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.

*Physics-based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising*; *however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data-driven state-dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride-induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities*.

The emerging taxi services, for instance, Uber and Lyft, are challenging traditional fully regulated taxi markets. Transportation agencies are spending significant efforts to understand the optimal pricing and fleet size taxis that are efficient for a given urban area. This study develops a modeling framework for studying a decentralized equilibrium based market study where the fare is strictly regulated by a taxi commission. The nature of demand-supply equilibria with stochastic demand are discussed to determine optimal development strategies, for instance, number of issued licenses and fare setting. Two Stackelberg games are formulated to specify leader-follower relationships between transportation authorities and and the followers — taxi drivers and passengers. An iterative approach is designed to simulate the games and solve corresponding mathematical optimization problems. The case study is based on New York City data which shows that the taxi market may be oversupplied and underpriced, which confirms findings from other studies and price hikes in 2012. Furthermore, different development strategies are proposed based on two Stackelberg games to respond to intended taxi system changes, such as price and quantity elasticity of taxi demand, levels of demand variance, average taxi operation speed, passengers' waiting time value, and taxi service coverage. The results have important implications in determining development strategies for taxi industry with emerging taxi services, stochastic demand, and the rapidly changing environment.

In real conditions, decision makers usually deal with multiple objectives and should make a decision in a state of certainty or uncertainty. The selection of the best constructions for a building from a number of alternatives is of great importance for owners, contractors, and stakeholders. Dozens of multicriteria/multi-attribute decision-making (MCDM/MADM) models developed for evaluating the performance of the available alternatives can be used for selecting the most suitable alternative from a given finite set of options based on a set of attributes. A guide to systematic selection among the available alternatives of building structures is the integrated methodology, thoroughly analyzed in the article. The article presents a MCDM model for selecting the type of foundation for a single-storey dwelling house based on the WASPAS-G (Weighted Aggregated Sum Product Assessment) method and Analytic Hierarchy Process (AHP) approach. The aggregate criteria weights are determined by using the AHP and experts’ judgement methods.

A Bayesian network model is developed, in which all the items or elements encountered when travelling a railway line, such as terrain, infrastructure, light signals, speed limit signs, curves, switches, tunnels, viaducts, rolling stock, and any other element related to its safety are reproduced. Due to the importance of human error in safety, especial attention is given to modeling the driver behavior variables and their time evolution. The sets of conditional probabilities of variables given their parents, which permits quantifying the Bayesian network joint probability, are given by means of closed formulas, which allow us to identify the particular contribution of each variable and facilitate a sensitivity analysis. The probabilities of incidents affecting safety are calculated so that a probabilistic safety assessment of the line can be done and its most critical elements can be identified and sorted by importance. This permits improving the line safety and saving time and money in the maintenance program by concentrating on the most critical elements. To reduce the complexity of the problem, an original method is given that permits dividing the Bayesian network in to small parts such that the complexity of the problem becomes linear in the number of items and subnetworks. This is crucial to deal with real lines in which the number of variables can be measured in thousands. In addition, when an accident occurs the Bayesian network allows us to identify its causes by means of a backward inference process. The case of the real Palencia–Santander line is commented on and some examples of how the model works are discussed.

*An optimization model is developed to guide recovery of a disrupted water distribution system. The model minimizes the total cost of recovery, including the disruption cost of unmet demand during the repair process and the repair cost itself. The optimization schedules repair tasks under precedence and resource constraints and contains an embedded flow problem that optimizes the distribution of water in each time period, given the state of the network. A simulated annealing algorithm is developed for scheduling the tasks, with the embedded flow problem solved using a generalized reduced gradient method. Experiments with a test water distribution system confirm the effectiveness of the model and provide insight regarding the effects of limited resources available for recovery and of the usefulness of having multiple modes for execution of specific tasks*.

