Thermo-physico-mechanical parameters of the numerical model.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"9971",leadTitle:null,fullTitle:"Data Science, Data Visualization, and Digital Twins",title:"Data Science, Data Visualization, and Digital Twins",subtitle:null,reviewType:"peer-reviewed",abstract:"Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. 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She also serves as the co-chair of BE\\'s Smart Cities and Infrastructure Cluster. Dr. Shirowzhan works as tomorrow\\'s leading champion for the Chartered Institute of Building (CIOB). Her research interests include sensing technologies, enhanced GIS, BIM, digital twins, and artificial intelligence in technologies pertinent to BE informatics. She teaches and supervises students at UNSW in the areas of GIS, BIM, digital twins, AI, machine learning, city analytics, urban informatics, smart cities, infrastructure, construction informatics, and other relevant topics. She now serves on the editorial boards of the journals MDPI and Advances in Civil Engineering. She is also a topic board member of the ISPRS International Journal of Geo-Information as well as Buildings. 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Various real-world applications have been using Scagnostics visual features to detect unusual bivariate data correlations. Concomitantly, many applications are required to be implemented on web platforms due to their accessibility and convenience. Therefore, this chapter discusses a recent JavaScript implementation of Scagnostics, an extension to higher dimensional data, and its applications in detecting abnormalities in bivariate and multivariate time series data. Its implementation in JavaScript supports the tremendous demand for visual features in the web environment. Likewise, its higher dimensional implementations allow generating Scagnostics features for the rapidly growing multivariate data. Finally, conventional ScagnosticsJS computations involve time-consuming algorithms, and they are sensitive to slight changes in the underlying data. Therefore, this chapter also discusses a recent attempt to tackle these issues using machine learning to estimate the Scagnostics scores.",signatures:"Vung Pham and Tommy Dang",downloadPdfUrl:"/chapter/pdf-download/76463",previewPdfUrl:"/chapter/pdf-preview/76463",authors:[{id:"330334",title:"Dr.",name:"Vung",surname:"Pham",slug:"vung-pham",fullName:"Vung Pham"},{id:"335450",title:"Dr.",name:"Tommy",surname:"Dang",slug:"tommy-dang",fullName:"Tommy Dang"}],corrections:null},{id:"75324",title:"Visualizing the Impact of COVID-19 in the Mobility Dynamics - A Dashboard Framework for Decision Support in Smart Cities",doi:"10.5772/intechopen.96295",slug:"visualizing-the-impact-of-covid-19-in-the-mobility-dynamics-a-dashboard-framework-for-decision-suppo",totalDownloads:189,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Being mobility one of the biggest challenge’s cities face today, the COVID-19 pandemic reinforced this challenge and caused a deep structural change in the mobility of the multilayered dynamic framework of Smart Cities. The need to supply decision support systems to city authorities is higher than ever. Planning and managing mobility in Smart Cities has become more challenging, as the amount of information available and the pressure to enforce sustainable and secure policies increases, stakeholders require faster and more targeted actions. Dashboards are powerful tools that can be used in this context to provide, in an understandable manner, multidimensional information otherwise unavailable in classically static visualizations, as these tools offer a reliable foundation for decision support systems. This chapter goes through the required visualization techniques used to produce meaningful dashboards, to both showcase spatial and temporal trends in the context of mobility in Smart Cities following the COVID-19 pandemic. A general framework for analyzing mobility patterns is suggested by gathering methods and techniques recently developed in the literature.",signatures:"Nuno Alpalhão, Miguel de Castro Neto and Marcel Motta",downloadPdfUrl:"/chapter/pdf-download/75324",previewPdfUrl:"/chapter/pdf-preview/75324",authors:[{id:"332638",title:"MSc.",name:"Nuno",surname:"Alpalhão",slug:"nuno-alpalhao",fullName:"Nuno Alpalhão"},{id:"333163",title:"MSc.",name:"Marcel",surname:"Motta",slug:"marcel-motta",fullName:"Marcel Motta"},{id:"333357",title:"Prof.",name:"Miguel",surname:"de Castro Neto",slug:"miguel-de-castro-neto",fullName:"Miguel de Castro Neto"}],corrections:null},{id:"75369",title:"3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems",doi:"10.5772/intechopen.96179",slug:"3d-point-cloud-based-tree-canopy-visualization-for-a-smart-deployment-of-mobile-communication-system",totalDownloads:248,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Mobile communication is one of the most important parameters of smart cities in terms of maintaining connectivity and interaction between humans and smart systems. However, In the deployment process of Mobile Communication Systems (MCS), Radio Frequency (RF) engineers use location depended empirical Signal Strength Path Loss (SSPL) models ending up with poor signal strength and slow data connection. This is due to the fact that empirical propagation models usually are restrained by the environment and do not implement state of the art technologies, including Unmanned Aerial Vehicles (UAV), Light Detection and Ranging (LiDAR), Image Processing, and Machine Learning to increase efficiency. Terrains involving buildings, hills, trees, mountains, and human-made structures are considered irregular terrains by telecommunication engineers. Irregular terrains, specifically trees, significantly affect MCS’s efficiency because of their complex pattern resulting in erroneous signal fading via multi-path reflection and absorption. Therefore, a virtual 3D environment is required to extract the required 3D terrain pattern and elevation data from the environment. Once this data is processed in the machine learning algorithm, an adaptive propagation model can be formed and can significantly improve SSPL prediction accuracy for MCS. This chapter presents 3D point cloud visualization via sensor fusion and 2D image color classification techniques, which lead to a novel propagation model for the smart deployment of MCS. The proposed system’s main contribution is to develop an intelligent environment that eliminates limitations and minimizes related signal fading prediction errors. In addition, having better connectivity and efficiency will resolve the communication problem of smart cities. The chapter also provides a case study that significantly outperforms other empirical models with an accuracy of 95.4%.",signatures:"Yunus Egi and Engin Eyceyurt",downloadPdfUrl:"/chapter/pdf-download/75369",previewPdfUrl:"/chapter/pdf-preview/75369",authors:[{id:"330309",title:"Assistant Prof.",name:"yunus",surname:"Egi",slug:"yunus-egi",fullName:"yunus Egi"},{id:"341040",title:"Dr.",name:"Engin",surname:"Eyceyurt",slug:"engin-eyceyurt",fullName:"Engin Eyceyurt"}],corrections:null},{id:"75296",title:"Digital Twin of the Mining Shaft and Hoisting System as an Opportunity to Improve the Management Processes of Shaft Infrastructure Diagnostics and Monitoring",doi:"10.5772/intechopen.96193",slug:"digital-twin-of-the-mining-shaft-and-hoisting-system-as-an-opportunity-to-improve-the-management-pro",totalDownloads:258,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"The following chapter presents a concept of a virtual model of a mine shaft equipped with a hoisting system for the purpose of improving the processes of diagnostics management of shaft infrastructure and its monitoring. The chapter presents a proposal of improvement of broadly known processes such as: diagnostics and monitoring of shaft infrastructure using digital models of 3D structures, the BIM and Digital Twin idea. Implementation of such systems in the operating mine working was presented together with expected results of monitoring. As the presented solution is currently only a concept, development of such system in real application is necessary to asses real benefits of application of Digital Twin system.",signatures:"Piotr Kalinowski, Oskar Długosz and Paweł Kamiński",downloadPdfUrl:"/chapter/pdf-download/75296",previewPdfUrl:"/chapter/pdf-preview/75296",authors:[{id:"318919",title:"Ph.D.",name:"Paweł",surname:"Kamiński",slug:"pawel-kaminski",fullName:"Paweł Kamiński"},{id:"343647",title:"MSc.",name:"Piotr",surname:"Kalinowski",slug:"piotr-kalinowski",fullName:"Piotr Kalinowski"},{id:"343648",title:"MSc.",name:"Oskar",surname:"Długosz",slug:"oskar-dlugosz",fullName:"Oskar Długosz"}],corrections:null},{id:"75258",title:"Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control",doi:"10.5772/intechopen.96347",slug:"using-trend-extraction-and-spatial-trends-to-improve-flood-modeling-and-control",totalDownloads:201,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Effective management of flood events depends on a thorough understanding of regional geospatial characteristics, yet data visualization is rarely effectively integrated into the planning tools used by decision makers. This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers. A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts. This prediction is then tied to a corresponding spatial flood inundation profile in a geographic information system (GIS) setting. The intersection of flood profile and affected road segments can be easily visualized and extracted. 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They present a wide and versatile metabolic and enzymatic diversity coupled with extraordinary physiological capacities in rich extreme environments. These enzymes and metabolites have been exploited to develop clean and sustainable industrial processes. Antibiotics, compatible solutes, and other compounds obtainable from these microbes are also finding a variety of uses. Recently, several investigations have been started to study the phylogenetic relationship between extremophilic microbes through the analysis of their genome sequences. Hence, comparative genomic analyses of genomes allowed the identification of distinctive genes and metabolic pathways involved in the extremophilic way of life. Such analyses provided key data that enabled us to understand gene functionality across organisms and environments and track the evolutionary events involved in environmental adaptations at the population and strain levels.
