Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes.     

Gender differentials in travel pattern – The case of a mid-sized city,Rajkot, India

This paper is at the conjunction of arguments for gender equity in transport services on one hand and need to move towards low carbon transport on the other in a developing country city. The case study city, Rajkot, a mid-sized Indian city with mixed land use and limited public transport at the time of this study, has short trip lengths and low trip rates. But, even in this city there is gender disparity in travel pattern. Thus, trip rates and trip lengths of women in the city in each of the income group are lower than that of their male counter parts. With the increase in income, both, the trip rates and lengths increase, but, the increase is higher for men than for women. While the large proportions of women in each income group walk, that among the lowest income group walking is the predominant mode, as they are ‘no-choice’ or ‘forced’ walkers. With the increase in household income, women tend to shift to para-transit and men to personal motorized transport. If sustainability arguments or climate policies are to target retaining the current low ecological footprint of the women as one of the strategies, it needs to cater to expansion of their mobility while improving infrastructure to support the same.     

基于关系代数的结构模式识别系统句法分析单元设计

介绍了一种基于关系代数的句法分析单元设计.尝试通过与关系代数模型紧密集成,利用关系数据库系统能够组织和快速检索海量数据的特点,提高句法分析单元的处理复杂文法样本的能力,同时减轻用户的编码负担.在此框架中,部分推导树集合被表示为关系；推理规则集被表示为扩展的关系代数运算.给出了算法实现所需的数据结构和控制结构.从而将句法分析算法完全表示为数据库系统中的一个关系运算过程.最后通过原型系统验证了该方法的可行性.     

Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data

Origin-destination (OD) pattern estimation is a vital step for traffic simulation applications and active urban traffic management. Many methods have been proposed to estimate OD patterns based on different data sources, such as GPS data and automatic license plate recognition (ALPR) data. These data can be used to identify vehicle IDs and estimate their trajectories by matching vehicles identified by different sensors across the network. OD pattern estimation using ALPR data remains a challenge in real-life applications due to the difficulty in reconstructing vehicle trajectories. This paper proposes an offline method for historical OD pattern estimation based on ALPR data. A particle filter is used to estimate the probability of a vehicle’s trajectory from all possible candidate trajectories. The initial particles are generated by searching potential paths in a pre-determined area based on the time geography theory. Then, the path flow estimation process is conducted through dividing the reconstructed complete trajectories of all detected vehicles into multiple trips. Finally, the OD patterns are estimated by adding up the path flows with the same ODs. The proposed method was implemented on a real-world traffic network in Kunshan, China and verified through a calibrated microscopic traffic simulation model. The results show that the MAPEs of the OD estimation are lower than 19%. Further investigation shows that there exists a minimum required ALPR sampling rate (60% in the test network) for accurately estimating the OD patterns. The findings of this study demonstrate the effectiveness of the proposed method in OD pattern estimation.     

基于时间卷积自编码网络的实时交通事件自动检测方法

为了提高道路异常交通事件检测效率并降低误报率,提出了一种基于时间卷积自编码网络的实时交通事件自动检测方法。首先设计了基于波动相似性度量的交通模式搜索算法用来筛选具有相同交通规律的样本数据;并构造了交通流模式矩阵作为网络模型输入,以避免样本不均衡与单一样本数据随机性对交通模式学习的干扰;同时设计了新的时间卷积自编码网络对交通模式特征进行无监督提取并对未来交通参数进行合理预测;为了降低交通流参数随机波动性带来的事件判别的干扰,设计了异常状态评估方法,通过对模型预测误差分布的学习,结合当前检测数据给出最终的事件判定结果。采用美国西雅图I90公路与I405公路2015年全年的交通流检测数据与历史事故数据进行实证研究,并与6种典型交通事件检测算法进行性能对比。研究结果表明:基于时间卷积自编码网络的实时交通事件自动检测算法具有较高的检测率、较低的误报率以及更快的平均检测时间;综合各种交通运行情况下,可接受误检率分别为5%、10%时,平均检测率可分别达到93%、98%;同时算法能够自适应学习交通状态的动态变化,对不同交通运行环境具有较强适应性与稳定性。     

船舶机舱集中监测系统新方法的研究

提出一种基于计算机模式识别技术的船舶机舱集中监测系统新方法，并对其实现方式作了初步探索。这种新方法直接采集机旁一次仪表的图像信息，经分析处理后得到对应数值，从而省却现行结构中的传感器、变送器和屏蔽线缆等，使系统结构大大简化。     

一种6～8 GHz超宽带天线的设计分析

设计印刷在有限大聚四氟乙烯板上的叶状偶极天线,设计的工作频带是6-8 GHz。应用结合完全匹配层（PML）技术的时域有限差分（FDTD）方法,对天线的阻抗特性和远区辐射场的分布进行了研究。结果表明：在没有任何加载的情况下,与50Ω馈线相连时,在设计的主频带内,其电压驻波比小于1.6,在几个不同频率点的辐射场方向图表现出良好的稳定性。该天线的特性能够满足超宽带的要求,可用于无载波超宽带无线数据通信系统。     

桁架式天线结构动态性能的有限元分析

以有限元理论为基础,运用有限元分析软件对车载桁架式天线系统进行结构动态分析及稳定性分析。模态分析的结果表明天线系统的低阶固有频率较低,不当的使用会导致结构的共振。稳定性分析表明天线结构的失稳属于局部屈曲行为,进一步的非线性屈曲分析表明,屈曲的临界载荷在正常工作载荷外,不影响系统工作。     

流场的动态可视化

采用改进的Eular算法 ,将数学模型计算成果数据换为具有二阶精度的拉格朗日流场 ,以动画的形式显示出来并能进行交互处理。该系统能直观地反映整个流场流态随时间变化的过程 ,比较准确地显示出流场的局部细节 ,为分析潮流和泥沙运动提供了基础     

浙江省B2C电子商务企业物流模式探讨

B2C电子商务企业能取得多大的成功很大程度上依赖于实际物流的操作,B2C电子商务企业必须根据自身情况,正确选择和采用企业自营物流模式或是第三方物流模式。本文根据实地调研的数据,就浙江省B2C电子商务企业的物流模式存在的问题进行分析探讨,寻求成本低廉、需求响应快速、安全可靠且适宜的物流模式。