隧道及地下工程远程自动化监测与预警系统构建研究

基于Python编程语言和大数据，构建可实现工程监测、综合预警、可视化、参数反演及实时动态分析等功能的自动化监测预警系统。该自动化监测预警系统可对施工过程中岩土体压力、位移、渗透水压等进行实时监测，并自动采集监测数据进行远程无线传输，自动存储到数据库中，借助互联网云平台进行数据共享；监测数据可供IA-BP算法模块、预警预报模块调用，采用IA-BP智能算法对数据库内监测数据进行反演及智能预测分析，并进行单项阈值和IA-BP智能算法综合对比分析，达到工程灾害预警预报目的。     

大跨径连续刚构桥运营期监测分析

为分析大跨径连续刚构桥的运营情况,文章采用现场监测法对各监测断面挠度和应力进行监测,并结合理论计算值对桥梁稳定性进行分析。根据监测结果,主桥中部两跨跨中截面的挠度和应力均较大,在监测三年后挠度增速明显下降,进入稳定发展阶段,且挠度和应力实测值均小于理论计算值,说明桥梁变形和应力均在设计允许范围内,桥梁结构安全稳定。     

隧道二次衬砌合理施作时机确定方法研究

隧道二次衬砌合理施作时机的确定关键在于分析评估围岩变形何时达到稳定状态,现场监控量测蕴含整个围岩发展演化过程的所有信息,是其应力、变形状态最直接的反映。文章构建了一种基于监控量测的隧道围岩变形状态动态综合评估模型,克服了传统综合评估方法仅在某一时间截面下进行静态评估的缺陷。该模型基于离差最大化原则对监控量测指标进行动态赋权,监测指标权重随其在评估时域内的稳定度而动态变化,时域内指标值离差越大赋予权重越大。同时,模型通过引入加速度修正系数概念,将监测指标变化速度状态和趋势进行融合考虑。并且模型引入了时间权重向量和时间度的概念,对采集的监测数据按不同时期合理地赋予不同时间权重(近期监测数据时间权重高于远期监测数据)。该模型的有效性通过在杭州紫之隧道设置试验段进行了验证,为确定隧道二次衬砌合理施作时机提供了一种新的可靠方法。     

某在役桥梁健康监测多级挠度预警阈值设定分析

为实现桥梁的健康监测和自动预警,依托实际工程,结合桥梁监测系统,通过分析温度与挠度之间的关系,将实际监测的竖向位移数据和竖向位移-温度的回归方程进行比较,得到桥梁在车辆荷载作用下的挠度值。结合桥梁结构特性以及有限元计算结果,设定多级桥梁竖向位移预警阈值,为桥梁结构长期安全运营提供准确、可靠的预警信息。     

基于多变量GP-DE模型的隧道变形时间序列预测研究

准确预测和控制隧道变形是确保隧道工程施工安全的重点。针对目前隧道围岩变形时间序列预测的不足,文章提出了一种基于多变量高斯过程(GP)-差异进化算法(DE)的隧道变形时间序列预测方法。根据隧道自动化监测结果进行多变量相空间重构,并通过主成分分析法降低输入维数。在此基础上采用GP-DE模型进行隧道变形预测研究。以吉林省高丽沟隧道围岩拱顶位移为例进行预测,将预测结果与BP神经网络、SVM模型预测结果进行比较。研究结果表明,多变量时间序列的GP-DE模型具有更高的预测精度,预测值与实测值吻合更好,是一种有效的隧道位移预测方法。     

跑马山隧道进口地表沉降监测及稳定性评价

隧道监控量测技术作为新奥法施工的核心手段之一,是进行动态设计和施工的重要参考依据,对于判定隧道围岩稳定性和确保施工安全具有重要的指导意义。文章通过对康新高速公路跑马山隧道进口地表进行沉降监测,依据地表沉降监测数据来判断围岩的稳定性和初次衬砌支护动态变化信息,确保隧道安全施工,同时对隧道进口地表稳定性进行评价,为二次衬砌的施做时间提供指导。     

