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.     

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.     

Development of a Data‐Driven Framework for Real‐Time Travel Time Prediction

Travel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long‐term prediction in a real‐time manner have been lacking. Existing methods do not fully utilize the advantages of the state‐of‐the‐art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real‐time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the long‐term (at least 6‐hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k‐nearest neighbor (Mk‐NN) method which is compared with the conventional k‐NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long‐term travel time with shorter computation time.     

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.     

Time‐Varying Lane‐Based Capacity Reversibility for Traffic Management

Abstract: This article presents a new bi‐level formulation for time‐varying lane‐based capacity reversibility problem for traffic management. The problem is formulated as a bi‐level program where the lower level is the cell‐transmission‐based user‐optimal dynamic traffic assignment (UODTA). Due to its Non‐deterministic Polynomial‐time hard (NP‐hard) complexity, the genetic algorithm (GA) with the simulation‐based UODTA is adopted to solve multiorigin multidestination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4 with a jam‐density factor parameter (JDF) employ time‐dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs best. The GA with the appropriate inclusion of problem‐specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.     

Surrogate‐Based Optimization of Expensive‐to‐Evaluate Objective for Optimal Highway Toll Charges in Transportation Network

This article adopts a family of surrogate‐based optimization approaches to approximate the response surface for the transportation simulation input–output mapping and search for the optimal toll charges in a transportation network. The computational effort can thus be significantly reduced for the expensive‐to‐evaluate optimization problem. Meanwhile, the random noise that always occurs through simulations can be addressed by this family of approaches. Both one‐stage and two‐stage surrogate models are tested and compared. A suboptimal exploration strategy and a global exploration strategy are incorporated and validated. A simulation‐based dynamic traffic assignment model DynusT (Dynamic Urban Systems in Transportation) is utilized to evaluate the system performance in response to different link‐additive toll schemes implemented on a highway in a real road transportation network. With the objective of minimizing the network‐wide average travel time, the simulation results show that implementing the optimal toll predicted by the surrogate model can benefit society in multiple ways. The travelers gain from the 2.5% reduction (0.45 minutes) of the average travel time. The total reduction in the time cost during the extended peak hours would be around US$65,000 for all the 570,000 network users assuming a US$15 per hour value of time. Meanwhile, the government benefits from the 20% increase of toll revenue compared to the current situation. Thus, applying the optimized pricing scheme in real world can be an encouraging policy option to enhance the performance of the transportation system in the study region.     

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.     

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.     

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

Abstract: In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.     

Long‐Distance‐Commuter (LDC) Lane: A New Concept for Freeway Traffic Management

Many freeways connect two major cities (e.g., Gold Coast to Brisbane) in Australia. A great portion of commuters travel between these two cities whereas the number of commuters taking the on‐ramps and off‐ramps in between is comparatively small. Enlightened by this observation, we propose a novel and practical concept for freeway traffic management: long‐distance‐commuter (LDC) lane. The LDC lane is a dedicated lane that only commuters traveling between the two major cities can use. Since there is no or only minor disturbance from forced lane‐changing of vehicles from other lanes, the LDC lane admits a much higher capacity. Our preliminary case study based on the freeway between Gold Coast and Brisbane demonstrates that using the LDC lane significantly increases the average speed as well as overall capacity.     

Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning

Structural damage detection is still a challenging problem owing to the difficulty of extracting damage‐sensitive and noise‐robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low‐level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise‐free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.     

Limitations of height‐to‐width ratio for base‐isolated buildings under earthquake

The limitation of height‐to‐width ratio (HWR) for a base‐isolated building with elastomeric rubber bearings is of considerable concern to structural design engineers. Guidelines and codes on this type of building have to deal with this issue. Nevertheless, until now, no systematical and quantitative studies have been done on this problem for base‐isolated buildings. For this reason, the main objective of this paper is to focus on investigations on the limit of the HWR for the isolated building with rubber bearings under different conditions subjected to earthquake excitations. The simplified formulation is derived to explore the rules of seismic responses for the structural system and some influential factors, such as the site soil conditions, seismic ground motion intensity, period of the isolated system, period of the superstructure and layout of isolators, are studied and discussed. According to the numerical results, it has been found that the effects of site soil conditions on the HWR limit values are important: the softer the site is, the smaller the HWR limit value is under different seismic intensities. The predominant period of an isolated building also plays a considerable role in the HWR limit value, namely, the isolated building with a longer period may have a relatively large HWR value; and the stiffness of the superstructure affects the HWR limit value little. Furthermore, an effective method to improve the HWR limit value is proposed. Copyright © 2006 John Wiley & Sons, Ltd.     

