Search

The vast number of deteriorated bridge infrastructure highlights the importance of implementing efficient and timely maintenance and rehabilitation strategies. Non-contact testing (NCT) technologies are potential tools for bridge condition assessment, facilitating fast and easy measurement with minimal traffic disruption, compared to conventional methods. The foremost objective of this study is to conduct a comprehensive review of the common NCT technologies for concrete bridge condition monitoring including defect detection, rebar corrosion, delamination, and cracking. Four NCT technologies, namely, ground penetrating radar, close-range photogrammetry, infrared thermography, and terrestrial laser scanning have been identified and analysed in depth. The review compiled one hundred and thirty-five laboratory and field studies, spanning from 2000 to 2019, on non-invasive assessment of bridge condition monitoring. In addition to comparison of stand-alone NCT methods, the study analysed integrated NCT systems, data acquisition tools, and processing and simulation platforms. Additionally, challenges and future perspectives of the reviewed NCT technologies are highlighted. Unlabelled Image • Critically analysed 135 studies discussing NCT technologies in bridge monitoring • Reviewed GPR, IRT, CRP, and TLS as NCT technologies in bridge monitoring • Highlighted integration of NCT technologies and its future potential • Identified relevant data acquisition tools and simulation platforms • Proposed further research to improve NCT accuracy and data processing capability [ABSTRACT FROM AUTHOR]

An enhanced version of the sigma–delta background estimation method for vehicle detection is proposed. A selective updating of the background model at the pixel level is included to deal with slow moving or temporarily stopped vehicles. The proposed algorithm is compared with some previous enhancements of the sigma–delta method, showing its effectiveness in urban traffic scenes. [ABSTRACT FROM AUTHOR]

Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L1-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L1-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L1-norm constraint for filter-divergence suppression. [ABSTRACT FROM AUTHOR]

• A hybrid observer merging PWSL modeling and Kalman filter estimator is proposed • Two new kNN-based mechanisms for detecting road traffic congestion are designed. • Measurements from the four-lanes SR-60 freeway in California are used for validation. • The detection results show the superior performance of the new coupled kNN-ES mechanism. A systematic approach for monitoring road traffic congestion is developed to improve safety and traffic management. To achieve this purpose, an improved observer merging the benefits of a piecewise switched linear traffic (PWSL) modeling approach and Kalman filter (KF) is introduced. The PWSL-KF observer is utilized as a virtual sensor to emulate the traffic evolution in free-flow mode. In the proposed approach, residuals from the PWSL-KF model are used as the input to k-nearest neighbors (kNN) schemes for congestion detection. Here, kNN-based Shewhart and exponential smoothing schemes are designed for discovering the traffic congestions. The proposed detectors merge the desirable properties of kNN to appropriately separating normal from abnormal features and the capability of the monitoring schemes to better identify traffic congestions. In addition, kernel density estimation has been utilized to set nonparametric control limits of the proposed detectors and compared them with their parametric counterparts. Tests on traffic measurements from the four-lane State Route 60 in California freeways show the effectiveness of the PWSL-KF-based kNN methods in supervising traffic congestions. [ABSTRACT FROM AUTHOR]

