提高TSP探测精度的途径

TSP作为目前最先进的隧道地质超前预报探测仪器，得到了广泛的应用。但是由于在现实中存在各种问题，从而导致该仪器的预测精度受到了极大限制，主要阐述如何提高其预测精度，更好地为隧道的建设服务。     

A methodology for evaluating the performance of model-based traffic prediction systems

Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive to the complexity of the network and to the amount and quality of the data available. They also require significant human intervention and are therefore not suitable for real-time monitoring. This paper outlines a set of criteria for designing tests that are appropriate for the mbTPS task. It also proposes a test that meets the criteria. The test compares the predictions of the mbTPS in question to those of a model-less alternative. A t-test is used to determine whether the predictions of the mbTPS are superior to those of the model-less predictor. The approach is applied to two different systems using data from the I-210 freeway in Southern California.     

Real-time trip purpose prediction using online location-based search and discovery services

The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

隧道工程建设地质预报及信息化技术的主要进展及发展方向

复杂的不良地质条件是制约隧道安全高效建设的主要因素,要实现隧道工程的安全高效建设,首先要提高地质预测预报技术水平及其信息化程度。1)介绍我国复杂不良地质隧道超前预报的方法进展及其应用,包括突水突泥灾害源超前探测方法与设备、断层破碎带超前预报、城市地铁溶洞和孤石等探测的进展及应用等;2)介绍我国隧道岩爆监测预警方法及其应用,预报清楚之后就要加强安全风险过程监控;3)介绍基于BIM技术的建筑物(隧道工程)安全风险监控最新进展,包括安全风险实时感知系统和实时预警系统;4)指出隧道工程建设信息化技术的发展方向,包括开展基于大数据技术的TBM/盾构施工的分析与控制研究以及数字隧道向智慧隧道(建设和运营维护)的发展。     

多元评价体系组合模型在铁路隧道变形预测中的应用

为克服传统预测模型结构单一、预测精度及稳定性不足等缺陷,提出多元体系组合预测模型的建模思路。首先,基于支持向量机、BP神经网络及ARMA模型3种单一预测模型,构建铁路隧道变形预测体系;再以均方根误差、误差平方和及平均绝对误差等为评价准则或指标,构建各预测结果的误差评价体系,求解各单项预测模型的权值贡献指数,得到最优组合权值;然后利用后验差检验、残差检验和关联度检验构建预测精度校验体系,对组合预测结果进行检验,评价预测模型的有效性;最后,结合工程实例,对多元体系组合预测模型在特大断面隧道中的变形预测效果进行检验。结果表明:多元评价体系组合模型预测相对误差值均小于2%,具有较高的预测精度,且较单一预测模型具有更高的预测精度,也一致通过相关检验,验证了多元体系组合预测模型的有效性。     

城市地下道路分(合)流匝道通风阻力特性

为分析多点进出结构的城市地下道路空气流动特性,以长沙市营盘路湘江隧道为原型,通过现场实测、缩尺模型试验以及CFD软件数值模拟方法,对分(合)匝道通风阻力特性进行了研究,考察了雷诺数、风量比、分岔角度对分(合)流匝道阻力特性的影响规律。研究结果表明:1)主隧道与匝道风量比、主隧道与匝道夹角是影响分(合)流匝道局部阻力系数的关键因素;2)城市地下道路分(合)流匝道局部阻力变化特性,不宜简单套用通风管道的三通构件的参数;3)结合最小二乘法和Matlab软件对计算结果进行多因素回归分析,给出了基于长沙营盘路湘江隧道的分(合)流匝道主隧道和匝道的局部阻力系数关联式。研究结果可为复杂结构城市地下道路通风系统阻力特性分析及通风工程优化设计提供方法参考。     

基于灰色残差GM（1，1）模型的道路交通量预测的研究

道路交通体系是一个多因素、多层次、多目标的复杂系统。其中交通量信息系统具有明显的层次复杂性,结构关系的模糊性,动态变化的随机性,指标数据的不完全和不确定性。由于技术方法、人为因素、自然环境变化的影响,造成各种数据误差、短缺甚至虚假现象,系统的作用机制不明确,系统的状态、结构、边界关系难以精确描述,属于典型的灰色系统。在作量化、模型化、实体化研究时,能作为反映系统主要动态特征的数据是很少的。由于环境对系统的干扰,系统信息中原始数据序列往往呈现离乱情况,离乱数列即为灰色数列或称灰色过程,灰色理论利用那些较少的或不确切的表示系统行为特征的原始数据序列作生成变换后建立微分方程,对灰色过程建立的模型称为灰色模型（Greymodel）,简称GM模型。本文从理论上介绍了GM（1,1）模型和灰色残差GM（1,1）模型建立的一般过程,然后将其应用于交通量预测的实际例子中。预测结果表明,该方法是可行的。     

探讨沥青路面局部渗水的原因及处理方法

通过武汉市汉蔡高速公路沥青路面出现的局部渗水现象,分析沥青路面产生局部渗水的原因及处理的必要性,提出对沥青路面局部渗水的处理原则及处理方法。