An infrastructure adapted to dynamic wireless recharging of electric vehicles is often referred to generically as Electric Road (“e-road”). E-roads are deemed to become essential components of future grid environments and smart city strategies. Several technologies already exist that propose different ways to integrate dynamic inductive charging systems within the infrastructure. One e-road solution uses a very thin rail with box-section made of fibre-reinforced polymer, inside which an electric current flows producing a magnetic field. In spite of the great interest and research generated by recharging technologies, the structural problems of e-roads, including vibrations and structural integrity in the short and/or long period, have received relatively little attention to date. This article presents a novel computational methodology for assessing the time-dependent structural performance of e-roads, including a recursive strategy for the estimation of the lifetime of surface layers. The article also reports some numerical findings about e-roads that will drive further numerical analyses and experimental studies on this novel type of infrastructure. Finally, numerical simulations have been conducted to compare an e-road with a traditional road (“t-road”), in terms of static, dynamic and fatigue behavior.

The growing use of composite materials on aircraft structures has attracted much attention for the impact monitoring as a kind of structural health monitoring method. Uniform linear sensor array (ULSA)-based multiple signal classification (MUSIC) technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, the monitoring range of ULSA-based MUSIC method is 0°–180°, and its beamforming properties degrade at angles close to 0° and 180°. Besides, the ULSA-based MUSIC methods proposed require the knowledge of the direction dependent velocity profile obtained by additional experiments. This article presents a novel two-dimensional (2-D) plum-blossom sensor array (PBSA)-based MUSIC method. First, the velocity propagating at the specific direction is estimated by impact signal itself using PBSA directly. Second, 2-D PBSA-based MUSIC method well realizes omnidirectional 0°–360° impact localization of composite structures. Experimental results show its successful performance on epoxy laminate plate and complex composite structure.

The extreme importance of emergency response in complex buildings during natural and human-induced disasters has been widely acknowledged. In particular, there is a need for efficient algorithms for finding safest evacuation routes, which would take into account the 3-D structure of buildings, their relevant semantics, and the nature and shape of hazards. In this article, we propose algorithms for safest routes and balanced routes in buildings, where an extreme event with many epicenters is occurring. In a balanced route, a trade-off between route length and hazard proximity is made. The algorithms are based on a novel approach that integrates a multiattribute decision-making technique, Dijkstra's classical algorithm and the introduced hazard proximity numbers, hazard propagation coefficient and proximity index for a route.

Existing computer models used to optimize railway timetables lead to a high complexity when the number of analyzed services exceeds a given threshold. A time partitioning technique is proposed which allows line design and timetable optimization and a reduction in the complexity of the problem by considering small time windows of the same or different durations in which the timetables of a small (equal or not) number of running trains are optimized in sequence. Though the optimal solution is not expected to be attained with this method, the analyzed examples demonstrate that the resulting solution is close to the global optimum and practically satisfactory. This technique can be used at the planning and implementation stages. Examples of two real lines are analyzed to show the goodness of the proposed methods. One is the network Madrid–Sevilla–Toledo–Málaga–Valencia–Albacete with a dense traffic of 170 trains per day. The second is the Palencia–Santander line with 70 trains in which as an alternative to a double-track proposal with a cost of M€ 3,200 an alternate double–single-track (ADST) solution, with a cost of M€ 330 (one tenth) is proposed.

This article defines, formulates, and solves a new equilibrium traffic assignment problem with side constraints—the traffic assignment problem with relays. The relay requirement arises from the driving situation that the onboard fuel capacity of vehicles is lower than what is needed for accomplishing their trips and the number and distribution of refueling infrastructures over the network are under the expected level. We proposed this problem as a modeling platform for evaluating congested regional transportation networks that serve plug-in electric vehicles (in addition to internal combustion engine vehicles), where battery-recharging or battery-swapping stations are scarce. Specifically, we presented a novel nonlinear integer programming formulation, analyzed its mathematical properties and paradoxical phenomena, and suggested a generalized Benders decomposition framework for its solutions. In the algorithmic framework, a gradient projection algorithm and a labeling algorithm are adopted for, respectively, solving the primal problem and the relaxed master problem—the shortest path problem with relays. The modeling and solution methods are implemented for solving a set of example network problems. The numerical analysis results obtained from the implementation clearly show how the driving range limit and relay station location reshape equilibrium network flows.