A multi-scale modelling system was developed to provide hourly NO x concentration fields at a building-resolving scale in the urban area of Modena, a city in the middle of the Po Valley (Italy), one of the most polluted areas in Europe. The WRF-Chem model was applied over three nested domains and employed with the aim of reproducing local background concentrations, taking into account meteorological and chemical transformation at the regional scale with nested resolutions of 15 km, 3 km and 1 km. Conversely, the PMSS modelling system was applied to simulate 3D air pollutant dispersion, due to traffic emissions, with a very high-resolution (4 m) on a 6 km × 6 km domain covering the city of Modena. The methodology employed to account for anthropogenic emissions relies on two different strategies. Traffic emissions were based on a bottom-up approach using emission factors suggested by the European Environmental Agency with traffic fluxes estimated by the PTV VISUM model in the urban area of Modena, combined with direct traffic flow measurements performed between October 28 and November 8, 2016 which was used for the hourly vehicle modulation. Other anthropogenic emissions were taken from the TNO-MACC III inventory at the scales resolved by the WRF-Chem model. Simulations were performed for the same period whereby the traffic measurement campaign was carried out. 2 m temperature and 10 m wind speed were captured quite well by the WRF-Chem model with statistical metrics in line with similar case studies related to the Northern Italy. The NO x concentrations reproduced in the Po Valley area by WRF-Chem were on average simulated reasonably well with a general negative bias in almost all the examined rural background monitoring stations. Additionally, the deployment of an emission inventory at the original resolution (7 km) highlighted that increasing resolution from 3 km to 1 km does not generally improve the model performance. Nevertheless, simulated and observed NO x hourly concentrations in the urban area of Modena exhibit a large agreement in particular for urban traffic site where detailed traffic emission estimations proved to be very successful in reproducing the observed NO x trend. At urban background stations, despite a general underestimation of the observed concentrations, the combination of WRF-Chem with PMSS provided daily pattern in line with observations. The analysis of the modelled NO x daily cycle pointed out also that at both traffic and background urban stations the morning NO x peak concentration was on average underestimated. This could be explained with an overestimation of mixing phenomena between 07:30 a.m. and 10:00 a.m. by WRF-Chem which leads to a greater dispersion of NO x along the vertical and thus a morning underestimation. The statistical analysis showed finally that PMSS combined with WRF-Chem at both the resolutions (3 km and 1 km) and at both traffic and background sites fulfilled standard acceptance criteria for urban dispersion model evaluation, confirming that the proposed multi-modelling system can be employed as a tool to support environmental policies, epidemiological studies and urban mobility planning. A hybrid modeling system composed by the WRF-Chem model and the Parallel Micro SWIFT and SPRAY modelling suite was employed to estimate urban NO x concentrations at very high resolution (4m) in a real case study. Results show that the modeling system provides good accuracy in reproducing observations, particularly at the traffic reference site. Model performances are also compliant with validation criteria at both the urban traffic and the urban background reference stations. Image 1 • Application of a multi-scale approach to estimate urban NO x level. • Tailored traffic emissions by the combination of direct measurements and simulation. • Model performances for NO x fulfill the acceptance criteria for urban environments. • Better description of NO x in urban traffic than in urban background conditions. [ABSTRACT FROM AUTHOR]

Collection of Annual Average Daily Traffic (AADT) is of major importance for a number of applications in road transport urban and environmental studies. However, traffic measurements are undertaken only for a part of the road network with minor roads usually excluded. This paper suggests a methodology to estimate AADT in England and Wales applicable across the full road network, so that traffic for both major and minor roads can be approximated. This is achieved by consolidating clustering and regression modelling and using a comprehensive set of variables related to roadway, socioeconomic and land use characteristics. The methodological output reveals traffic patterns across urban and rural areas as well as produces accurate results for all road classes. Support Vector Regression (SVR) and Random Forest (RF) are found to outperform the traditional Linear Regression, although the findings suggest that data clustering is key for significant reduction in prediction errors. [ABSTRACT FROM AUTHOR]

Scene categorization is an indispensable technique in intelligent systems, such as scene parsing, video surveillance or autonomous driving. Considering traffic analysis under big data, in this paper, we propose scene categorization towards urban tunnel traffic based on image quality assessment. Specifically, the dataset is obtained through analyzing urban tunnel traffic incidents from 2016 to 2018. And we classify the traffic accidents in the big data environment. Then, the vehicles in the surveillance videos are extracted using conventional detector. The spatial information of vehicles in the image reflects the traffic situation. In order to encode such important information, we leverage the information clustering algorithm based on information entropy for image classification. Afterward, we establish a quality evaluation model based on each clustered images. The trained image quality assessment model will guide tunnel traffic classification and event analysis. The experimental results show the correct rate is more than 90%, and the overall detection effect is better than the k-modes algorithm and the Ng'k-modes algorithm. [ABSTRACT FROM AUTHOR]

Measuring traffic in real time is one of the main functionalities of Smart Cities. To reduce the costs of deployment and operation, traffic measurement with mobile devices has been widely studied. In this paper, a traffic monitoring system using mobile devices is proposed. The proposed algorithm has the advantage of having a very low computational cost, allowing most of the pre-processing to be done in the mobile device and therefore making possible the centralized collection of a massive number of measurements. The proposed system is composed of three algorithms; a map-matching algorithm to correct minor location errors, a Virtual Inductive Loop that estimates the traffic and a traffic data collector that aggregates the information from many devices and combines it with other information sources. The system has been tested in a real scenario, comparing its accuracy with a traditional traffic sensor, showing its accuracy. [ABSTRACT FROM AUTHOR]

A novel statistical model for real-time traffic flow prediction is proposed. In this study, a 3D Markov random field (MRF) is used to model the temporal dynamics of the traffic flow measured by VDS sensor network. Then, the spatial and temporal relations between roads at a given location are represented by the 3D graph using cliques, then its structure is determined by clique parameters. Here, a support vector regression is adopted for estimating the correlation parameters. The technique is applied to actual traffic flow data from Gyeongbu expressway, South Korea. The experiments demonstrate that the proposed method can predict the traffic flow with an accuracy of 85.6%, which improves 17.3% of the existing state-of-the-art method. [ABSTRACT FROM AUTHOR]