",isbn:"978-1-80356-819-5",printIsbn:"978-1-80356-818-8",pdfIsbn:"978-1-80356-820-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"9c39aa5fd22296ba53d87df6d761a5fc",bookSignature:"Dr. Afef Najjari",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11630.jpg",keywords:"Bacteria, Archaea, Fungi, Diversity, Adaptation, Metabolites, Extremozymes, Salinity, Temperature, Acidity, Osmoadaptation, Astrobiology",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 30th 2022",dateEndSecondStepPublish:"June 10th 2022",dateEndThirdStepPublish:"August 9th 2022",dateEndFourthStepPublish:"October 28th 2022",dateEndFifthStepPublish:"December 27th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Assistant Professor of Bioinformatics at the Faculty of Sciences of Tunisia, University of Tunis El Manar who worked on several topics including genetic and enzymatic diversities of lactic acid bacteria isolated from dairy and meat products and microbial diversity and ecology of extremophilic microbes in arid and saline ecosystems and mainly on archaeal groups.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"196823",title:"Dr.",name:"Afef",middleName:null,surname:"Najjari",slug:"afef-najjari",fullName:"Afef Najjari",profilePictureURL:"https://mts.intechopen.com/storage/users/196823/images/system/196823.jpeg",biography:"Dr. Afef Najjari is an Assistant Professor of Bioinformatics at the Faculty of Sciences of Tunisia, University of Tunis El Manar. Dr. Afef has worked on several topics including (i) genetic and enzymatic diversities of lactic acid bacteria isolated from dairy and meat products, and (ii) microbial diversity and ecology of extremophilic microbes in arid and saline ecosystems and mainly on archaeal groups. These works were funded by national and international projects. Currently, she is interested in metagenomic analysis, genome assemblies and annotations, transcriptomic data analysis (microarrays), and biological database development.",institutionString:"University of Tunis El Manar",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Tunis El Manar University",institutionURL:null,country:{name:"Tunisia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"13",title:"Immunology and Microbiology",slug:"immunology-and-microbiology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"117",title:"Artificial Neural Networks",subtitle:"Methodological Advances and Biomedical Applications",isOpenForSubmission:!1,hash:null,slug:"artificial-neural-networks-methodological-advances-and-biomedical-applications",bookSignature:"Kenji Suzuki",coverURL:"https://cdn.intechopen.com/books/images_new/117.jpg",editedByType:"Edited by",editors:[{id:"3095",title:"Prof.",name:"Kenji",surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3828",title:"Application of Nanotechnology in Drug Delivery",subtitle:null,isOpenForSubmission:!1,hash:"51a27e7adbfafcfedb6e9683f209cba4",slug:"application-of-nanotechnology-in-drug-delivery",bookSignature:"Ali Demir Sezer",coverURL:"https://cdn.intechopen.com/books/images_new/3828.jpg",editedByType:"Edited by",editors:[{id:"62389",title:"PhD.",name:"Ali Demir",surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"872",title:"Organic Pollutants Ten Years After the Stockholm Convention",subtitle:"Environmental and Analytical Update",isOpenForSubmission:!1,hash:"f01dc7077e1d23f3d8f5454985cafa0a",slug:"organic-pollutants-ten-years-after-the-stockholm-convention-environmental-and-analytical-update",bookSignature:"Tomasz Puzyn and Aleksandra Mostrag-Szlichtyng",coverURL:"https://cdn.intechopen.com/books/images_new/872.jpg",editedByType:"Edited by",editors:[{id:"84887",title:"Dr.",name:"Tomasz",surname:"Puzyn",slug:"tomasz-puzyn",fullName:"Tomasz Puzyn"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"58167",title:"Thermomechanical Time-Dependent Deformation and Fracturing of Brittle Rocks",doi:"10.5772/intechopen.72326",slug:"thermomechanical-time-dependent-deformation-and-fracturing-of-brittle-rocks",body:'Disposal of radioactive waste is currently considered as the preferred option to store and isolate this waste from biosphere over hundreds of thousands of years for waste management worldwide. In the geological disposal, the temperature in the near-field rock mass will increase due to the heat generated by the radioactive waste and in turn affect the time-dependent behavior of the host rock and consequently change the overall long-term performance of the disposal. Therefore, a good understanding of the time-dependent behavior of the host rock at elevated temperatures is of great significance to evaluate the stability of nuclear waste disposal.
Extensive effort has been made to investigate the time-dependent behavior of rocks like sedimentary rock and rock salt [1–4] and hard rocks like granite [5–10] and basalt [11]. Granite is a widely recognized potential host rock for the disposal of radioactive waste due to its low permeability and high strength [12]. Moreover, a good understanding of time-dependent deformation in granite will also assist in our understanding of crustal deformation and natural hazards since granite is a major constituent of the continental crust. The distribution of stress and permeability evolution in granite is of great importance when we analyze natural hazards and rock engineering projects. Despite the importance of granite in these high-temperature environments, very few studies of time-dependent brittle creep on granite have been made at elevated temperature [2, 8, 9, 13]. During a brittle creep experiment of rock at a constant differential stress for an extended period of time, the strain against time first decelerates before accelerating as macroscopic sample failure is approached [14]. The onset of the acceleration to failure in brittle creep experiments has been ascribed as the result of the sample reaching a microcrack density at which microcracks can interact and coalesce, sometimes referred to as the critical damage threshold [2, 11, 13, 15]. Time-dependent brittle deformation is often attributed to a mechanism of subcritical crack growth, namely stress corrosion cracking [14, 16]. Stress corrosion describes fluid-rock reactions that occur preferentially between a chemically active pore fluid and strained atomic bonds at the crack tips and therefore is sensitive to environmental factors such as stress, temperature, and pore fluid reactivity [16]. Temperature influences the crack growth rate through the Arrhenius temperature dependence of crack growth rate and because temperature affects the stress dependency of the rate of crack growth. Experimental tests have shown that speed of crack growth during experiments increases with an increase in temperature [14, 17, 18].
