Modeling of Shotcrete Application for Use in a Real‐Time Training Simulator

Simulators provide significant advantages in training operators of concrete spraying machinery, such as economic savings, the practical absence of safety risks, and environmental and educational benefits. The main challenge in developing a real‐time training simulator for concrete spraying machinery lies in the modeling of shotcrete application. This article presents a novel method that models and simulates in real time the three main factors influencing shotcrete sprayability: adhesion, cohesion, and rebound. Furthermore, thanks to the addition of an obstacle model, the method makes it possible to spray onto additional supporting elements, which is a typical shotcrete application. The proposed method considers a wet‐mix thick flow spraying process and is based on experiments that were run with a real concrete spraying machine and complemented by expert advice. The method was developed and evaluated using a user‐centered methodology, resulting in realistic shotcrete application modeling that meets the needs for training concrete spraying machinery operators.     

Seismic Data‐Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

A two‐stage dual‐objective structural identification method is presented in this article. The complexity of the identification of story‐level physical models for large‐scale building structures is first addressed through a comparative study. A stiffness variation‐based stabilizing objective is proposed to be necessarily incorporated into iterative optimization with the classical performance objectives to improve the model feasibility, and an area‐type evaluation index is subsequently proposed for the stopping criteria. Accordingly, a two‐stage differential evolution‐based dual‐objective optimization framework is presented for the computation of Pareto fronts for nondominated candidate solutions. Then, the proposed method is investigated using two illustrative examples, including a nine‐story benchmark structure, and a real‐world seven‐story reinforced concrete structure. A series of condensed models are identified from the nondominated solutions on the Pareto front. The prediction performance of the single‐objective optimal model and the dual‐objective acceptable models is compared using the overall discrepancies of acceleration, interstory drift, and modal properties, within both estimation and validation cases. Incorporation of the noise effect into the method is finally studied and discussed.     

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

This article presents a novel real‐time traffic network management system using an end‐to‐end deep learning (E2EDL) methodology. A computational learning model is trained, which allows the system to identify the time‐varying traffic congestion pattern in the network, and recommend integrated traffic management schemes to reduce this congestion. The proposed model structure captures the temporal and spatial congestion pattern correlations exhibited in the network, and associates these patterns with efficient traffic management schemes. The E2EDL traffic management system is trained using a laboratory‐generated data set consisting of pairings of prevailing traffic network conditions and efficient traffic management schemes designed to cope with these conditions. The system is applied for the US‐75 corridor in Dallas, Texas. Several experiments are conducted to examine the system performance under different traffic operational conditions. The results show that the E2EDL system achieves travel time savings comparable to those recorded for an optimization‐based traffic management system.     

Development of Recurrent Neural Network Considering Temporal‐Spatial Input Dynamics for Freeway Travel Time Modeling

Abstract: The artificial neural network (ANN) is one advance approach to freeway travel time prediction. Various studies using different inputs have come to no consensus on the effects of input selections. In addition, very little discussion has been made on the temporal–spatial aspect of the ANN travel time prediction process. In this study, we employ an ANN ensemble technique to analyze the effects of various input settings on the ANN prediction performances. Volume, occupancy, and speed are used as inputs to predict travel times. The predictions are then compared against the travel times collected from the toll collection system in Houston. The results show speed or occupancy measured at the segment of interest may be used as sole input to produce acceptable predictions, but all three variables together tend to yield the best prediction results. The inclusion of inputs from both upstream and downstream segments is statistically better than using only the inputs from current segment. It also appears that the magnitude of prevailing segment travel time can be used as a guideline to set up temporal input delays for better prediction accuracies. The evaluation of spatiotemporal input interactions reveals that past information on downstream and current segments is useful in improving prediction accuracy whereas past inputs from the upstream location do not provide as much constructive information. Finally, a variant of the state‐space model (SSNN), namely time‐delayed state‐space neural network (TDSSNN), is proposed and compared against other popular ANN models. The comparison shows that the TDSSNN outperforms other networks and remains very comparable with the SSNN. Future research is needed to analyze TDSSNN's ability in corridor prediction settings.     