No abstract is available for this article. [ABSTRACT FROM AUTHOR]

Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates and diagnoses the traffic status and the time delay at intersections across whole urban road networks. This method is grounded on a massive amount of floating car data sampled at a rate of 3 s, and it is composed of three major parts. (1) A grid model is built to transform intersections into discrete cells, and the floating car data are matched to the grids through a simple assignment process. (2) Based on the grid model, a set of key traffic parameters (e.g., the total time delay of all the directions of the intersection and the average speed of each direction) is derived. (3) Using these parameters, intersections are evaluated and the ones with the longest traffic delays are identified. The obtained intersections are further examined in terms of the traffic flow ratio and the green time ratio as well as the difference between these two variables. Using the central area of Beijing as the case study, the potential and feasibility of the proposed method are demonstrated and the unreasonable signal timing phases are detected. The developed method can be easily transferred to other cities, making it a useful and practical tool for traffic managers to evaluate and diagnose urban signal intersections as well as to design optimal measures for reducing traffic delay and increase operation efficiency at the intersections. [ABSTRACT FROM AUTHOR]

The coastal environment plays a fundamental role in human life as it is characterised by the highest values of ecosystem services and by multiple uses that are often in conflict with each other. The coastal environment is also subject to anthropogenic stressors of various kinds that are correlated with the different uses in place and that increase its vulnerability. In order to acquire more detailed information on the sedimentation processes and on the degree of pollution, this work studied the sedimentary fluxes and the concentrations of Arsenic (As), Nickel (Ni) and Lead (Pb) associated with marine sediments in the Gulf of Gaeta (Latium coast, Italy). In particular, we tried to verify the influence of the anthropic activities in the area on the sediment fluxes, taking into account the natural solid contribution to the sea deriving from the coastal streams and the Garigliano river. Sediment fluxes were measured using a deposimetric station located near the commercial port of Gaeta, through monthly campaigns. This station allowed to deploy sediment traps at 6 different distances from the bottom. The total sedimentary flux in the area, discerning the amount of the primary flux (Fp) and the resuspension flux (Fr) with respect to the total flux (Ft) as well as the concentration of As, Ni and Pb in the sediment traps were measured monthly during the sampling campaigns. The measured sediment fluxes were also compared with the meteo-marine conditions (wind intensity, wave height), pluviometric measurements and maritime traffic. The comparison between the sediment fluxes and the concentration of the considered trace metals allowed to quantify over time the trace metals fluxes in the study area. The results obtained from the sediment analysis showed a moderate degree of contamination of As and Ni. The exceeding of the Effect Range Low (ERL) threshold described in the Sediment Quality Guidelines (SQGs) could be attributed to the influence of anthropogenic activities associated with the use of fossil fuels, maritime traffic and industrial activities in the study area. [ABSTRACT FROM AUTHOR]

Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN‐based approaches proposed for short‐term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is reviewed. An in‐depth discussion is provided to demonstrate how different types of NNs have been used for different aspects of short‐term traffic state prediction. Finally, possible further research directions are suggested for additional applications of NN models, especially using deep architectures, to address the dynamic nature in complex transportation networks. This article is categorized under: Technologies > PredictionTechnologies > Machine LearningApplication Areas > Science and Technology A neural network based model for traffic state prediction. [ABSTRACT FROM AUTHOR]

Vehicle driving path planning is an important information service in intelligent transportation systems. As an important basis for path planning optimization, the travel time prediction method has attracted much attention. However, traffic flow has features of high nonlinearity, time-varying, and uncertainty, which makes it hard for prediction method with single feature to meet the accuracy demand of intelligent transportation system in big data environment. In this paper, the historical vehicle Global Positioning System (GPS) information data is used to establish the traffic prediction model. Firstly, the Clustering in QUEst (CLIQUE)-based clustering algorithm V-CLIQUE is proposed to analyze the historical vehicle GPS data. Secondly, an artificial neural network (ANN)-based prediction model is proposed. Finally, the ANN-based weighted shortest path algorithm, A-Dijkstra, is proposed. We used mean absolute percentage error (MAPE) to evaluate the predictive model and compare it with the predicted results of Average and support regression vector (SRV). Experiments show that the improved ANN path planning model we proposed can accurately predict real-time traffic status at the given location. It has less relative error and saves time for users' travel while saving social resources. [ABSTRACT FROM AUTHOR]

