北京市道路交通事故致死率的分析与预测

根据北京市道路交通事故的统计资料,以交通事故致死率为指标,对北京市目前道路交通安全情况进行评价.并基于灰色预测理论,建立北京市道路交通事故死亡人数的灰色GM(1,1)预测模型.预测结果显示,北京市未来几年道路交通事故死亡人数虽处于下降趋势,但死亡人数仍在千人左右,平均每起事故死亡人数仍处于上升趋势,道路交通事故致死率仍然偏高.道路交通管理部门应加大惩治道路交通违法行为力度,以控制道路交通事故的严重后果.     

北京市道路交通事故灰色预测模型的构建与应用

利用灰色预测理论,根据北京市1999～2006年道路交通事故的统计资料,建立了北京市道路交通事故发生次数的灰色GM(1,1)预测模型,并根据实际数据的特点,对所建立的模型进行了改进.经证实,改进后的GM(1,1)模型预测精度高,预测结果与实际相符.GM(1,1)模型的预测结果可为北京市道路交通管理部门制定预防交通事故的措施提供重要的数据支持和理论依据,这对加强北京市的道路交通管理、改善北京市的道路交通安全状况将具有一定的现实意义.     

基于MATLAB的交通事故发生规律的模糊分布拟合

借助于数学软件MATLAB,运用模糊数学方法,探讨中国交通事故发生及危害的变化规律.首先,作中国交通事故统计数据的散点图,观察发现交通事故的变化同时具有线性增长总趋势和波动性特征,呈现出复杂的非线性组合特点;再对这些数据进行归一化处理,给出交通事故的模糊隶属函数;最后对交通数据进行分布拟合、组合回归处理,得到交通事故非线性模糊分布函数.结果表明,随着时间的变化,中国交通事故的死亡人数呈线性与正态叠加波动上升趋势,受伤人数呈线性与正弦叠加波动上升趋势,发生起数与损失折款都呈双峰正态分布波动趋势,2002年以来交通状况正在好转.得到的结果能客观地反映交通事故的现状分布和精确地刻划中国交通事故发生及危害变化规律,对于交通事故预测、综合治理具有重要的理论意义和应用价值,可供交通事故研究和有关部门制定交通安全管理决策作参考.     

基于M/M/1/K模型的车道占用对道路通行能力影响的分析与预测

依据一段实时路况监控视频,采用T型交叉平面口模型对事故所处横断面的实际通行能力进行建模.运用排队论的M/M/1/K模型对交通事故所影响路段的车辆排队长度与事故横断面实际通行能力、事故持续时间、路段上游车流量之间的关系进行建模,利用MATLAB软件与微元法思想进一步拟合与改进,并用模型预测出新交通条件下车辆排队至上游路口的时间,说明模型的有效性.     

中国交通事故损失的超越对数生产函数模型

根据中国交通事故的统计数据,借助于超越对数生产函数模型,定量探讨国内生产总值等社会因素对交通事故的直接损失的影响,首先根据中国在1979-2007年的有关交通事故的统计数据,利用相关分析找出影响直接财产损失的主要指标,分别为国内生产总值、全国总人口、民用汽车拥有量、机动车驾驶员数量,并以这些主要指标为投入,研究了该模型各种投入对交通事故的直接损失的产出弹性和替代弹性.借助于MATLAB和SPSS,建立超越对数生产函数模型.模型的理论值与真实值的平均相对误差为0.0849,预测2009年和2010年的交通事故直接财产损失分别为14.6亿元和16.9亿元.     

基于BP神经网络的道路交通事故预测

道路交通事故预测是交通研究的一个重要课题.以我国交通安全状况为研究对象,依据我国道路交通事故的特点,利用神经网络具有自学习、自组织、自适应能力特性,运用神经网络的方法及我国多个年度道路交通事故统计数据,建立了道路交通事故神经网络宏观预测模型,预测精度符合道路交通事故预测的要求.     

