Nonlinear analysis of traffic jams in an anisotropic continuum model

This paper presents our study of the nonlinear stability of a new anisotropic continuum traffic flow model in which the dimensionless parameter or anisotropic factor controls the non-isotropic character and diffusive influence. In order to establish traff^c flow stability criterion or to know the critical parameters that lead, on one hand, to a stable response to perturbations or disturbances or, on the other hand, to an unstable response and therefore to a possible congestion, a nonlinear stability criterion is derived by using a wavefront expansion technique. The stability criterion is illustrated by numerical results using the finite difference method for two different values of anisotropic parameter. It is also been observed that the newly derived stability results are consistent with previously reported results obtained using approximate linearisation methods. Moreover, the stability criterion derived in this paper can provide more refined information from the perspective of the capability to reproduce nonlinear traffic flow behaviors observed in real traffic than previously established methodologies.

Recognition Method for Change Point of Traffic Flow Linear Regressions

Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the field data of Liantong Road of Zibo city to verify the validity and the feasibility of the theory. The results show that change point method of multiple linear regression can make out the rule of quantitative changes in traffic flow more accurately than ordinary methods. So, the change point method can be applied to traffic information management system more effectively.

Evaluating Traffic Congestion Using the Traffic Occupancy and Speed Distribution Relationship: An Application of Bayesian Dirichlet Process Mixtures of Generalized Linear Model

Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.

A Multipath Routing Algorithm Based on Traffic Prediction in Wireless Mesh Networks

The technology of QoS routing has become a great challenge in Wireless Mesh Networks (WMNs). There exist a lot of literatures on QoS routing in WMNs, but the current algorithms have some deficiencies, such as high complexity, poor scalability and flexibility. To solve the problems above, a multipath routing algorithm based on traffic prediction (MRATP) is proposed in WMNs. MRATP consists of three modules including an algo-rithm on multipath routing built, a congestion discovery mechanism based on wavelet-neural network and a load balancing algorithm via multipath. Simulation results show that MRATP has some characteristics, such as better scalability, flexibility and robustness. Compared with the current algorithms, MRATP has higher success ratio, lower end to end delay and overhead. So MRATP can guarantee the end to end QoS of WMNs.

异质交通流环境下无信号T形交叉口控制策略

针对无信号控制T形交叉口,提出一种异质交通流环境下交叉口的分级限速控制模型.根据异质交通流的车辆运行特性提出分级限速的概念,并建立交叉口的车辆信息矩阵.构建以车流通过冲突区域时间最短为优化目标,以保证冲突车辆安全行驶的时间间隔为约束条件的线性规划模型.通过MATLAB搭建仿真平台对所提出的模型有效性进行试验验证.结果表明:在混合行驶的交通环境下,当车辆到达率为0.30~0.50时,本研究所提模型可以在传统控制模型的基础上提升交叉口通行能力约20%;分级限速控制模型下智能网联车辆渗透率达到0.90时,最多可以增加交叉口约17%的通行能力;所提模型能有效提高道路资源利用效率,保证主路运行通畅的情况下大幅降低支路车辆延误.

Road traffic congestion measurement considering impacts on travelers

The article intends to find a method to quantify traffic congestion＇s impacts on travelers to help transportation planners and policy decision makers well understand congestion situations. Three new congestion indicators, including transportation environment satisfaction （TES）, travel time satisfaction （TTS）, and traffic congestion frequency and feeling （TCFF）, are defined to estimate urban traffic congestion based on travelers＇ feelings. Data of travelers＇ attitude about congestion and trip information were collected from a survey in Shanghai, China. Based on the survey data, we estimated the value of the three indi- cators. Then, the principal components analysis was used to derive a small number of linear combinations of a set of variables to estimate the whole congestion status. A linear regression model was used to find out the significant variables which impact respondents＇ feelings. Two ordered logit models were used to select significant variables of TES and TTS. Attitudinal factor variables were also used in these models. The results show that attitudinal factor variables and cluster category variables are as important as sociodemographic variables in the models. Using the three congestion indicators, the government can collect travelers＇ feeling about traffic congestion and estimate the transportation policy that might be applied to cope with traffic congestion.