Due to the aforementioned importance of understanding the long-term mechanical behavior of rock for disposal of radioactive waste, here we present a two-dimensional constitutive creep model to describe the time-dependent deformation of granite at different temperatures and confining pressures. We first formulate the coupled thermomechanical time-dependent model. We then validate the model and determine the required thermo-physico-mechanical parameters with published experimental data [10, 19]. Finally, the results of brittle creep simulations at different temperatures, differential stresses, confining pressures, and sample homogeneities were presented and discussed.
In this chapter, a quantitative model is proposed to describe the coupled heat transfer and rock failure problems associated with rock exposed to elevated temperatures. For a model used to investigate time-dependent creep deformation at high temperature, the coupled effect of the medium deformation and heat transfer must be important. Three components including a heat transfer description, a stress description, and a failure description must be accounted for. The descriptions of heat transfer, stress, and failure in the model are, respectively, presented.
It is important to introduce material heterogeneity to reveal a collective macroscopic behavior different from that of the individual elements. The mechanical parameters such as uniaxial compressive strength and Young’s modulus of the mesoscopic material elements are assumed to be homogeneous and isotropic and are randomly assigned with a Weibull statistical distribution (Eq. (1)) [19] to reflect the material heterogeneity at a mesoscale:
in which
in which
Figure 1 shows two numerical standard rock specimens with a homogeneity index
Numerical specimens with a geometry of 100 mm in length and 50 mm in width at a homogeneity index
It is assumed that the conductive flux is saturated at a value comparable to the enthalpy per unit volume. The heat conduction equation in a medium is known as Fourier’s law:
in which
It is assumed that thermal equilibrium between the phases and heat conduction as the dominant mechanism of heat transfer and the energy balance equation in an anisotropic medium can be expressed as
in which
Assuming the total strain for a stressed medium is made up of elastic, creep, and thermal components. The total strain can be decomposed:
in which the subscripts
in which
in which
where
For creep problems, a Norton-Bailey equation [4] known as a constitutive law of the creep strain rate was adopted to characterize time-dependent creep deformation based on the approach of the equation of state theory:
where
Equation (10) can also be expressed in a strain rate form because the strain rate is of great interest for our study:
Creep flow rule under multiaxial stress conditions can be expressed in tensor form:
in which
where
This creep model can describe the decelerating creep commonly seen in laboratory [14], but it fails to capture the acceleration in strain rate in the approach to macroscopic sample failure. Thus, a damage evolution law for the accelerating creep of rock is incorporated at this stage.
The static stress equilibrium equation can be expressed in tensor form:
in which
in which
in which
Thus, the final governing equation of heat transfer in a medium can be written in displacement form:
An elastic damage constitutive law is incorporated in the model to describe its stress-strain relationship. The elastic modulus for an isotropic elastic medium is expressed:
where
According to damage mechanics theory, the Young’s modulus of an element will degrade according to Eq. (19) when the stress on the element exceeds a damage threshold. The stress-strain curve of the element is considered linear elastic with a constant residual strength until the given damage threshold is reached. The elastic damage constitutive law of each element under uniaxial stress condition is shown in Figure 2. The maximum tensile stress criterion and modified Mohr-Coulomb criterion are chosen as the damage thresholds to determine whether any elements are damaged in tension or shear, respectively:
where
Elastic damage constitutive law for element under uniaxial compression and tension.
The damage variable D according to the constitutive law as shown in Figure 2 can be described.
where
In this section, the proposed model was calibrated with experimental data [10, 18, 21] to determine appropriate input parameters. Mechanical data from uniaxial and triaxial constant displacement rate experiments at room temperature were used to obtain the physico-mechanical input parameters at the mesoscale. The geometry of the granite samples in laboratory tests was 100 mm in length and 50 mm in width. The compressive strength of air-dried Beishan granite under confining pressures of 0, 1, and 5 MPa is 165.2, 174, and 216.8 MPa, respectively. The Young’s modulus and Poisson’s ratio of air-dried Beishan granite in uniaxial compression are 43 GPa and 0.25, respectively. The randomly generated numerical samples were discretized into 20,000 square elements. The size of the modeled sample was kept the same for all numerical simulations in the present paper. A suite of unconfined and confined compressive tests on the modeled rock samples were performed at constant room temperature and constant confining pressures of 0, 1, and 5 MPa. The physico-mechanical input parameters of the individual elements at a mesoscale used in the simulations were listed in Table 1.
Input parameters | Granite |
---|---|
Homogeneity index | 5 |
Mean elastic modulus (GPa) | 43 |
Mean uniaxial compressive strength (MPa) | 350 |
Poisson’s ratio | 0.25 |
Frictional angle (°) | 30 |
Ratio of uniaxial compressive to tensile strength | 10 |
Axial stress (MPa) | 150 |
Specific heat capacity (J/(kg K)) | 900 |
Coefficient of linear thermal expansion (1/K) | 4.6 × 10−6 |
Thermal conductivity (W/(m K)) | 3.48 |
6.8 × 10−11 | |
1.75 | |
0.39 | |
3000 |
Thermo-physico-mechanical parameters of the numerical model.
The numerical and experimental stress-strain curves for the granite simulations are plotted in Figure 3. It can be seen from Figure 3 that the simulated stress-strain curves are in good agreement with the experimental stress-strain curves. It needs to be noted that the nonlinear behavior at the early beginning of the experimental stress-strain curves is a result of the closure of preexisting compliant microcracks. This nonlinearity is not replicated in the present model because the stress-strain behavior of an element is considered linear elastic until the given damage threshold is attained. We highlight that the model input parameters were the same for each of the simulations in Figure 3, adding confidence that the model is capable of accurately capturing the short-term mechanical behavior of Beishan granite under different confining pressures.
Comparison between numerical and experimental stress-strain curves for granite.
The numerical model was validated via a successful attempt to replicate published uniaxial and triaxial experimental data for Beishan granite. We will now run a suite of conventional brittle creep simulations on Beishan granite under different constant temperatures of 23, 50, and 90°C in order to find the required thermo-physico-mechanical properties of the granite. These thermophysical parameters were determined from the experimental data, and material constants
The numerically simulated creep curves together with the experimental creep curves are plotted in Figure 4. It can be seen that the simulated creep curves agree well with the experimental curves. The numerical creep curves clearly capture the phenomenology of brittle creep: the strain rate first decelerates followed by an acceleration in strain rate prior to final macroscopic failure.
Comparisons between numerical and experimental creep curves under uniaxial conditions (σx = 0 MPa) at constant temperatures of 23, 50, and 90°C.
Figure 5 presents the snapshots of the damage evolution of the numerical specimens deformed at constant temperatures of 23, 50, and 90°C. Figure 5 shows when and where damage and failure occur in the numerical specimen. The value of Young’s modulus is randomly distributed since the numerical rock samples are heterogeneous. Therefore, the elements with a low Young’s modulus act as nucleation sites for damage. As time goes on, these damaged elements grow and form localized damage zones. The localized damaged zones alter the stress field in their surrounding region, and these alterations further trigger the dynamic extension of the damage zone. Eventually, a thoroughgoing macroscopic fracture forms that signals the macroscopic failure of the sample.
Snapshots of the simulated failure process during the brittle creep.