Real‐Time Estimation of Pollution Emissions and Dispersion from Highway Traffic

Traffic‐related air pollution is a serious problem with significant health impacts in both urban and suburban environments. Despite an increased realization of the negative impacts of air pollution, assessing individuals' exposure to traffic‐related air pollution remains a challenge. Obtaining high‐resolution estimates are difficult due to the spatial and temporal variability of emissions, the dependence on local atmospheric conditions, and the lack of monitoring infrastructure. This presents a significant hurdle to identifying pollution concentration hot spots and understanding the emission sources responsible for these hot spots, which in turn makes it difficult to reduce the uncertainty of health risk estimates for communities and to develop policies that mitigate these risks. We present a novel air pollution estimation method that models the highway traffic state, highway traffic‐induced air pollution emissions, and pollution dispersion, and describe a prototype implementation for the San Francisco Bay Area. Our model is based on the availability of real‐time traffic estimates on highways, which we obtain using a traffic dynamics model and an estimation algorithm that augments real‐time data from both fixed sensors and probe vehicles. These traffic estimates combined with local weather conditions are used as inputs to an emission model that estimates pollutant levels for multiple gases and particulates in real‐time. Finally, a dispersion model is used to assess the spread of these pollutants away from the highway source. Maps generated using the output of the dispersion model allow users to easily analyze the evolution of individual pollutants over time, and provides transportation engineers and public health officials with valuable information that can be used to minimize health risks.     

Freeway Travel Time Prediction Using Takagi–Sugeno–Kang Fuzzy Neural Network

This article presents a Takagi–Sugeno–Kang Fuzzy Neural Network (TSKFNN) approach to predict freeway corridor travel time with an online computing algorithm. TSKFNN, a combination of a Takagi–Sugeno–Kang (TSK) type fuzzy logic system and a neural network, produces strong prediction performance because of its high accuracy and quick convergence. Real world data collected from US‐290 in Houston, Texas are used to train and validate the network. The prediction performance of the TSKFNN is investigated with different combinations of traffic count, occupancy, and speed as input options. The comparison between online TSKFNN, offline TSKFNN, the back propagation neural network (BPNN) and the time series model (ARIMA) is made to evaluate the performance of TSKFNN. The results show that using count, speed, and occupancy together as input produces the best TSKFNN predictions. The online TSKFNN outperforms other commonly used models and is a promising tool for reliable travel time prediction on a freeway corridor.     

A Quasi‐Market Framework for Development Competition

This article describes a quasi‐market framework to integrate the diverse perspectives on local government development competition found in the economic development literatures. Within this framework local governments seek to obtain positive externalities associated with economic growth through the provision of services and inducements to private firms in exchange for commitments of employment and investment. Efficient pursuit of economic development is impeded by market and government failures. Better understanding of how the quasi‐market for economic development works promises to enhance our understanding of the relationships between economic and political demands and local development with important implications for evaluation of local growth policy and development competition.     

A Computationally Efficient Algorithm for Real‐Time Tracking the Abrupt Stiffness Degradations of Structural Elements

Real‐time structural identification and damage detection are necessary for on‐line structural damage detection and optimal structural vibration control during severe loadings. Frequently, structural damage can be reflected in the stiffness degradation of structural elements. In this article, a time‐domain three‐stage algorithm with computational efficiency is proposed for real‐time tracking the onsets, locations, and extents of abrupt stiffness degradations of structural elements using measurements of structural acceleration responses. Structural dynamic parameters before damage are recursively estimated in stage I. Then, the time instants and possible locations of degraded structural elements are detected by tracking the errors between the measured data and the corresponding estimated values in stage II. Finally, the exact locations and extents of stiffness degradations of structural elements are determined by solving simple constrained optimization problems in stage III. Both numerical examples and an experimental test are used to validate the proposed algorithm for real‐time tracking the abrupt stiffness degradations of structural elements in linear or nonlinear structures using measurements of structural acceleration responses polluted by noises.     

基于浮动车的城市道路行程时间预测算法研究

装载GPS的浮动车在社会交通流中比重越来越高,已成为主要的行程时间采集手段。研究了基于浮动车的城市道路路段行程时间预测算法,输入数据包括静态空间属性数据、行程时间历史备份数据和基于GPS采集的动态交通行程时间数据,并以5 min为预测间隔进行20 min短时行程时间预测。最后经2.1 km含3个交叉口的路段预测验证,表明该算法单方向单次最大误差23.0%;经过滚动预测,单方向平均绝对误差为5%~6%,精度满足用于城市道路路段的信息发布要求。     

Real‐Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification

In this article, a novel Bayesian real‐time system identification algorithm using response measurement is proposed for dynamical systems. In contrast to most existing structural identification methods which focus solely on parametric identification, the proposed algorithm emphasizes also model class selection. By embedding the novel model class selection component into the extended Kalman filter, the proposed algorithm is applicable to simultaneous model class selection and parametric identification in the real‐time manner. Furthermore, parametric identification using the proposed algorithm is based on multiple model classes. Examples are presented with application to damage detection for degrading structures using noisy dynamic response measurement.     