京张高速公路官厅湖特大桥健康监测系统实施的研究

通过桥梁健康监测项目实现了对应变、挠度、振动、温度和动态轴重等方面实时在线监测,并实现对原始数据的处理和显示,以保证大桥的安全运营。     

基于交通大数据的动态交通状态预测及全局路径规划研究

为提高动态交通状态预测的准确性,对基于交通大数据的动态交通状态预测及全局路径规划进行研究。以交通大数据为基础,获取指定路段的属性信息与采集信息,通过对样本数据进行描述,得到该路段的试验数据。在具体的预测和分析阶段,构建基于交通大数据的预测模型,实现对动态交通状态的可靠预测。试验结果表明,时间特征值对交通流量的影响较大,某一时间段的交通状态数据可为全局路径规划提供数据源,有利于相关人员作出科学决策,可更好地满足当前智慧交通管理新需求。     

时间序列法预测沥青路面损坏状况指数研究

由于沥青路面损坏状况影响因素很多,因此要准确预测路面损坏状况较困难。文章采用时间序列法建立预测模型,结合同三高速公路（上海段）路面损坏状况的实测数据进行预测分析。分析结果表明时间序列法具有较高的预测精度和易修正性。     

非线性分类模型在高速公路边坡监测中的应用

针对高速公路边坡形变预测数据存在非线性和不确定性的特点,为探究边坡监测地质参数与边坡安全系数的相关性,文章基于广西某高速边坡监测数据,应用SVM和ANFIS等非线性分类模型对边坡监测地质参数进行计算分析。结果表明:ANFIS计算结果最大误差为0.281 3,平均误差为0.107 8,更接近实际误差,获得的预测结果相对更为准确有效,为判断边坡安全性和采用边坡防护防治措施提供参考。     

Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway

The main purpose of this study was to investigate the predictability of travel time with a model based on travel time data measured in the field on an interurban highway. Another purpose was to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-kilometre-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the majority of the forecasts were close to the actual measured values. Consequently, use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times.     

Predicting short-term bus passenger demand using a pattern hybrid approach

This paper proposes an Interactive Multiple Model-based Pattern Hybrid (IMMPH) approach to predict short-term passenger demand. The approach maximizes the effective information content by assembling the knowledge from pattern models using historical data and optimizing the interaction between them using real-time observations. It can dynamically estimate the priori pattern models combination in advance for the next time interval. The source demand data were collected by Smart Card system along one bus service route over one year. After correlation analysis, three temporal relevant pattern time series are generated, namely, the weekly, daily and hourly pattern time series. Then statistical pattern models are developed to capture different time series patterns. Finally, an amended IMM algorithm is applied to dynamically combine the pattern models estimations to output the final demand prediction. The proposed IMMPH model is validated by comparing with statistical methods and an artificial neural network based hybrid model. The results suggest that the IMMPH model provides a better forecast performance than its alternatives, including prediction accuracy, robustness, explanatory power and model complexity. The proposed approach can be potentially extended to other short-term time series forecast applications as well, such as traffic flow forecast.     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

We propose Time–Space Threshold Vector Error Correction (TS-TVEC) model for short term (hourly) traffic state prediction. The theory and method of cointegration with error correction mechanism is employed in the general design of the new statistical model TS-TVEC. An inherent connection between mathematical form of error correction model and traffic flow theory is revealed through the transformation of the well-known Fundamental Traffic Diagrams. A threshold regime switching framework is implemented to overcome any unknown structural changes in traffic time series. Spatial cross correlated information is incorporated with a piecewise linear vector error correction model. A Neural Network model is also constructed in parallel to comparatively test the effectiveness and robustness of the new statistical model. Our empirical study shows that the TS-TVEC model is an effective tool that is capable of modeling the complexity of stochastic traffic flow processes and potentially applicable to real time traffic state prediction.     

Application of time series techniques for forecasting truck traffic attracted by the Bombay metropolitan region

Knowledge of future traffic flow is an essential input in the planning, implementation and development of a transportation system. It also helps in its operation, management and control. Time series analysis techniques have been extensively adopted for this purpose in the fields of economics, social sciences and in other fields of technology. An attempt has been made in this study to apply the techniques of time series analysis to goods traffic, particularly truck traffic. Four predominant corridors, N.H.3, N.H.4, N.H.8 and Lal Bahadur Shastri Road (L.B. S. Rd.), accounting for majority of truck movement in the Bombay Metropolitan Region (BMR), have been considered for modeling. Raw data was processed initially, to obtain an insight into the structure of time series. Ten candidate models of the Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) family are investigated to represent each of the four corridors. Models finally proposed, to represent each of the four corridors have been selected based on Minimum Mean Square Error (MMSE) and Maximum Likelihood Rule (MLR) criteria. Models ARIMA (2, 1, 0), ARMA (1.0), ARMA (1, 1) and ARIMA (1, 1, 0) are proposed for N.H.3, N.H.4, N.H.8 and L.B.S. Rd. respectively, based on significant weekly periodicity.     