Chen et al. [18] also performed a series of multistep creep experiments to investigate the influence of temperature and stress on the time-dependent behavior of Beishan granite. Multistep creep tests were performed at confining pressures of 0, 1, and 5 MPa and at temperatures of 23°C (room temperature) and 90°C (the maximum temperature on the canister surface according to the current disposal conceptual design in China), respectively. The stress applied on the sample during the experiments was increased stepwise to predetermined percentages of the average peak stress (20, 40, 60, and 80%). The sample was kept at each level of stress for 1 week. We also performed numerical multistep creep simulations under the same conditions to further validate our model. It is noted that the numerical multistep creep simulations were all performed using the determined physico-mechanical and thermophysical parameters listed in Table 1. During the simulations, the rock samples are fixed in the vertical direction but can move freely in the horizontal direction.
The numerically simulated multistep creep curves together with the experimental multistep creep curves are plotted in Figure 6. It can be seen from Figure 6 that the simulated multistep creep curves are in good agreement with the experimental curves. In detail, the model captures the influence of both confining pressure and temperature on the mechanical behavior, and the strain at the different stress steps and the time to failure are very similar between the experiment and the model (Figure 6).
Multistep creep curves for simulations performed under different confining pressures (σx = 0, 1, and 5 MPa) and temperatures (23 and 90°C).
We therefore conclude that, based on the above validations, our model can be used to investigate time-dependent creep of low-porosity granite at different temperatures. We will now use our model to further explore the influence of temperature, differential stress, confining pressure, and sample heterogeneity on brittle creep in low-porosity granite.
In this section, the influence of temperature, differential stress, confining pressure, and sample heterogeneity on brittle creep of granite was investigated with the proposed model. The numerical samples as shown in Figure 7 are the same as the samples modeled in the validation described above. The modeled samples were discretized into 20,000 square elements. We applied various axial stresses of 140, 145, 150, 155, and 160 MPa and various constant temperatures 23, 40, 50, 75, and 90°C on numerical samples with a inhomogeneity index 4, 5, and 6, respectively, to study the influence of temperature, differential stress, and sample heterogeneity on brittle creep of granite. The loading conditions are also shown in Figure 7. The relevant model parameters in the simulations are the same as the parameters listed in Table 1.
Numerical model and loading conditions for the simulated creep experiments.
In addition to the validations above, two additional simulations of brittle creep were conducted under uniaxial compressive stress of 150 MPa at constant temperatures of 40 and 75°C. The creep curves and creep strain rate curves for various constant temperatures are shown in Figure 8, and the curves clearly show the accelerating-decelerating phenomenology of brittle creep observed in laboratory tests. We can see that there is a clear temperature effect on brittle creep in granite from these simulations.
Creep curves and creep strain rate curves for simulations performed under uniaxial conditions at constant temperatures.
It is noted that the creep strain rate strongly depends on temperature as observed in brittle creep experiments. The evolution of creep strain rate at various constant temperatures is shown in Figure 8. The strain rate first decreases, reaches the minimum creep strain rate, and finally increases as the sample approaches macroscopic failure. The simulations show that there are several orders of magnitude difference in the minimum creep strain rate between a lower temperature (23°C) and a higher temperature (90°C). A large increase in strain rate at a higher temperature results in a large decrease in the time to failure (Figure 9) as observed in laboratory experiments on granite [8].
Time-to-failure vs. temperature curve for simulations performed under uniaxial conditions at constant temperatures of 23, 40, 50, 75, and 90°C.
Here, the results of single-step brittle creep experiments under different constant-applied differential stresses were presented to study the effect of differential stress on brittle creep in granite. Uniaxial creep tests at a constant temperature of 50°C and constant axial stresses of 140, 145, 150, 155, and 160 MPa are numerically performed, and the numerically obtained creep curves for the five simulations are shown in Figure 10. Likewise, Figure 10 clearly shows the accelerating-decelerating phenomenology of brittle creep seen in laboratory tests. The simulations indicate that the differential stress has a great influence on brittle creep in granite, as observed in brittle creep experiments [14]. First, the creep strain rate is higher when the differential stress is higher. A change in differential stress from 140 to 160 MPa leads to an increase in the minimum strain rate by over an order of magnitude. Similar observations have been experimentally obtained for many rock types [14]. As a result of the higher strain rate at higher differential stress, the time-to-failure is reduced as differential stress is increased (Figure 11).
Creep curves and creep strain rate curves for simulations performed under uniaxial conditions at constant differential stresses between 140 and 160 MPa.
Time-to-failure vs. temperature curve for simulations performed under uniaxial conditions at constant differential stresses between 140 and 160 MPa.
Moreover, brittle creep tests under different constant confining pressures of 0, 2, and 10 MPa, but the same constant temperature and applied axial stress of 50°C and 150 MPa, respectively, were also performed, and the brittle creep curves were presented in Figure 12. The simulations capture the decelerating-accelerating phenomenology in laboratory experiments and reveal that confining pressure plays a great influence on brittle creep in granite. The creep strain rate reduces with an increase in the confining pressure (Figure 12): an increase in confining pressure from 0 to 10 MPa leads to a reduction in the minimum strain rate by about an order of magnitude; the results are consistent with those from brittle creep experiments at different confining pressures. Figure 12 also shows snapshots of each of the failed samples. It can be seen that more localized shear damage occurred in rock samples at higher confining pressures.
Creep and creep strain rate curves for simulations performed under a constant differential stress of 150 MPa and confining pressures of 0, 2, and 10 MPa.
As we know, rock is heterogeneous, and thus we use a statistical Weibull distribution to reproduce material heterogeneity in rock. A set of simulations were performed to study the effect of material heterogeneity on brittle creep in granite with different homogeneity indices but at a temperature of 50°C, confining pressure of 0 MPa, and constant-applied axial stress of 150 MPa. The mean Young’s modulus and mean UCS of the elements were kept the same (43 GPa and 350 MPa, respectively), and only the homogeneity indices were changed in these simulations. As described above, a larger homogeneity index implies that the elements within the sample are closer to the mean Young’s modulus and the mean UCS. Therefore, a sample with a larger homogeneity index indicates that there are fewer low-strength elements in rock sample, and it will become stronger and more brittle.
The simulated creep curves and the evolution of creep strain rate presented in Figure 13 show that an increase in material homogeneity leads to a decrease in creep strain rate and an increase in time to failure. For example, the minimum creep strain rate decreases by about an order of magnitude, and the time to failure increases from about 111,600 to 358,200 s when the homogeneity index increases from 4 to 6. On the contrary, a decrease in material homogeneity results in an increase in the creep strain rate and a decrease in the time to failure.
Creep curves and creep strain rate curves for simulations performed on granite samples characterized by a different homogeneity index.
A numerical time-dependent thermomechanical model was proposed to simulate brittle creep in granite under different loading conditions and different temperatures. The mechanical parameters such as uniaxial compressive strength and Young’s modulus of the mesoscopic material elements assumed to be homogeneous and isotropic are randomly assigned with a Weibull statistic distribution to reflect the material heterogeneity at a mesoscale. It is noted that the model can well capture the cooperative interaction between microcracks in the transition from distributed to localized damage. The model is validated against published experimental data, and then conventional brittle creep experiments at various constant temperatures, applied differential stresses, confining pressures, and sample homogeneities were simulated. The simulations accurately capture the short- and long-term mechanical behavior of the experimental brittle creep tests using unique thermo-physico-mechanical properties. The simulations further show that an increase in temperature and differential stress leads to an increase in the creep strain rate and therefore a decrease in time to failure and an increase in confining pressure and sample homogeneity leads to a decrease in creep strain rate and an increase in time to failure. Thus, the model proposed in the present paper will help the management and optimization of rock engineering projects in granite.