An on-site earthquake early warning system (EEWS) can provide more lead-time at regions that are close to the epicenter of an earthquake because only seismic information of a target site is required. Instead of leveraging the information of several stations, the on-site system extracts some P-wave features from the first few seconds of vertical ground acceleration of a single station. It then predicts the intensity of the forthcoming earthquake at the same station according to these features. However, the system may be triggered by some vibration signals that are not caused by an earthquake or by interference from electronic signals, which may consequently result in a false alarm at the station. Thus, this study proposes two approaches to distinguish the vibration signals caused by non-earthquake events from those caused by earthquake events based on support vector classification (SVC) and singular spectrum analysis (SSA). In the first approach (Approach I), the fast Fourier transform algorithm and the established SVC model are employed to classify the vibration signals. In the second approach (Approach II), a SSA criterion is added to Approach I for the purpose of identifying earthquake events that are classified as non-earthquake events by the SVC model with increased accuracy. Both approaches are verified by using data collected from the Taiwan Strong Motion Instrumentation Program and EEW stations of the National Center for Research on Earthquake Engineering. The results indicate that both of the proposed approaches effectively reduce the possibility of false alarms caused by an unknown vibration event.

The three-dimensional mapping of the built environment is of particular importance for engineering applications such as monitoring work-in-progress and energy performance simulation. The state-of-the-art methods for fitting primitives, non-uniform B-Spline surface (NURBS) and solid geometry to point clouds still fail to account for all the topological variations or struggle with mapping of physical space to parameter space given unordered, incomplete, and noisy point clouds. Assuming an input of points that can be described by a single non-self-intersecting NURBS, this article presents a new method that leverages segmented point clouds and outputs NURBS surfaces. It starts by successively fitting uniform B-Spline curves in two-dimensional as planar cross-sectional cuts on each surface. An intermediate B-Spline surface is then computed by globally optimizing and lofting over the cross-sections. This surface is used to parameterize the points and perform final refinement to a NURBS. For cylindrical segments such as pipes, a new supervised method is also introduced to string the fitted segments, identify connection types, standardize the connections, and then refine them using NURBS optimization. Experimental results show the applicability of the proposed methods for as-built modeling purposes.

In this article, an accurate method for the registration of point clouds returned by a 3D rangefinder is presented. The method modifies the well-known iterative closest point (ICP) algorithm by introducing the concept of deletion mask. This term is defined starting from virtual scans of the reconstructed surfaces and using inconsistencies between measurements. In this way, spatial regions of implicit ambiguities, due to edge effects or systematical errors of the rangefinder, are automatically found. Several experiments are performed to compare the proposed method with three ICP variants. Results prove the capability of deletion masks to aid the point cloud registration, lowering the errors of the other ICP variants, regardless the presence of artifacts caused by small changes of the sensor view-point and changes of the environment.

*A vehicle equipped with a vehicle-to-vehicle (V2V) communications capability can continuously update its knowledge on traffic conditions using its own experience and anonymously obtained travel experience data from other such equipped vehicles without any central coordination. In such a V2V communications-based advanced traveler information system (ATIS), the dynamics of traffic flow and intervehicle communication lead to the time-dependent vehicle knowledge on the traffic network conditions. In this context, this study proposes a graph-based multilayer network framework to model the V2V-based ATIS as a complex system which is composed of three coupled network layers: a physical traffic flow network, and virtual intervehicle communication and information flow networks. To determine the occurrence of V2V communication, the intervehicle communication layer is first constructed using the time-dependent locations of vehicles in the traffic flow layer and intervehicle communication-related constraints. Then an information flow network is constructed based on events in the traffic and intervehicle communication networks. The graph structure of this information flow network enables the efficient tracking of the time-dependent vehicle knowledge of the traffic network conditions using a simple graph-based reverse search algorithm and the storage of the information flow network as a single graph database. Further, the proposed framework provides a retrospective modeling capability to articulate explicitly how information flow evolves and propagates. These capabilities are critical to develop strategies for the rapid flow of useful information and traffic routing to enhance network performance. It also serves as a basic building block for the design of V2V-based route guidance strategies to manage traffic conditions in congested networks. Synthetic experiments are used to compare the graph-based approach to a simulation-based approach, and illustrate both memory usage and computational time efficiencies*.