Methodology for developing earthquake‐resilient structures

This paper proposes a methodology for developing earthquake‐resilient structures (ERSs). This is achieved by following principles of full cycle performance control and embracing a holistic approach to design led analysis (DLA) of ERS. Collapse prevention (CP) and postearthquake realignment and repairs (PERRs) are the basic traits of ERS. Despite the availability of several systems involving combinations of gap opening link beams (GOLBs), rigid rocking cores, buckling‐restrained braces, replaceable energy dissipating moment connections, and so forth, neither CP nor PERR are addressed in any code of practice. Although most of these devices have passed several tests of experiments and time–history analysis they have rarely been examined as integral parts of actual buildings. Real buildings cannot be ideally recentered unless specifically designed and detailed for CP and PERR. Almost all simple beam–column joints, especially standard hinged supports absorb small but sufficient amounts of residual strains that hinder PERR. The proposed methodology is introduced by way of developing an earthquake‐resilient rocking core‐moment frame, as the lateral resisting component of a gravity resisting structure that has been detailed not to develop residual effects while sustaining large lateral deformations.     

A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies

New technologies have emerged to estimate the travel time on freeways by matching certain unique identifications of passing vehicles at different locations. These types of technologies share many similarities despite having different mechanisms. In this article, a generic method is presented to estimate freeway travel times using vehicle ID‐matching technologies. In particular, the new method addresses two long‐standing challenges: outlier screening and travel time estimation. Innovations include (1) using both statistical methods and traffic flow theory to screen outliers; and (2) accounting for mechanisms of various equipment measurement errors. The effectiveness of the proposed method is demonstrated using simulation and shown to be more accurate and responsive to travel time changes than methods based on the use of traditional inductive loops.     

Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring are costly and inefficient because of the number of inspectors and sensors required. To date, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has become popular, but UAVs require skilled pilots or a global positioning system (GPS) for autonomous flight. Unfortunately, GPS cannot be used by a UAV for autonomous flight near some parts of certain structures (e.g., beneath a bridge), but these are the critical locations that should be inspected to monitor and maintain structural health. To address this difficulty, this article proposes an autonomous UAV method using ultrasonic beacons to replace the role of GPS, a deep convolutional neural network (CNN) for damage detection, and a geo‐tagging method for the localization of damage. Concrete cracks, as an example of structural damage, were successfully detected with 97.7% specificity and 91.9% sensitivity, by processing video data collected from an autonomous UAV.     

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.     

Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network

The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel‐perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F‐measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F‐measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.     

Field‐to‐laboratory analysis of clay wall coatings as passive removal materials for ozone in buildings

Ozone reacts readily with many indoor materials, as well as with compounds in indoor air. These reactions lead to lower indoor than outdoor ozone concentrations when outdoor air is the major contributor to indoor ozone. However, the products of indoor ozone reactions may be irritating or harmful to building occupants. While active technologies exist to reduce indoor ozone concentrations (i.e, in‐duct filtration using activated carbon), they can be cost‐prohibitive for some and/or infeasible for dwellings that do not have heating, ventilating, and air‐conditioning systems. In this study, the potential for passive reduction of indoor ozone by two different clay‐based interior surface coatings was explored. These coatings were exposed to occupied residential indoor environments and tested bimonthly in environmental chambers for quantification of ozone reaction probabilities and reaction product emission rates over a 6‐month period. Results indicate that clay‐based coatings may be effective as passive removal materials, with relatively low by‐product emission rates that decay rapidly within 2 months.     

A Cell‐Based Model for Multi‐class Doubly Stochastic Dynamic Traffic Assignment

Abstract: This article proposes a cell‐based multi‐class dynamic traffic assignment problem that considers the random evolution of traffic states. Travelers are assumed to select routes based on perceived effective travel time, where effective travel time is the sum of mean travel time and safety margin. The proposed problem is formulated as a fixed point problem, which includes a Monte–Carlo‐based stochastic cell transmission model to capture the effect of physical queues and the random evolution of traffic states during flow propagation. The fixed point problem is solved by the self‐regulated averaging method. The results illustrate the properties of the problem and the effectiveness of the solution method. The key findings include the following: (1) Reducing perception errors on traffic conditions may not be able to reduce the uncertainty of estimating system performance, (2) Using the self‐regulated averaging method can give a much faster rate of convergence in most test cases compared with using the method of successive averages, (3) The combination of the values of the step size parameters highly affects the speed of convergence, (4) A higher demand, a better information quality, or a higher degree of the risk aversion of drivers can lead to a higher computation time, (5) More driver classes do not necessarily result in a longer computation time, and (6) Computation time can be significantly reduced by using small sample sizes in the early stage of solution processes.