The supports provided by the National Basic Research Program (973) of China (Grant no. 2014CB047100), Natural Science Foundation of China (Grant nos. 41,672,301 and 51,474,051), and Fundamental Research Funds for the Central Universities of China (N150102002) are highly acknowledged.
Recent decades have witnessed a rapid surge in population growth. Consequently, a high concentration of social and economic activities in urban metropolitans has led to the emergence of various transportation modes and services. Urban traffic congestion has become a daunting challenge in cities around the world. Excessive delay, low traveling speeds, increased travel costs, elevated drivers’ anxiety and frustrations, high fuel consumption, and vehicular emissions are the few consequences of traffic congestion. It also poses a threat to a stable urban economy [1, 2]. Traffic demands fluctuate significantly during the day (TOD), especially during rush hours, which is one of the main causes of congestion buildup. Congestion may be recurrent, arising from routine cyclic fluctuations in traffic volumes, or it may be non-recurrent produced due to unforeseen events such as traffic incidents, unpredictable weather conditions. Existing transport infrastructure cannot withstand the ever-growing traffic demands, while the inappropriate allocation of temporal and spatial resources further exacerbates the problems [3, 4]. An effective solution to mitigate traffic congestion is to embed intelligent transportation system (ITS) technologies in existing transport infrastructure for efficient and sustainable operations. Researchers and practitioners have proposed various strategies such as signal control optimization and dynamic lane grouping to address the issue in recent years.
Signalized intersections are a vital component of urban traffic networks and play a pivotal role in traffic control and management strategies. Over the years, they have been the primary focus of traffic improvement efforts since they are representative of frequent and restrictive bottlenecks. Poor traffic management at urban intersections leads to traffic jams and unsustainable travel patterns network-wide. Alternatively, intelligent traffic control and better management at these critical locations could result in smooth, safe, cheap, and sustainable operations. Traffic Signal Control (TSC) is an integral part of ITS. TSC is an important operation that can tackle various urban traffic issues such as congestion, fuel consumption and exhaust emission, and inefficient resource utilization. TSC involves determining appropriate signal timings parameters to improve various traffic performance measures like average vehicle delay, travel time, maximizing throughput, and reducing queue lengths and vehicular emissions. One of the main objectives of traffic signal control is to facilitate the safe and efficient movement of people through a road network. Achieving this goal warrant establishment of an accommodation plan that ensures appropriate assignment of right-of-way (ROW) to different users.
Over the years, different strategies have been proposed to address the TSC problem. A fixed-time signal control scheme has been widely used for managing traffic lights in urban areas. This strategy requires the determination of optimum TOD breakpoints for establishing TOD intervals, which are subsequently used for obtaining the predefined green splits for each split (green times) using Webster’s formula or some other optimization tools [5]. However, the fixed-time signal control strategy is suitable for stable and nearly homogenous traffic patterns. Alternatively, studies have focused on actuated and traffic responsive TSC schemes for dynamic traffic control and management. In such traffic control schemes, signal cycle length and green splits are adjusted according to real-time traffic data collected from sensors installed on each approach. Though actuated TSC strategies overcome some limitations of the former methods, they do not work well under all traffic and adverse conditions. TSC problem was initially addressed using various probability and regression-based methods [6, 7]. However, for oversaturated and undersaturated traffic conditions, such methods do not provide reliable solutions. Few notable classic TSC strategies proposed during the last few decades include: SCOOT [8], SCAT [9], MAXBAND [10], CRONOS, PRODYN [11], TRANSYT [12], RHODES [13], OPAC [14], and FUZZY LOGIC [15]. Few other methods recently used for traffic light setting are ARRB [16], TRRL [8], and HCM [17]. In addition, to signal control strategies, traffic light design could be isolated intersection based or coordinated. Isolated intersections signal schemes have limited benefits compared to coordinated strategies that consider the network of intersections.
The timing of traffic signals significantly influences the performance of the transportation system. Obtaining the optimal signal timing plan for a network in its entirety is challenging due to the stochastic and non-linear characteristics of the traffic system. From a computational perspective, the signal control optimization problem under the influence of several constraints is a highly non-linear and non-convex problem. To reduce the complexity of problem, studies have assumed partial convexification for obtaining the optimal signal plans [18, 19]. It has been shown that traffic light optimization belongs to the family of NP-complete problems whose complexity increases dramatically for real-world and more extensive transportation networks with prolonged study periods. Classical optimization methods used in this regard are not suitable for a variety of reasons. For example, they are sensitive to initial estimates of solution vector and require gradient computation of constraints and the objective functions. Further, the discrete nature of signal timing plan and phasing sequence limit the application of traditional optimization approaches. Similarly, classical signal control optimization techniques are usually more suited to isolated intersections. They are not scalable for large urban transport networks where the interdependence of traffic signals across multiple intersections may be explored. Hence, such methods do not consider the interdependencies and connectivity of traffic signals vital for large-scale urban transport networks.
Metaheuristics techniques, including and swarm intelligence and evolutionary algorithms, have emerged as appealing alternatives to classical optimization methods for addressing signal control problems. They can be easily adapted for solving signal optimization problems with mixed types of continuous and discrete variables on large-scale transportation systems. Metaheuristics are based on approximate random methods and involve an iterative master process that can efficiently provide high-quality, acceptable solutions with relatively low computational efforts [20]. No prior information regarding the search space characteristics is required. In addition, metaheuristics do not rely on gradient information of the objective functions and the associated constraints with reference to signal timing variables. Further, the process of finding the optimal solution is simple and straightforward. Entailing less complexity than exact methods means that metaheuristics could be easily implemented to solve non-linear complex optimization problems. Furthermore, for many large-scale engineering problems that involve uncertainties (such as traffic flow), obtaining near-optimal solutions within a reasonable time is acceptable. Owing to these benefits, several metaheuristics techniques have been successfully applied for solving TSC optimization problems. Metaheuristics aim at obtaining the optimal values/ranges for various signal parameters that influence the performance of signalized intersections and include variables such as cycle length, green splits, phase sequence, offsets, change interval, etc. These parameters of interest are also known as decision variables. Constraints conditions for signal optimization include lower and upper cycle length, green splits thresholds, etc.
Metaheuristics have been widely applied to solve the TSC problems under a single objective framework known as mono-objective optimization. The single objective optimization can be classified into four main types: i) travel time minimization, ii) delay minimization, iii) throughput maximization, and iv) fuel consumption and exhaust emissions (
This study provides a comprehensive review of metaheuristics techniques applied to signal control optimization. The surveyed literature is categorized based on the types of metaheuristics used, i.e., evolutionary algorithms and swarm intelligence techniques. A total of over 15 metaheuristics optimization techniques in traffic signal control and optimization are presented. Literature is summarized based on classification of techniques, considered optimization objectives, decision variables, and constraints conditions. Finally, based on the identified literature gaps, major challenges and prospects for future research are also proposed.
The remainder of this work is organized as follows. Section 2 provides research methods and publication analysis of signal control optimization using metaheuristics. Section 3 reviews evolutionary algorithms’ metaheuristics for signal optimization. Section 4 provides a summary of swarm intelligence techniques in the context of the subject domain. Section 5 and 6 presents surveys of trajectory-based metaheuristics and few others for TSC optimization. Finally, Section 7 presents the review conclusions and outlines the current challenges and recommendations for future research.