Adaptable active control strategies besides advance sensors and actuators technologies lead to higher performance of vibrational control in civil infrastructures under severe ground motions. These resilience control systems are robust against model uncertainties as well as being online recoverable from the malfunctioning of sensors and actuators. In this study, resilient control system based on sliding mode (SM) fault detection observer and SM fault tolerant control is improved for actuator fault in large-scale systems. The SM fault detection observer is modified for eliminating the excessive chattering in estimating states and actuators’ fault, and the reconfigurable SM fault tolerant control is improved to minimizing input forces in control framework under seismic action. Design of observer and controller is performed using linear matrix inequalities. Numerical simulations on the cable-stayed bridge benchmark demonstrate the effectiveness of the proposed fault-tolerant system. Despite the high order of this large-scale structure, the proposed fault detection and diagnosis method can effectively find the location and size of faults in actuators without performance degradation and computational costs. The fault-tolerant controller maintains the performance of the structure at an acceptable level in the post-fault case by redistribution of control signal to actuators.

Operation and maintenance of an infrastructure system rely on information collected on its components, which can provide the decision maker with an accurate assessment of their condition states. However, resources to be invested in data gathering are usually limited and observations should be collected based on their Value of Information (VoI). Assessing the VoI is computationally intractable for most applications involving sequential decisions, such as long-term infrastructure maintenance. In this article, we propose an approach for integrating adaptive maintenance planning based on Partially Observable Markov Decision Process (POMDP) and inspection scheduling based on a tractable approximation of VoI. Two alternative myopic approaches, namely pessimistic and optimistic, are introduced, and compared theoretically and by numerical examples.

Dealing with short-term deformations and the tension-stiffening effect in reinforced concrete (RC), the current study consists of two parts as presented in two separate manuscripts. Based on the test data of more than 300 RC ties, alternative tension-stiffening relationships of different complexity were proposed in the first article (Part I). In the companion manuscript (Part II), a stochastic modeling technique for assessing the deformation response of RC elements, subjected to different combinations of tension and flexure, is proposed. Based on stochastic principles, this technique allows not only to predict the average deformation response, but also to establish bounds of these predictions that are of vital importance for practical problems. The proposed technique is verified with the help of independent test data in order to validate the accuracy of the predictions of deformation, using the tension-stiffening models proposed in Part I of the article. Test specimens with different arrangements of steel or GFRP bars in the tensile zone were considered. The analysis has revealed that the influence of the degree of sophistication of the tension-stiffening models on the analysis results is smaller than the one of an adequate assessment of the shrinkage effect. The prediction accuracy is also related to the specific arrangement of reinforcement.

This article presents a new simulation approach for multidestination pedestrian crowds in complex environments. The work covers two major topics. In the first part, a novel cellular automaton (CA) model is proposed. The model describes the pedestrian movement by a set of simple rules and produces fundamental diagrams similar to those derived from laboratory experiments. The second topic of this work describes how the CA can be integrated into an iterative learning cycle where the individual pedestrian can adapt travel plans based on experiences from previous iterations. Depending on the setup, the overall travel behavior moves either toward a Nash equilibrium or the system optimum. The functional interaction of the CA with the iterative learning approach is demonstrated on a set of transport paradoxes. Furthermore, time series of speed and density observed in a small-scale experiment show a general agreement between the CA simulation and laboratory experiments. The scalability of the proposed approach is demonstrated on a large-scale scenario.

*A model-reference health monitoring algorithm with two damage sensitive features is presented in this study, utilizing structural acceleration measurements from earthquake-damaged building structure. A virtual linear healthy model, representing linear behavior of the instrumented structure, is used to generate real-time reference response signals for health monitoring during a disastrous earthquake. The tracking error of acceleration and a relevant statistical factor are first proposed for identifying damage occurrence and location at story level. The severity of the hysteretic damage is estimated numerically using a model-based prediction curve in an equivalent stiffness reduction manner with the implementation of robust Kalman filtering. The performance of damage detection and evaluation in the presented algorithm are illustrated by numerical simulation of structural models with different hysteretic characteristics, and further validated by experimental investigation employing a base-isolated three-story structure and real-world case study of a seven-story frame structure. The influence of measurement noise and uncertain stiffness in linear healthy model is also discussed through a parametric study*.

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.