Effect of multi-velocity-difference in traffic flow

Based on the optimal velocity models, an extended model is proposed, in which multi-veloclty-dllterence aheacl is taken into consideration. The damping effect of the multi-velocity-difference ahead has been investigated by means of analytical and numerical methods. Results indicate that the multi-velocity-difference leads to the enhancement of stability of traffic flow, suppression of the emergence of traffic jamming, and reduction of the energy consumption.

公路隧道入口区域驾驶人吸睛效应综合评价

为全面和系统评价公路隧道入口区域视觉吸引对驾驶人吸睛效应视觉表现的影响,本文招募30名被试开展自然驾驶实验,采集驾驶人在公路隧道入口区域不同视觉吸引条件下的眼动数据,基于因子分析法选取驾驶人吸睛效应敏感评价指标,并分别构建线性混合效应模型和数据包络分析模型,识别和探究公路隧道入口区域视觉吸引对驾驶人吸睛效应视觉表现的影响特征和作用机理。结果表明:因子分析结果显示,驾驶人吸睛效应敏感视觉指标为注视持续时间、瞳孔直径、扫视持续时间和扫视幅度;公路隧道入口区域不同视觉吸引条件对驾驶人吸睛效应的视觉表现和综合效率有显著影响,且受驾驶人年龄和驾驶经验的个体特质因素影响显著,而性别因素对其没有显著性影响;视觉吸引的存在均会不同程度地损害驾驶人的正常视觉绩效,降低其合理有效性;提示标语视觉吸引条件下,驾驶人的视觉注意水平最差,视觉认知负荷程度最高,吸睛效应的负面影响最大,广告牌条件次之。

An Interval Programming-based Traffic Planning Model for Urban Vehicle Emissions Management

An interval linear traffic planning model is developed for supporting vehicle emissions limited under uncertainty. The interval linear traffic planning model can address uncertainties of traffic system and vehicle emissions related to system costs and limitation of emission. The interval linear traffic planning model is applicable to complex traffic system. One virtual city as our study object was taken by using the interval linear traffic planning model. In this study, one virtual case and a scenario are provided for three planning periods. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.

A Real-Time Urban Traffic Detection Algorithm Based on Spatio-Temporal OD Matrix in Vehicular Sensor Network

Currently, there are kinds of algorithms in order to detect real-time urban traffic condition. Most of these approaches consider speed of vehicles as a main metric to describe traffic situation. In this paper, we find out two important observations through several experiments. (1) In urban city, the speed of vehicles is influenced significantly by some factors such as traffic lights delay and road condition. The actual situation rarely satisfy hypothesis required for these solutions. Therefore, these traditional algorithms do not work well in practical environment. (2) Traffic volume on a road segment shows strong pattern and changes smoothly at adjacent time. This feature of traffic volume inspires us to define a metric: traffic-rate, which is used to detect traffic condition in real time. In our solution, we develop a novel traffic-detection algorithm based on original- destination (OD) matrix. We illustrate our approach and measure its performance in real environment. The performance evaluations confirm the effectiveness of our algorithm.

On use of the alpha stable self-similar stochastic process to model aggregated VBR video traffic

The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.