The relevant literature on TSC was searched (in May 2021) using a detailed systematic review (SR). SR is a formal and standard protocol for performing a review study. To ensure that findings were reached in a valid and reliable manner, the study adopted a three-staged approach, i.e., i) planning, ii) execution, and iii) analysis. The planning stage involved defining the research scope and aims, setting the inclusion and exclusion criteria, and developing the review protocols. The execution stage involved a systematic search using relevant search strings. The relevant publications were meticulously selected by browsing through different electronic databases such as “Google Scholar,” Science Direct,” Wiley Online Library,” “Scopus,” “Web of Science,” and “IEEE Xplore.” To explore these databases, the following “Keywords” were used: “signalized intersections,” “traffic congestion,” “traffic signal control,” “traffic signal timing optimization,” “traffic control through metaheuristics,” “intelligent traffic control,” “dynamic traffic management,” “traffic simulation and optimization,” “multi-objective traffic control,” etc. Titles, keywords, and abstracts of all the downloaded documents were reviewed to determine the appropriate selection of articles for the current study. Additional appropriate publications were added to the list by looking at the references selected publications. Publications were searched irrespective of publication year and the number of citations to have the maximum number for initial consideration. Duplicate articles found in various databases were also identified and removed. Non-academic publications, such as magazine articles, company reports, newspapers, presentations, and interview transcripts, were excluded. Finally, the analysis stage involved the classification, categorization, and summarization of the main theme of selected articles.
Figure 1 presents the chronological distributions of shortlisted publications in which metaheuristics are used for solving traffic signal control optimization. It may be observed from the publications reporting in Figure 1 that is there is a growing trend in the application of metaheuristics in the subject domain. Figure 2 shows the percentage distribution of published studies in the area of traffic control optimization based on the type of metaheuristic applied. It may be observed from the Figure that the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) have been widely used for signal optimization.
Chronological distribution of indexed publications on traffic signal optimization using swarm intelligence and evolutionary computation techniques (period 2000–2021).
Percentage distribution of indexed publications on traffic signal optimization based on metaheuristic type.
This section reviews the previous studies in the literature that applied evolutionary algorithms (EAs) for traffic signal control and optimization. EAs are the most widely used metaheuristics optimization techniques across diverse fields of science and engineering. EAs are population-based random search techniques and are inspired by Darwin’s theory of natural theory of evolution. The EAs contain a population of individuals, each symbolizing a search point in the feasible solution space exposed to a common learning process while proceeding among different generations. EAs begins with the initialization of random population, which are then subjected to selection, crossover, mutation through various generations so that offsprings generated evolve toward more favorable regions in the search space. At each generation, the fitness of the population is evaluated, and those with better fitness values are selected and recombined that have an increased probability of improved fitness. The program is iteratively repeated until it converges to the best (or near-optimal) solutions. The basic structure of EAs remains similar for all the algorithms under its family. Figure 3 presents the sample structure of EAs and their working principle. The following passages provide a brief explanation of various EAs employed in the field of traffic signal optimization. Table 1 presents a summary of previous studies that have applied EAs for traffic signal control and optimization.
General flow depicting the search mechanism of EAs.
1 | GA | ✓ | [21] | ||||||
2 | GA | ✓ | ✓ | [22] | |||||
3 | DE | ✓ | ✓ | [23] | |||||
4 | GA | ✓ | [24] | ||||||
5 | DE | ✓ | ✓ | [25] | |||||
6 | DE | ✓ | [26] | ||||||
7 | GA | ✓ | ✓ | [27] | |||||
8 | GA | ✓ | ✓ | [28] | |||||
9 | GA and DE | ✓ | [29] | ||||||
10 | GA | ✓ | ✓ | ✓ | [30] | ||||
11 | DE | ✓ | ✓ | [31] | |||||
12 | GA | ✓ | ✓ | [32] | |||||
13 | GA | ✓ | [33] | ||||||
14 | NSGA | ✓ | ✓ | [34] | |||||
15 | NSGA-II | ✓ | ✓ | ✓ | ✓ | [35] | |||
16 | GA | ✓ | [36] | ||||||
17 | DE | ✓ | ✓ | [37] | |||||
18 | GP | ✓ | [38] |
Summary of previous studies on traffic signal optimization using EAs.
Genetic algorithm is the most widely used method for traffic light optimization. John Holland initially proposed the GA metaheuristic in 1975 [39]. GA search mechanism for finding the optimal solution of an objective function mimics the natural selection process of the evolutionary theory of nature, which supports the “survival of the fittest” concept. It is a population-based technique that involves the ranking of individual members of the population according to their fitness.
The search process of the optimal solution begins with the initialization of a random population of solutions. The offspring population is created by iteratively applying various genetic operators such as crossover, mutation, elitism, etc. until the stopping criteria are satisfied. In literature, many studies have demonstrated the robustness of GA for adaptive traffic signal control. For example, Foy et al. utilized GA for traffic light optimization, considering delay time minimization as the objective function [36]. The number of initial GA generations was varied over five GA traffic runs. The optimal fitness value was achieved for populations ranging between the 20th to 30th generations with an average vehicle waiting time of around 40 seconds. GA was noted to yield rational signal timing plans reducing the timing delay significantly compared to the existing traffic control scheme. In their study, Rahbari et al., studied that traffic control at the signalized intersection with GA could reduce the congestion [40]. Yang and Luo adopted a hybrid GA simulated annealing (GA-SA) for signal control optimization at isolated signalized intersections considering delay as the objective function [41]. Empirical results showed that GA produced a rational signal timing plan compared to fixed control scenarios. A study conducted by Mingwei et al. proposed the application of multi-objective for intelligent traffic management at an isolated signalized intersection for a case study in China [42]. The considered optimization objectives included; average vehicle delay, vehicular stops, and fuel consumption. It was found that the optimized signal timing plan from GA significantly improved the considered traffic performance measures.
In another study, Turki et al. proposed a multi-objective NSGA-II to optimize various measures of effectiveness (such as delay, stops, fuel consumption, and emissions) at isolated signalized intersections in the city of Dhahran, Saudi Arabia [35]. Study results were compared with Synchro traffic simulation and optimization tool, and the results for a typical intersection are shown in Figures 4 and 5. All the performance measures witnessed considerable improvement for the optimized signal timing plan obtained using an NSGA-II optimizer. Figure 4(a–d) depicts the evolution of the four selected performance measures (delay, stops, fuel consumption, and emissions) against the number of iterations for three random initial populations. It may be noted that all the algorithms converged to their respective objective functions at approximately 70 to 100 generations. Comparing the random initial populations, population size 30 for all cases yielded the best results.
Evolution of different performance measures against NSGA-II iterations; (a) delay evolution, (b) number of vehicle stops evolution, (c) fuel consumption evolution, (d) emission evolution. Reprinted with permission from Ref. [
Comparison of NSGA-II and synchro optimizers for various traffic performance measures. Reprinted with permission from Ref. [
Figure 5 shows the performance comparison of NSGA-II and Synchro signal control strategies for the selected measures of effectiveness (delay, stops, fuel consumption, and emissions). It may be noted from the Figure that the NSGA-II optimizer outperformed the Synchro results for all the performance measures.