Construction of traffic state vector using mutual information for short-term traffic flow prediction

Short-term traffic flow prediction is an integral part in most of Intelligent Transportation Systems (ITS) research and applications. Many researchers have already developed various methods that predict the future traffic condition from the historical database. Nevertheless, there has not been sufficient effort made to study how to identify and utilize the different factors that affect the traffic flow. In order to improve the performance of short-term traffic flow prediction, it is necessary to consider sufficient information related to the road section to be predicted. In this paper, we propose a method of constructing traffic state vectors by using mutual information (MI). First, the variables with different time delays are generated from the historical traffic time series, and the spatio-temporal correlations between the road sections in urban road network are evaluated by the MI. Then, the variables with the highest correlation related to the target traffic flow are selected by using a greedy search algorithm to construct the traffic state vector. The K-Nearest Neighbor (KNN) model is adapted for the application of the proposed state vector. Experimental results on real-world traffic data show that the proposed method of constructing traffic state vector provides good prediction accuracy in short-term traffic prediction.     

Estimation of infrastructure performance models using state-space specifications of time series models

We consider state-space specifications of autoregressive moving average models (ARMA) and structural time series models as a framework to formulate and estimate inspection and deterioration models for transportation infrastructure facilities. The framework provides a rigorous approach to exploit the abundance and breadth of condition data generated by advanced inspection technologies. From a managerial perspective, the framework is attractive because the ensuing models can be used to forecast infrastructure condition in a manner that is useful to support maintenance and repair optimization, and thus they constitute an alternative to Markovian transition probabilities. To illustrate the methodology, we develop performance models for asphalt pavements. Pressure and deflection measurements generated by pressure sensors and a falling weight deflectometer, respectively, are represented as manifestations of the pavement’s elasticity/load-bearing capacity. The numerical results highlight the advantages of the two classes of models; that is, ARMA models have superior data-fitting capabilities, while structural time series models are parsimonious and provide a framework to identify components, such as trend, seasonality and random errors. We use the numerical examples to show how the framework can accommodate missing values, and also to discuss how the results can be used to evaluate and select between inspection technologies.     

Modeling schedule recovery processes in transit operations for bus arrival time prediction

Many existing algorithms for bus arrival time prediction assume that buses travel at free‐flow speed in the absence of congestion. As a result, delay incurred at one stop would propagate to downstream stops at the same magnitude. In reality, skilled bus operators often constantly adjust their speeds to keep their bus on schedule. This paper formulates a Markov chain model for bus arrival time prediction that explicitly captures the behavior of bus operators in actively pursuing schedule recovery. The model exhibits some desirable properties in capturing the schedule recovery process. It guarantees provision of the schedule information if the probability of recovering from the current schedule deviation is sufficiently high. The proposed model can be embedded into a transit arrival time estimation model for transit information systems that use both real‐time and schedule information. It also has the potential to be used as a decision support tool to determine when dynamic or static information should be used.     

A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data

Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.     

Coherent approach for modeling and nowcasting hourly near-road Black Carbon concentrations in Seattle,Washington

With a growing awareness of the importance of near-road air pollution and an increasing population of near-road pedestrians, it is imperative to “nowcast” near-road air quality conditions to the general public. This necessitates the building hourly predictive models that are both accurate and easy to use. This study demonstrates an approach to model the hourly near-road Black Carbon (BC) concentrations given on-road traffic information and current meteorological conditions using datasets from two urban sites in Seattle, Washington. The optimal set of prediction variables is determined with a Bayesian Model Averaging (BMA) method and three different model structures are further developed and compared by goodness-of-fit. An innovative approach is proposed to translate wind direction from numerical values to categorical variables with statistical significance. By modeling the autocorrelation within the BC time series using an AR(1) component, the model achieves a satisfactory prediction accuracy. The conditional heteroscedasticity and heavy-tailed distribution of the model residuals are successfully identified and modeled by the General Auto Regressive Conditional Heteroscedasticity (GARCH) model, which provides valuable insights to the interpretation of prediction results. The methodological procedure demonstrated in selecting and fine-tuning the model is computationally efficient and valuable for further implementation onto online platforms for near-road BC nowcasting. A comparison between the two sites also reveals the effectiveness of local freight regulation for mitigating the environmental impacts from a heavy truck fleet.