城市路内违章停车时空特征及影响因素作用模型

为揭示城市路内违章停车的时空模式及影响机理,结合持续两个月共10396条电子警察违停抓拍数据,从土地利用、停车供给与道路设计这3个维度选取16个影响因素进行分析。利用标准椭圆方法、层次聚类法及皮尔逊相关性检验等方法,分析路内违停时空特征并进行影响因素筛选,进一步根据因变量特征构建贝叶斯分位数回归模型,解析早晚高峰违停数量与影响因素的关系。研究结果表明:从违停数量的时间特征来看,工作日违停数量明显高于周末违停数量,且主要集中在周中,工作日违停数量按小时聚类结果显示,违停数量分为3个时间模式,分别为早晚高峰时间段(8:00-10:00和17:00-19:00)、早晚高峰汇聚时间段(10:00-11:00和15:00-17:00)与其他时间段;从违停数量的空间特征来看,工作日违停数量的整体空间分布主要集中在商业和居住密度较高的区域,其在一天内的空间变化趋势呈现“扩撒-稳定-收缩”的态势;分位数回归结果显示,土地使用、停车供给及道路设计这3个维度因素对路内违停存在非线性影响与阈值效应,且早晚高峰违停产生机制具有差异,路内违停治理需要考虑时空特征进行差异化治理。

山地城市公交服务感知与出行幸福感关系研究——基于获得感的调节作用

将幸福感理论与城市公共交通出行相结合,基于结构方程模型,实证检验山地城市居民公交服务质量感知与出行幸福感之间的非线性关系,以及公交资源获得感在两者之间的调节效应。结果表明:1)公交服务质量感知与出行幸福感之间存在非线性关系,且出行幸福感呈现出边际递减特征;2)山地城市居民公交资源获得感在公交服务质量感知与出行幸福感之间具有显著负向调节效应,公交服务质量感知对出行幸福感的正向影响随公交资源获得感的增强而减弱;3)山地城市公交资源获得感对出行幸福感具有正向显著影响。研究结论可为促进城市公交“满意度-获得感-幸福感”的制度改革提供启发,利于提高山地城市居民公交出行幸福感。

考虑异质交通流的随机参数优化速度跟驰模型

为分析交通流异质性对车辆跟驰行为的影响,基于随机参数线性回归方法改进优化速度函数.根据分位数回归对交通流速度-密度数据进行分类,对每个类别数据进行随机参数线性回归,并得到不同类别的改进优化速度函数与假设检验结果,结合改进的优化速度函数和全速度差跟驰模型建立随机优化速度跟驰模型,利用傅里叶变化理论对跟驰模型进行稳定性分析,并搭建环形车道仿真平台对跟驰模型进行数值实验.结果表明,分类处理后的随机参数模型误差较未分类降低28%;随机参数跟驰车队的速度值随着0.5分位点车辆的增多而增大;随机参数跟驰模型车队较固定参数跟驰模型车队更能反映交通流异质性对车队的影响.建立的模型能够提高仿真维度,真实反映交通流的复杂运行状况.

优化遗传算法在天然气管网流量分配过程模拟计算中的应用

在天然气管网流动过程中,多根管道连接处气体流动不均匀,流量分配的损失难以精确计算,对下游管道天然气参数带来计算误差。以管网实际数据为基础,构建了流量分配多目标优化模型,采用线性化方法处理控制方程,通过对边界条件的选取,将多目标优化模型简化为单目标优化模型,并将控制矩阵改写为可以使用TDMA方法求解的三点格式。使用遗传算法求解流量分配后各参数,通过监测计算误差对计算模型及时修正,对管网后续流量分配过程进行计算。结果表明,该方法可以准确模拟天然气管网流量分配过程,压力、流量的最大相对误差分别为0.15%、0.31%,温度误差为0.16 K,满足能量计量要求。

平行流交叉口车道控制与信号配时组合优化

为了提升平行流交叉口实际应用的灵活性,提出车道控制与信号配时组合优化方法,将单向、非对称双向、对称双向、三向、四向设置与布设方向组合共16种方案整合到优化模型中,通过修正交通冲突矩阵自动生成相位相序方案.构建混合整数线性规划模型,实现交叉口设置方案选择、车道分配和信号配时的组合优化.结果表明,在各种流量场景下,对称双向、三向、四向设置方案相较于常规交叉口分别能够提升约20%、20%、50%的通行能力,单向、非对称双向设置方案通行能力与常规交叉口接近,说明平行流交叉口不宜采用单向、非对称双向设置.四向设置方案通行能力的提升幅度最大,最大值能达到70.51%.对称双向和三向设置方案的通行能力提升相差不大,但三向设置在不对称流量场景中的表现优于对称双向设置.

