信号控制下交叉口延误计算方法研究

为了对交通信号控制参数进行优化,需要对交叉口延误进行定量的分析与计算。根据信号控制交叉口理论,在以往定时信号延误研究的基础上,基于交叉口一个进口方向的车辆延误分析,针对交叉口各进口方向同时处于非饱和与同时处于过饱和交通状况,分析并推导了交叉口延误公式.并用具体的算例说明了公式的用法。公式表明了交叉口延误与信号控制参数、车辆到达率等参数之间的动态关系,为进一步研究交通信号自适应控制方法和建立交通信号控制参数优化的性能指标函数提供了信息。     

偏压连拱隧道病害分析及治理措施

基于某高速公路偏压连拱隧道衬砌发生严重破坏和隧道上方发生较大规模的山体开裂等病害,采用地震CT手段对隧道周围的工程地质进行了探测;根据现场监测数据,分析了隧道支护结构的受力状态及其安全性;对复杂地质条件下两种典型的施工方案进行了动态施工的数值模拟,对比分析了不同施工方案对围岩稳定和衬砌受力的影响;探讨了衬砌发生严重破坏的原因,并提出了相应的治理措施。研究结果表明:连拱隧道内侧断裂带、上方松散层等不良地质条件和不合理的施工方案是导致隧道衬砌严重破坏和山体地表开裂的主要原因。研究结果为分析和治理偏压连拱隧道病害提供了依据。     

基于实测数据评估交通事件检测中神经网络应用性的研究

神经网络为交通事件自动检测技术摆脱传统方法探测率低、误报率高的状况提供了新的解决思路。在以往局限于仿真数据研究的基础上,本文利用I-880数据库实测交通事件数据对神经网络在交通事件自动检测中的实际应用进行了研究,结果表明:神经网络应用于交通事件自动检测技术中,具有较高的探测率和较低的误报率,但该算法可移植性较差,在实际应用中要予以考虑。     

基于视觉的前方车辆探测技术研究方法综述

对国内外关于车道上本车前方车辆检测方法进行了综述。根据探测车辆所采用的传感器不同,分别对采用单目视觉、双目视觉、视觉与其他传感器融合以及非视觉传感器的几种探测方法进行了比较与分析。同时,针对每种探测方法,进一步阐述了目前车辆识别的一些具体算法。最后,指出了每种方法存在的优点和一些不足。     

灌注混凝土桩施工方法及工艺的探讨

通过对钻孔灌注桩与沉入桩的对比分析,得出了钻孔灌注桩的优点,并且详细介绍了钻孔灌注桩的施工工艺和方法,总结了提高灌注混凝土桩承载力的方法和灌注桩的检测手段及一般事故处理方法,以供今后类似工程实践参考.     

大直径盾构管片水平拼装试验方法探究

大直径盾构管片水平拼装试验存在效率低下甚至失败的情况,不能真实全面反映产品尺寸精度。分析其原因除大直径管片体积大、重量大的因素外,主要原因在于现有的试验设备工装简陋、试验方法粗放、规范要求低等。结合当前水平拼装的规范要求,分析现有试验方法存在的不足,对现有拼装设备工装进行改造,制定高标准的、更加严格的工艺措施,创造性地提出在每层管片拼装时增加标高检测的新思路。将新方法应用于南京长江第五大桥夹江隧道工程管片水平拼装试验,结果良好,证明了此试验方法的科学性与有效性。     

信号词在英语听力学习的作用研究

英语听力学习是一个复杂的过程,影响英语听力学习的因素众多,其中对于语言微技能如信号词的训练非常重要。本文以湖北职业技术学院外语学院的学生为例,针对信号词在英语听力学习中所起的作用进行研究,证实了掌握好信号词、增强语言微技能的训练对于学生听力理解能力的提高有着非常重要的作用。     

Analytical and simulation approaches to understand combined effects of transit signal priority and road-space priority measures

Transit signal priority (TSP) may be combined with road-space priority (RSP) measures to increase its effectiveness. Previous studies have investigated the combination of TSP and RSP measures, such as TSP with dedicated bus lanes (DBLs) and TSP with queue jump lanes (QJLs). However, in these studies, combined effects are usually not compared with separate effects of each measure. In addition, there is no comprehensive study dedicated to understanding combined effects of TSP and RSP measures. It remains unclear whether combining TSP and RSP measures creates an additive effect where the combined effect of TSP and RSP measures is equal to the sum of their separate effects. The existence of such an additive effect would suggest considerable benefits from combining TSP and RSP measures. This paper explores combined effects of TSP and RSP measures, including TSP with DBLs and TSP with QJLs. Analytical results based on time-space diagrams indicate that at an intersection level, the combined effect on bus delay savings is smaller than the additive effect if there is no nearside bus stop and the traffic condition in the base case is under-saturated or near-saturated. With a near-side bus stop, the combined effect on bus delay savings at an intersection level can be better than the additive effect (or over-additive effect), depending on dwell time, distance from the bus stop to the stop line, traffic demand, and cycle length. In addition, analytical results suggest that at an arterial level, the combined effect on bus delay savings can be the over-additive effect with suitable signal offsets. These results are confirmed by a micro-simulation case study. Combined effects on arterial and side-street traffic delays are also discussed.     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.