降雨量对OD 行程时间可靠性影响分析

为详细研究降雨量对OD行程时间可靠性的影响，基于Uber 出行共享的3 年美国波士顿10 对OD行程时间数据及WeatherUnderground 网站提供的小时历史天气，构建了OD行程时间高斯混合模型(GMM). 模型参数利用EM法进行求解，K 值根据K-S 检验后的P 值 (大于0.500 0)进行确定，模型分位数利用二分法进行求解. 提出一种基于缓冲指数(BI)的新指标——缓冲指数变化率(BIVR)作为定量评估指标. 结果表明：降雨会降低总体OD行程时间可靠性，降低效果随降雨量提高而增强，但增强效果并不明显；尽管可能性较低，但当降雨处于次要影响因素时可能提高可靠性；小雨天气可视为正常天气；雨天可靠性显著低于正常天气，居民在雨天(除小雨外)出行应预留更多时间.     

Rice公式外推桥梁荷载效应

在当今,如何对桥梁进行合理评估已成为桥梁工作者的一个重要课题。而在桥梁评估过程中,科研工作者往往需要定量描述评估周期内某座桥梁的交通车辆荷载大小,一般用荷载效应描述。就此根据动态称重系统实测的107国道路段交通荷载数据产生的模拟车流对评估周期内荷载效应极值的计算方法进行了初步研究,以各种跨径简支梁为例,利用Rice外推理论外推各个观测周期的荷载效应极值,并以此作为建立评估荷载模型的基础。     

A nested recursive logit model for route choice analysis

We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value functions are the solution to a system of non-linear equations. We propose an iterative method with dynamic accuracy that allows to efficiently solve these systems.We report estimation results and a cross-validation study for a real network. The results show that the NRL model yields sensible parameter estimates and the fit is significantly better than the RL model. Moreover, the NRL model outperforms the RL model in terms of prediction.     

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.     

基于高斯曲率识别板式无砟轨道中CA砂浆脱空伤损

板式无砟轨道中CA砂浆在列车荷载、环境温度等多种作用下容易产生脱空等伤损,准确检测出这些伤损显得尤为重要。针对CA砂浆的伤损,建立轨道板-CA砂浆模型,利用有限元软件对系统进行模态分析,通过计算分析得到轨道板的曲率模态,结合高斯曲率确定CA砂浆的伤损及伤损位置。计算结果表明：轨道板的前五阶高斯曲率可以反映伤损的有无及其具体位置,一阶高斯曲率最为明显。轨道板-CA砂浆系统一阶高斯曲率不仅可以准确识别单处CA砂浆伤损,还可以准确识别多处CA砂浆伤损。     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

基于浮动车速度波动特征的交通状态识别

为实现路段交通状态的准确判别,解决单参数无法直接识别道路交通状态问题,本文利用高频浮动车速度数据,使用灰度共生矩阵特征值对比度和逆方差表示车辆行驶的波动特征。基于城市道路交通状态变化的动态性与连续性,围绕固定时间窗口内车辆的平均车速、对比度和逆方差,采用FCM (Fuzzy c-means)算法进行聚类分析,得到畅通、平稳、拥挤和阻塞这4种状态阈值。提出基于多维高斯隐马尔可夫模型的交通状态识别方法,分别以3,5,6 min固定时间窗口训练模型。模型状态转移矩阵表明,时间窗口越小其保持原有交通状态的可能性越大,时间窗口越大交通状态突变的可能性越大。使用不同序列长度对比3种时间窗口在测试集中的识别精度,结果表明,随着序列长度的变化,精度显示出先升高后降低的趋势,且固定时间窗口越大,不同序列长度的识别精度变化越均匀。最后利用5 min固定时间窗口划分数据使用本文方法和支持向量机以及随机森林分别进行道路交通状态识别,综合精度分别为92.00%、84.89%、88.48%,同时本文方法在查准率、召回率和F1度量(F1-score)指标均优于其他两个模型,说明道路车速的波动特征可以很好地反映道路交...     

基于高斯混合分布的交通拥堵评价模型

为了客观、有效的评价交通拥堵的程度,采用速度指标作为反映交通流状态特征的关键变量,针对交通流速度变量存在混合分布的特点,建立了基于高斯混合分布的交通拥堵评价模型,并将EM算法运用于模型求解,通过对速度变量的聚类分析,以判别交通状态,评价交通拥堵程度.上海市快速高架路实测数据应用模型分析的结果表明:模型可以客观分析交通流...     

交通网络两个相邻结点之间有流量约束的最大流分配算法

交通网络最大流的分配是基于容量限制条件和流量守恒条件进行的,但在实际应用中,往往对交通网络中两个相邻结点之间的流量有具体的要求和约束限制。本文对交通网络中两个相邻结点之间的流量约束问题进行了分析,基于寻找增流链的算法,构造了带有上限或下限三种流量约束限制条件下的最大流分配算法,这些算法可以为解决实际的交通问题提供一定的应用基础．