基于公交优先的直线式公交站台共享超车道时空资源优化分析

受城市道路红线条件限制,许多公交车站只能设置直线式公交站台;这种形式的站台很容易产生拥堵排队和机动车冲突的交通现象。基于此,提出了一种共享超车道感应信号控制的优化方案,使用VISSIM软件对共享超车道前后车辆的延误水平进行了仿真分析。研究结果表明:通过设置共享超车道能有效地缓解公交车辆在公交站点拥堵排队和机动车冲突的交通现象;在交通量较大和公交发车频率较高情况下,通过设置带有共享超车道的直线式公交站台能有效地提升公交服务水平,保障近道路交叉口上游路段的公交优先通行和社会车辆有序通行,提升了交通的流畅性与安全性。

限行政策下兰州市空气质量影响因素分析

机动车尾气排放是空气污染物的主要来源之一。为了解机动车限行政策下兰州市空气质量的影响因素,首先对2016—2019年兰州市空气质量数据进行统计分析,其次利用逐步回归分析法建立空气质量指数(Air Quality Index,AQI)与空气污染物浓度的多元线性回归模型,最后对气象因素和空气污染物浓度进行相关性分析。结果表明,限行政策有助于改善空气质量,AQI与空气污染物浓度有明显相关性,气象因素与空气污染物浓度也有明显相关性。

CycleLLH:一种基于周期性整合的新型网络流量预测模型

精准的网络流量预测是实现网络精细化和智能化管理的关键,也是网络运营商、云服务提供商等实现网络智能运维及应用服务保障的重要支撑,属于当前业界研究的热点.网络流量预测问题一般可被视为一种时间序列预测问题,现有时间序列预测模型虽然能起到一定作用,但这些通用模型很少考虑流量数据集本身特点,从而无法在网络流量预测性能上取得突破.为此,本文重点研究了网络流量数据集中的自然周期特征,提出了一种能有效利时间序列周期性特点的网络流量预测通用模型——Cycle Little Linear Head(CycleLLH).该模型主干为Transformer的编码器,其中两个关键设计在于:(1)周期整合.将流量序列按照一个特定周期划分步长划分为不同的周期块,然后将这些周期块对应相位的时间节点分别嵌入到不同输入令牌;(2)小线性层.由多个多层感知机组成,并且多层感知机单独作用于每个相位的时间特征.周期整合使得模型具有两个优点:更有利于模型提取数据集在一个周期内的特征;注意力矩阵的计算和内存复杂度可以看作是和周期划分步长二次方有关的常数,使得模型可以使用更大的回溯窗口而仅增加少量计算资源.通过在公共流量数据集上进行大量实验,本研究表明:与当前最先进的模型相比,CycleLLH在流量预测精度方面表现出显著优势,在六个数据集上的预测精度分别提升了12.3%、8.4%、29.9%、5.8%、8.3%和2.0%.代码可从https://github.com/wenjietang218/CycleLLH.git中获取.

公交导向式慢行系统对交通出行结构影响分析

考虑到合理的公交导向式(TOD)慢行系统规划设计能够引导居民减少私家车出行,优化城市交通出行结构,降低碳排放量,提出了公交导向式慢行系统设计的定义,通过分析公交路线和站点的空间结构,给出TOD+TID模式慢行系统空间布局方案以及模式慢行系统设计流程和方法。采用多目标线性规划模型对城市交通出行结构进行建模,选取碳排放、能源消耗、社会成本、出行舒适度作为目标函数,将路网最佳容量、停车场容量、慢行系统容量作为约束条件构建城市交通出行结构优化模型,计算并对比优化前后交通出行对碳排放变化的影响。最后,结合广州市荔湾区实际案例分析TOD模式下慢行系统对碳排放及能源消耗、交通出行结构的影响,表明TOD模式下慢行系统的合理建设能够降碳减排,优化城市交通出行结构。