Li et al. also investigated the applicability of NSGA-II for solving signal control optimization problems [34]. Average queue ratio and vehicle throughput were the objective functions. The algorithm’s results were validated on a microscopic traffic simulation tool, VISSIM. Kwak et al. developed a GA traffic optimizer to evaluate the influence of traffic light setting on vehicle fuel consumption and emissions [32]. Model results were compared with TRANSIM, a microscopic traffic simulator. It was observed that the proposed GA traffic optimizer could reduce exhaust emissions by approximately 20% and fuel consumption in the range of 8–20%. In another study, Kou et al. employed multi-criteria GA for optimizing the signal timing plan of signalized junctions and compared the results with the highway capacity manual (HCM) method [28]. The study considered several optimization objectives such as stops, delays, and emissions. A reasonable trade-off established an optimal Pareto front among different conflicting objectives. Study results demonstrated the superior performance of the proposed GA traffic control scheme compared to the HCM method in terms of all the optimization objectives. Guo et al. developed a model for area-wide intersection traffic control in the central business district (CBD) area of Nanjing, China [43]. Capacity ratio, turning movement delay, and travel time was the three chosen objective functions. Computational experiments results showed significant mobility improvement compared to existing conditions. Study results were also validated in PARAMICS traffic simulation tool. In their study, Dezani et al. have shown that simultaneous optimization of traffic lights via GA and vehicle routes could significantly reduce the vehicle travel time compared to optimization considering only routes [44]. In another study, Tan et al. proposed a new Decentralized Genetic Algorithm (DGA) for signal timing optimization of traffic networks under oversaturated traffic conditions [45]. Average vehicle delay was used as the performance metric to evalauate the performance of proposed algorithm. Simulation results showed that DGA could effectively optimize the traffic light setting and reduced the average network delay.
Differential evolution is another population-based metaheuristic technique initially proposed by K.V. Pricein 1995 [46]. DE is characterized by its robustness, fast convergence to the objective function, and simplicity. Though the method has been successfully used for numerous applications across different disciplines, only a few studies have adopted DE for urban traffic control and management [25, 26, 27, 28, 29]. For example, in their recent study, Jamal et al. compared the performance of GA and DE for optimizing traffic lights at isolated signalized intersections in the city of Dhahran, Saudi Arabia [29]. Average delay time minimization was the objective function. The study concluded that both GA and DE could yield intelligent and rational signal timing plans, reducing the intersection average delay between 15 and 35%. DE was noted to converge to objective function faster than DA over several simulation runs. Similarly, in another study, Liu et al. proposed bacterial foraging optimization-based DE algorithm for optimizing delay at signalized intersections [37]. To improve convergence precision, DE was utilized for updating the bacteria position during the chemotaxis process. The proposed scheme yielded very promising results, reducing the intersection delay by over 28% compared to only 5% obtained by GA optimization. In their study, Korkmaz et al. suggested three different types of delay differential evolution-based delay estimation models (DEDEM), i.e., linear, quadratic, and exponential [47]. The researchers reported that all the proposed models effectively predicted the vehicle delay estimates in terms of relative errors between estimated and simulated values; however, quadratic DEDEM methods outperformed other models. Ceylan also approached the signal control optimization problem using the metaheuristic DE and Harmony-Search (HS) for network-wide traffic control and optimization [48]. Study results showed that DE algorithms yielded better results compared to HS.
In another research study, Yunrui et al. proposed multi-agent fuzzy logic control based on DE to optimize delay and queue length through a network of eleven intersections in the urban traffic context [31]. DE was used to decide and optimize the parameters of the fuzzy systems because it is easy to understand and implement. Empirical results revealed that the proposed method could substantially improve the network performance measures such as average vehicle delay, traffic throughput, and queue length. In a recent study, Liu et al. have proposed an improved adaptive differential evolution (ADE)-based evolvable traffic signal control (EvoTSC) scheme for global optimization of different traffic performance measures on large scale urban transportation networks [49]. The proposed TSC method was compared with a conventional TSC scheme on two practical and three synthetic transportation networks with varying traffic flow demands and different physical scales. Comparison results indicated that the DE-based EvoTSC method significantly outperformed its counterpart under all the considered scenarios. Zhang et al. also applied an online intelligent urban traffic signal control approach using multi-objective DE for real-time traffic control and optimization [50]. Experimental results showed that the proposed approach provides a more robust configuration of traffic signal phases and has relatively better real-time performance than the traditional traffic control scheme.
Genetic programming (GP) is another population-based metaheuristic technique that belongs to the family of evolutionary algorithms [51]. GP is an extension of GAs that allows for deep exploration of space on computer programs. GP starts with a population of random programs (candidate solutions) that are fit for applying evolutionary operators similar to genetic processes, thereby simulating the fundamental principles of Darwin’s evolution theory [52]. GP follows an iterative process to breed the solutions to problems using the probabilistic selection procedure for the carryover of fittest solutions to the offerings by applying genetic operators such as crossover and mutation. In literature, not many studies have focused on applications of GP for traffic analysis and management in urban transport networks. Montana and Czerwinski used a hybrid GA with strongly typed GP (STGP) for intelligent control and optimization of evolving traffic signals on a small-scale transport network [53]. Numerical simulation results showed that the proposed hybrid STGP model could effectively improve network performance under varying traffic demands.
A study conducted by González also proposed the application of GP for solving signal control problems [54]. This study considered four different traffic scenarios with properties and traffic conditions in a previous study [55]. Study results were also validated using the microscopic traffic simulator tool SUMO. Findings showed that GP could provide competitive and robust results for all the tested scenarios. However, the highway/network scenario had a more pronounced performance improvement (having an improvement of 10.34%) than the isolated intersection scenario (with an improvement of 4.24%). In another study, Ricalde and Banzhaf adopted an improved GP with epigenetic modifications for traffic light scheduling and optimization under dynamic traffic conditions [56]. Extensive simulation analysis revealed that the proposed model improved the network performance compared to standard GP and other previously used methods. This study, however, did not use any real-world data for validation purposes. In another study, the authors used a similar GP approach with epigenetic modifications (EpiGP) to design and evolve traffic signals using real-time field traffic data [38]. Results indicated significant improvement in network performance compared to conventional methods, including standard GP, static, and actuated traffic control techniques. Over 12% improvement in average delay was reported under high-density traffic conditions.
This section reviews the previous studies in the literature that applied swarm intelligence (SIs) techniques for traffic signal control and optimization. SI is another class metaheuristics that are increasingly used for various engineering and industrial applications. The search mechanisms of SI are believed to be inspired by human cognition representing the individual’s interaction in a social environment. For this reason, SI techniques are also sometimes called “behaviorally inspired algorithms.” In SI algorithms, each swarm member has a stochastic behavior due to their perception of the neighborhood and acts without supervision. By collective group intelligence, swarm utilizes their resources and environment effectively. The primary attribute of a swarm system is self-organization, which assists in evolving and obtaining the desired global level response by effective interactions at the local level. Just like EAs, SIs are population-based iterative procedures. After randomly initializing the population, individuals are evolved across different generations by mimicking the social behavior of animals or insects to reach the optimal solutions. However, SIs do not involve the use of evolutionary operators like crossover and mutation like EAs. Instead, a potential solution modifies itself based on its relationship with the environment and other individuals in the population as it flies through the search space. The following passages provide a brief explanation of various swarm intelligence techniques employed for solving signal control optimization problems. Table 2 presents a summary of previous studies that have applied SIs for traffic signal control and optimization.