Airborne particulate samples were collected from three main squares (Ramsis, El Giza, and Sphinx) representing heavy traffic areas in Greater Cairo during the period of December 2015–February 2016, and analysed for polycyclic aromatic hydrocarbon (PAHs). The maximum concentrations of particle-bound PAHs were observed at El Giza, while the minimum levels were recorded at Sphinx. The levels of particle-bound PAHs in the square areas of Greater Cairo are higher than those found in many different locations in Egypt and around the world.The distribution of individual particle-bound PAHs as well as PAH categories, depending on the ring number in Ramsis, El Giza, and Sphinx, wasquite similar. This similarity implies similar emission sources of PAHs in the three square areas, with vehicle exhaust emissions being the dominant one. Benzo[b]fluoranthene (BbF), benzo[ghi]perylene (BGP), and indeno[1,2,3-cd]pyrene(IND) were the most abundant PAH compounds. Diagnostic concentration ratios of PAH compounds in the three square locations suggest that both petrogenic and pyrogenic sources emit these compounds. Moreover, they originate mainly from traffic emissions in the study areas. Based on the calculated benzo[a]pyrene equivalent (BaPeq) for the individual particle-bound PAH compounds, health risks associated with the inhalation of these compounds were assessed. Total carcinogenic activity (TCA) for all measured PAHs represented 20.03% (El Giza), 20.40% (Ramsis), and 20.60% (Sphinx) of the total PAH concentrations. Benzo[a]pyrene (BaP) and dibenz[a,h]anthracene (DBA) were the highest contributors to the total health risks; these accounted for 42.72% and 38.50% (El Giza), 41.79% and 39.17% (Ramsis), and 42.92% and 37.78% (Sphinx) of the TCA of all PAH compounds, respectively. These results indicate the importance of BaP and DBA as surrogate compounds for PAHs in the atmosphere of square areas of Greater Cairo. [ABSTRACT FROM AUTHOR]

Problems of optimization in natural science, technology, economics, and management are of great interest. Students also usually find them intriguing. However, the selection of such problems in introductory undegraduate physics textbooks is very limited and should be broadened. To that end, a model of traffic flow optimization, simple enough for undegraduate students to analyze, is presented. Calculations of the main optimal parameters of cars’ flow on a one-lane freeway, based on this model, are compared with experimental data. [ABSTRACT FROM AUTHOR]

Presently there is quite few analytical studies in traffic flows due to the non-linearity of the governing equations. In the present paper we introduce travelling wave solutions for the homogeneous one-dimensional FREFLO model, which are expressed in the form of series and describe the procedure that vehicles/pedestrians move with a negative velocity and decelerate until rest, then accelerate inversely to positive velocities. This method is expect to be extended to more complex situations in the future. [ABSTRACT FROM AUTHOR]

Transportation planning requires the use of accurate traffic data to produce estimates of traffic volume predictions over time and space. The annual average daily traffic (AADT) data is an important component of transportation design, operation, policy analysis, and planning. The use of traffic volume forecasting models for the characterization, analysis, and estimation of transportation data has proven to be a useful method for reducing high costs, overcoming spatial constraints, and limiting the errors associated with data collection and analysis in transportation planning. The geostatistical kriging technique is a viable method for modeling and forecasting AADT. The degree to which the technique of kriging can be useful in forecasting AADT depends highly on an understanding of the decision-making variables, the relationship between the variables, and the practical limitations of the various kriging techniques and variogram models. This paper applied three different linear kriging techniques [simple kriging (SK), ordinary kriging (OK), and universal kriging (UK)] and five variogram models (nugget effect, spherical, exponential, Gaussian, and power) to characterize and interpolate the annual average daily traffic of Washington State. The statistical errors (i.e., mean error, root-mean-square error, average standard error, mean standardized error, and root-mean-square standardized error) of the resulting output were then compared to determine the most suitable combination of linear spatial interpolation and variogram algorithms for each of the data sets. Ordinary and/or universal kriging combined with the exponential variogram were the most appropriate methods for the 2008 AADT data set, whereas ordinary kriging combined with the spherical variogram and simple kriging combined with the spherical variogram were the most suitable methods for the 2009 and 2010 data sets, respectively. Results from this study suggest that using the same combination of kriging and variogram algorithms to characterize and interpolate different AADT data sets (2008, 2009, and 2010) could lead to suboptimal results. One reason for the lack of optimal results is for instance, the directional variation in the 2009 and 2010 data sets which undermine the assumption of mean stationarity in the use of ordinary kriging for modeling. [ABSTRACT FROM AUTHOR]

A cellular automaton model is proposed to consider the anticipation effect in drivers' behavior. It is shown that the anticipation effect can be one of the origins of synchronized traffic flow. With anticipation effect, the congested traffic flow simulated by the model exhibits the features of synchronized flow. The spatiotemporal patterns induced by an on-ramp are also consistent with the three-phase traffic theory. Since the origin of synchronized flow is still controversial, our work can shed some light on the mechanism of synchronized flow. [ABSTRACT FROM AUTHOR]