Evaluation of Information Applications of a Self‐Organizing Distributed Traffic Information System for a Large‐Scale Real‐World Traffic Network

Abstract: This article presents an evaluation of the system performance of a proposed self‐organizing, distributed traffic information system based on vehicle‐to‐vehicle information‐sharing architecture. Using microsimulation, several information applications derived from this system are analyzed relative to the effectiveness and efficiency of the system to estimate traffic conditions along each individual path in the network, to identify possible incidents in the traffic network, and to provide rerouting strategies for vehicles to escape congested spots in the network. A subset of vehicles in the traffic network is equipped with specific intervehicle communication devices capable of autonomous traffic surveillance, peer‐to‐peer information sharing, and self‐data processing. A self‐organizing traffic information overlay on the existing vehicular roadway network assists their independent evaluation of route information, detection of traffic incidents, and dynamic rerouting in the network based both on historical information stored in an in‐vehicle database and on real‐time information disseminated through intervehicle communications. A path‐based microsimulation model is developed for these information applications and the proposed distributed traffic information system is tested in a large‐scale real‐world network. Based on simulation study results, potential benefits both for travelers with such equipment as well as for the traffic system as a whole are demonstrated.     

Near-Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Abstract:  Accurate short-term prediction of travel speed as a proxy for time is central to many Intelligent Transportation Systems, especially for Advanced Traveler Information Systems and Advanced Traffic Management Systems. In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert–Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters.     

Sense‐Think‐Act Framework for Intelligent Building Energy Management

The realization of smart and energetically efficient buildings is contingent upon the successful implementation of two tasks that occur on distinct phases of the building life cycle: in the design and subsequent retrofitting phases, the selection and implementation of an effective energy concept, and, during the operation phase, the actuation of energy systems to ensure parsimonious energy use while retaining acceptable end‐user thermal comfort. Operational efficiencies are achieved through the use of Building Energy Management Systems tasked to deliver core Sense, Think, Act (STA) functionalities: Sense, using sensing modalities installed in the building; Think, utilizing, typically a rule‐based decision system; and Act, by sending actuation commands to controllable building elements. Providing the intelligence in this STA process can be a formidable task due to the complex interplay of many systems and occurrence of disturbances. In this article, an architectural and algorithmic framework is presented to provide streamlined implementation of this process. Important ingredients in this framework are: (S) a data access component capable of collecting and aggregating information from a number of heterogeneous sources (sensors, weather stations, weather forecasts); (T) a model‐based optimization methodology to generate intelligent operational decisions; and (A) an assessment and actuation component. An illustrative application of the proposed methodology in an office building is provided.     

产品:企业持续发展的动力

过去的几年,中国工程机械行业在国家良好的宏观经济形势下和积极的财政政策的作用下,得到了快速发展。然而,这一高速发展以市场问题的急剧放大和行业各企业普遍增长为主要特点。从行业增长的质量看,并不能让人欣慰,“粗放式”增长的特征极其明显。行业的持续增长缺乏强有力的动力。     

Job-Employed Resident Imbalance and Travel Time in Gauteng: Exploring the Determinants of Longer Travel Time

In the Gauteng City Region, a substantial number of workers reside far from their place of work, translating into long travel distances and high travel costs and time costs. This study examines the relationship between job-employed ratio, i.e. the percentage of residents that work in the same location in which they live, and average travel times. It also compares the average travel time between internal capture workers, who work and reside in the same area, with employment leakage workers, who work in areas other than those they reside in, and analyses other factors that influence average travel times. The ANOVA results reveal that job-rich and balanced areas are associated with higher average travel times for workers in housing-rich areas. Internal capture workers had the lower average travel time compared employment leakage workers. The regression results indicate that male gender, age and Black African ethnicity are positively associated with higher mean average travel time. Income, education level, informality and private transportation modes are negatively correlated with mean average travel time. This finding implies that land-use planning and public transport policies should be integrated to reduce travel time to work in the Gauteng City Region.