高速公路交通事故灰色Verhulst预测模型

在分析我国高速公路交通事故历史数据的基础上,引入灰色Verhulst预测理论,建立了高速公路交通事故灰色Verhulst预测模型.通过对2000～2007年我国高速公路交通事故死亡人数进行实例分析,发现灰色Verhulst模型的预测精度高于GM(1,1)模型.结果表明,灰色Verhulst模型的预测结果较好的反映了高速公路交通事故的发展趋势,该模型用于高速公路交通事故预测是可行的.     

基于判别分析的骑推行交通事故鉴定研究

道路交通事故鉴定对于交通事故责任认定和法庭举证具有重要作用.采集某市近三年125起交通事故案例数据,建立机动车、自行车、人体总计73个调查变量,运用SPSS软件开展骑推行事故鉴定研究.通过相关性分析发现车座、机动车类型等6个变量与鉴定结论相关.开展线性判别分析研究,结果表明直接对相关变量进行判别分析可以快速获得最佳判别效果.当函数选入的自变量为车座时,交叉验证准确率最高可达72.8%,说明采用数据挖掘的方法来鉴别交通事故中的行为方式具有一定可行性.     

基于最小二乘法的道路交通事故预测机理模型

基于相似理论提出一种新的道路交通事故预测方法,建立了新的交通事故预测非线性机理模型,作为道路交通事故预测的初步探讨.采用机动车保有量作为模型的输入变量,非线性最小二乘法求出模型参数.通过计算表明新预测模型预测精度较高,有应用价值,同时也为交通事故预测提出了新的预测理论.     

促进建模的探究性 培养思维的深刻性

以基于数据分析的回归模型建模活动为例,梳理思维型课堂的教学模式,包括创设情境与认知冲突、思维互动与自主构建、梳理归纳与思维监控、拓展提升与应用迁移四个教学的基本过程.以问题驱动学生思考,用计算机操作增强学生的动手能力,在小组合作研究中探寻模型检验和模型优化的方法,强化学生思维的深刻性.     

城市交通投资结构调整与交通需求互动优化模型研究

城市交通投资结构研究有助于了解交通供给能力特征,缓解城市交通拥堵状况.北京市在过去的十几年来大量的交通资金投入到道路建设中,但并没有从根本上扭转日益突出的交通供需矛盾状况.从交通投资与交通需求的关系出发,探讨投资方向和投资力度对交通供需结构的影响.给出交通投资结构阶段统计特征、分析了北京市"七五"至"十五"时期交通投资系统与交通需求、交通供给和社会经济系统关系.应用多目标优化方法,并运用M atlab软件计算不同交通结构情境下的交通投资取向、结构、管理上提出建议.     

A new multifractal network traffic model

In this paper, a new multifractal traffic model to capture the multifractal nature of modern Internet traffic was developed. Employing the algorithm of network traffic analysis (binomial inverse cascade process) to analyze the multifractal feature of traffic data and adopting the algorithm of network traffic synthesis (binomial cascade process) to model the network traffic, this approach gave an easy and efficient way to infer the model parameters from the measured traffic traces. Moreover, the traffic was simulated and analyzed using obtained parameters. It was found that the simulated traffic data were in a close fit to the real trace statistics. The analysis results showed that this model could capture the real network traffic very well.     

Analytical studies on the instabilities of heterogeneous intelligent traffic flow

It has been widely reported in literature that a small perturbation in traffic flow such as a sudden deceleration of a vehicle could lead to the formation of traffic jams without a clear bottleneck. These traffic jams are usually related to instabilities in traffic flow. The applications of intelligent traffic systems are a potential solution to reduce the amplitude or to eliminate the formation of such traffic instabilities. A lot of research has been conducted to theoretically study the effect of intelligent vehicles, for example adaptive cruise control vehicles, using either computer simulation or analytical method. However, most current analytical research has only applied to single class traffic flow. To this end, the main topic of this paper is to perform a linear stability analysis to find the stability threshold of heterogeneous traffic flow using microscopic models, particularly the effect of intelligent vehicles on heterogeneous (or multi-class) traffic flow instabilities. The analytical results will show how intelligent vehicle percentages affect the stability of multi-class traffic flow.     

