通流变量规律的辨认。依据交通流理论,采用非线性回归模型变点的统计方法,对变点识别的关键问题进行了分析。结合淄博市中心路的实地数据对本方法进行了标定和检验,验证了方法的有效性和可行性。     

一种新的循环谱统计量频谱感知算法

为了提高认知无线电中感知用户对授权用户（主用户）的检测概率,提出一种新的基于循环谱统计量的主用户信号检测算法．该算法利用循环谱离散频域平滑方法,对信号的循环谱在循环频率处建立合理的检测统计量,然后求解得到对应不同虚警概率的判决门限,以使感知用户能检测信噪比更低的授权用户信号．该算法具有检测结构简单,易实现的特点．仿真结果表明,在高斯白噪声信道中,虚警概率为0．1,频域平滑累积次数为255,在信噪比为-16dB的条件下,与Dandawate-Giannakis（DG）方法相比,本文算法的检测概率提高23％．     

货运道路平面交叉口车速特性分析

货运道路车型构成复杂,不同车型的车速分布离散,造成追尾事故频发。在实地调查的基础上,结合一定的数理统计方法,对货运道路平面交叉口不同车型的车速分布特征进行分析,其结果可为交叉口的改善提供依据。     

数理统计在工程质量控制中的应用

在实际工程中,通常通过取样来实现质量控制。取样得出的数据能否反映实体质量的情况,必须用数理统计的办法来检验。应用数理统计理论对样本数量、试验数据进行分析,提出理论计算方法。     

基于地铁施工安全事故统计规律的致险因素关系研究

地铁施工中作业环境复杂、风险因素多样,致使地铁施工事故频发.文章通过统计2001年 —2019年的国内地铁施工事故案例,分析事故特征和发生规律.在此基础上,针对不同事故类型采用鱼骨图分析法辨识出潜在致险因素,并研究致险因素之间的关系.     

2019年城市轨道交通线路统计分析

截至2019年年末,中国大陆地区共40个城市开通城轨交通运营线路,里程共计6 736.2 km,当年新增运营线路长度974.8 km,全年累计完成客运量237.1亿人次。56个城市当年完成建设投资5 958.9亿元,在建线路总长6 902.5 km。65个城市的城轨线网规划获批,扣除已运营长度,获批规划线路总长7 339.4 km。进入“十三五”4年来,共有27个城市、投资额合计约25 000亿元的新一轮建设规划或规划调整获批;完成建设投资19 992.7亿元;新增运营线路长度为3 118.2 km。规划、建设、运营线路规模和投资额稳步增长,城轨交通继续保持快速发展态势。     

Minimum sample size for extreme value statistics of flow-induced response

Extreme value statistics are one way to determine the maximum design loads for systems in extreme conditions, such as operational loads experienced by ships. Accurate predictions typically require large sample sizes, which are not always possible to obtain. Conversely, small sample sizes lead to more variation in the predictions. Increasing the sample size improves the variance to a desired range. The proposed method aimed to estimate a minimum sample size for an extreme value process by specifying and obtaining an acceptable variance. Minimum sample sizes for extreme value statistics depend on the distribution's behavior, so the method proposed here was designed for use before and during measurements. To test the proposed method, the response of a cantilever fin with a varying angle of attack was measured. The proposed method was able to estimate minimum sample sizes for several distributions. Accuracy was demonstrated by randomly drawing measured and simulated samples.     

车辆荷载特性影响下的碰撞时间分布规律

受车辆荷载特性影响的碰撞时间(Time-To-Collision, TTC)，被认为是车辆避撞系统中跟驰过程风险评估的有效指标.本文以车辆类型、超重和超速指标量化表征车辆荷载特性，分解前后车辆不同荷载特性组合的12类跟驰场景.基于动态称重技术获取融合荷载特性的交通流数据，分析车辆荷载特性对自由流交通状态下12类跟驰场景TTC分布的影响，利用KS 检验对比TTC分布的显著性.结果表明：TTC累计频率分布服从指数模型，在5%置信度水平上，跟驰前后车的车辆类型对TTC分布无显著影响；前后车均为超重轻车显著增加了潜在冲突风险，超重增加了轻车跟驰重车，轻车跟驰轻车场景的潜在冲突风险；前后车不超速跟驰场景下，轻车跟驰重车的风险比例高于轻车跟驰轻车.     

Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice

In this paper, we review both the fundamentals and the expansion of computational Bayesian econometrics and statistics applied to transportation modeling problems in road safety analysis and travel behavior. Whereas for analyzing accident risk in transportation networks there has been a significant increase in the application of hierarchical Bayes methods, in transportation choice modeling, the use of Bayes estimators is rather scarce. We thus provide a general discussion of the benefits of using Bayesian Markov chain Monte Carlo methods to simulate answers to the problems of point and interval estimation and forecasting, including the use of the simulated posterior for building predictive distributions and constructing credible intervals for measures such as the value of time. Although there is the general idea that going Bayesian is just another way of finding an equivalent to frequentist results, in practice Bayes estimators have the potential of outperforming frequentist estimators and, at the same time, may offer more information. Additionally, Bayesian inference is particularly interesting for small samples and weakly identified models.