大数据背景下基于5S的城市交通拥堵评价模型研究

近几年来,城市交通拥堵问题日益突出,极大制约了城市发展。在大数据背景下,为了准确掌握交通实时拥堵状况,改善城市交通,便利市民出行,本文深入挖掘城市交通拥堵的影响因素,构建了基于交通5S要素的城市拥堵理论模型,运用径向基函数神经网络方法工具,以上海静安寺、上海站、陆家嘴周围三大拥堵路段的交通数据集为例,验证了该模型的有效性。实验结果表明,由该模型获得的城市交通拥堵预测值与上海实际交通路况具有较好的拟合效果,表明交通5S模型与方法能够准确有效地评价城市交通拥堵。     

A stochastic and dynamic routing policy using branching processes with state dependent immigration

A stochastic and dynamic vehicle routing problem called the Dynamic Traveling Repairman Problem (DTRP) was introduced by Bertsimas and van Ryzin. Several routing policies were analyzed in light traffic and in heavy traffic conditions. But, the good light traffic policies become very quickly unstable with increasing traffic intensity, and the good heavy traffic policies are inefficient in light traffic conditions. In this paper, a new routing policy is defined and analyzed, using results from branching processes with state dependent immigration. This policy not only performs optimally in light traffic, but also performs very well in heavy traffic. This is important to the designer of a service system because the traffic conditions may be variable and/or be unpredictable, and having to switch routing policies could prove to be costly and difficult to implement.     

Payne-Whitham型宏观交通流模型波动特性

宏观交通流模型将交通流比拟成流体流,通过整体变量如交通流量、平均车速以及交通密度来研究其整体性质,得到了越来越多的肯定.文章采用波前展开的方法,研究Payne-Whitham型宏观交通流模型描述扰动沿交通流波动的特性,同时给出了相应的稳定性条件.最后利用Padé逼近法进行数值仿真,得到的结果与理论分析相一致.     

基于季节ARIMA模型的GSM话务量建模和预报

本文用季节模型对天津移动GSM网的话务量进行了建模分析和预报,研究表明用季节模型对移动话务量进行建模分析和预报是可行的,同时在文中我们还给出了带两个周期季ARIMA模型的一般表达式,并用这种带多个周期的模型对实际的网络业务进行了建模和预报。     

Applying a recurrence plot scheme to analyze non-stationary transition patterns of IP-network traffic

This paper describes a recurrence plot (RP) approach to the analysis of non-stationary transition patterns of IP-network traffic. To get a quantitative measure of dynamical transition patterns of IP-network traffic, we used the values of determinism (DET) defined by the recurrence quantification analysis (RQA). Also, when evaluating the fractal-based properties of IP-network traffic, we focused on two parameters: (i) the long-range dependence (LRD)-related scaling parameter α derived from the detrended fluctuation analysis (DFA) and (ii) the range of the generalized fractal dimension. In applying this method to traffic analysis, we performed two kinds of traffic measurement in Tokyo, Japan, and derived the values of DET and fractal-based parameters of IP-network traffic over time. In checking the measured network traffic, we found that the characteristic with respect to DET and self-similarity seen in the measured network traffic fluctuated over time, with different time-variation patterns for two measurement locations. Results also confirmed that a larger value of DET or accumulated DET reflected increases in the degree of LRD of IP-network traffic and that the accumulated DET reflected the decreases in the degree of multi-fractality of IP-network traffic. As a result, we confirmed that RP-based measures can be effective for evaluating the non-stationary transition patterns of IP-network traffic in terms of quantitative fractal-based properties.     

Spatial distribution complexities of traffic congestion and bottlenecks in different network topologies

Recently, urban traffic congestion has become a popular social problem. The generation and the propagation of congestion has close relation with the network topology, the traffic flow, etc. In this study, based on the traffic flow propagation method, we investigate the time and space distribution characteristics of the traffic congestion and bottlenecks in different network topologies (e.g., small world, random and regular network). The simulation results show that the random network is an optimal traffic structure, in which the traffic congestion is smaller than others. Moreover, the regular network is the worst topology which is prone to be congested. Additionally, we also prove the effects of network with community structure on the traffic system and congestion bottlenecks including its generation, propagation and time–space complexities. Results indicate that the strong community structure can improve the network performance and is effective to resist the propagation of the traffic congestion.