1 | ACO | ✓ | ✓ | ✓ | [57] | ||||
2 | AIS | ✓ | ✓ | [58] | |||||
3 | GWO | ✓ | [59] | ||||||
4 | ABC | ✓ | ✓ | [60] | |||||
5 | ACO | ✓ | ✓ | [61] | |||||
6 | BA | ✓ | ✓ | [62] | |||||
7 | CS | ✓ | [63] | ||||||
8 | PSO | ✓ | ✓ | [64] | |||||
9 | PSO | ✓ | ✓ | [65] | |||||
10 | BA | ✓ | [66] | ||||||
11 | PSO | ✓ | [33] | ||||||
12 | PSO | ✓ | ✓ | [67] | |||||
13 | ABC | ✓ | [68] | ||||||
14 | ABC | ✓ | ✓ | [60] | |||||
15 | PSO | ✓ | ✓ | ✓ | [40] | ||||
16 | ACO | ✓ | [69] | ||||||
17 | CS | ✓ | [70] | ||||||
18 | ACO | ✓ | ✓ | [71] | |||||
19 | PSO | ✓ | ✓ | ✓ | [72] | ||||
20 | PSO | ✓ | [73] | ||||||
21 | PSO | ✓ | [74] | ||||||
22 | INA | ✓ | ✓ | [75] | |||||
23 | FFA | ✓ | [76] |
Summary of previous studies on traffic signal optimization using SI techniques.
Particle swarm optimization is a population-based swarm intelligence technique that was first introduced in 1995 by Eberhart and Kennedy. In the PSO algorithm, every potential solution is referred to as a particle representing a location in the problem space. The entire population of potential solutions (particles) is called the swarm. PSO search mechanism for global optima is inspired by birds in which each particle can update its velocity and position by using local and global best values. PSO is yet another widely used optimization algorithm for signal control problems. For example, Celtek applied PSO for real-time traffic control and management in the city of Kilis city in Turkey [77]. Algorithm performance was investigated in real-time using the SUMO traffic simulator. Social Learning-PSO was introduced as an optimizer for the traffic light. Empirical results obtained using the proposed PSO architecture resulted in travel time by 28%. The algorithms performed well both for undersaturated and oversaturated traffic conditions. Gokcxe and Isxık proposed a microscopic traffic simulator VISSIM-based PSO model for optimizing vehicle delay and traffic throughput using field data from28 signalized roundabout in Izmir, Turkey [64]. The simulation tool was used to evaluate the solutions obtained by PSO. Optimization of traffic signal head reduced the average delay time per vehicle by approximately 56% and increased the number of passing vehicles by 9.3%. In their study, Jia et al. employed multi-objective optimization of TSC using PSO [72]. The optimization objectives included average vehicle delay, traffic capacity, and vehicle exhaust emissions. The validity of the algorithm was examined by applying it to the real-time signal timing problem. The suggested algorithm provided competitive performance for all the MOEs compared to other efficient algorithms such as NSGA-II, IPSO, and GADST.
Abushehab et al. compared PSO and GA techniques for signal control optimization on a network of 13 traffic lights [78]. SUMO was used as a simulator tool for the network. Both the algorithms yielded systematic and rational signal timing plans; however, some algorithm variants were found to be more efficient than the others. In their study, Angraeni et al. proposed a modified PSO (MSPO) and fuzzy neural network (FNN) for optimizing signal cycle length at an isolated intersection [79]. Simulation results using PSO led to a reduction in MSE value from 6.3299 to 2.065, while network performance was improved by 4.26%. The accuracy of the training process using MPSO was higher than FNN. Chuo et al. reported a significant decrease in vehicle queue length by using PSO as a traffic signal optimizer [73]. In another study, Garcıa-Nieto et al. applied PSO to optimize the cycle program of 126 traffic signals located in two large and heterogenous metropolitans of cities of Bahıa Blanca in Argentina and Malaga in Spain [80]. The Obtained solutions were validated using the traffic simulation package SUMO.
In comparison to the existing pre-defined traffic control schemes, PSO achieved significant quantitative improvement for both the objectives, i.e., overall journey time (74% improvement) and the number of vehicles reaching their destinations (31.66%) improvement). In another study, a researcher proposed an improved PSO architecture by combining traditional PSO with GA for multi-objective traffic light optimization. The selected performance indexes included vehicular emissions, vehicle delay, and queue length [40]. The authors reported that the improved PSO method has a quick response and higher self-organization ability which is beneficial for improving the efficiency of traffic signal control.
Olivera et al. investigated the applicability of PSO to reduce vehicular exhaust emissions (CO and NOx) and fuel consumption considering large-scale heterogeneous urban scenarios in the cities of Seville and Malaga in Spain [67]. Study results showed that the proposed signal control strategy could significantly reduce the exhaust emission (CO by 3.3% and NOx by29.3%) compared and fuel consumption (by 18.2%) compared to signals designed by human experts. In their study, Qian et al. designed a simulation protocol for traffic different signal parameters such as cycle, green signal ratio, and phase difference using three Swarms Cooperative-PSO algorithms [74]. The considered optimization objectives included average vehicle delay and average parking number per vehicle. Algorithm simulation results were validated using traffic simulator CORSIM. Lo and Tung compared the performance of PSO and GA-based signal control along four intersections on an urban arterial and noted that the PSO algorithm outperformed GA both in terms of speed convergence and accuracy of search [81]. A couple of other recent studies also demonstrated the adequacy and robust performance of PSO for TSC and optimization [82, 83].
Ant Colony optimization is a swarm intelligence method-based optimization technique that mimics the natural behavior of ants in finding the shortest path from an origin to a food source [84]. In ACO, the path of every ant from origin to destination is considered as a possible solution. ACO has been widely used for traffic signal optimization. In their study, Putha et al. used ACO for traffic signal coordination and optimization in the context of an oversaturated urban transport network [85]. The authors reported that ACO could provide reliable solutions of optimal signal timing plan compared to GA. Yu et al. also applied ACO for intelligent traffic control at signalized intersections considering vehicle waiting time as the optimization objective [86]. The authors reported that ACO outperformed the traditional traffic actuated scheme, predominantly during traffic flow periods. He and Hou also proposed the application of a multi-objective ACO algorithm for the timing optimization of traffic signals [57]. Several parameters such as vehicle delay, number of stops, and traffic capacity performance indices were chosen as performance indexes. Numerical simulation results demonstrated that ACO is a simple and robust technique for signal control optimization problems. The proposed ACO technique significantly improved the selected performance indicators compared to Webstar and GA algorithms.
In another study, ACO optimized the timing plan for traffic lights at isolated signalized intersections [61]. All the selected intersection measures of effectiveness (MOEs), including vehicle delay, parking rate, and the number of stops, were improved by a fair margin. Sankar and Chandra proposed a multi-agent ACO for effective traffic management on a network level [69]. The authors concluded that the method could be pretty useful in reducing average vehicle delays and traffic congestion under varying traffic conditions. Haldenbilen et al. developed an ACO-based TRANSYT (ACOTRANS) model for area traffic control (ATC) through a coordinated signalized intersection networks under different traffic demands [87]. A total of 23 links were considered for the analysis, and the network Disutility Index (DI) was chosen as the primary performance index. A comparative analysis of the network’s PI obtained using TRANSYT-7F with hill-climbing (HC) optimization and TRANSYT-7F with GA was also performed. Study results showed that the proposed ACOTRANS improved the network’s PI by 13.9% and 11.7% compared to its counterparts TRANSYT-7F optimization with HC and GA. Li et al. compared ACO and Fuzzy Logic for optimizing traffic signal timing in a simulated environment [88]. Traffic capacity and vehicular delay were considered as the objective functions and did not consider pedestrian traffic. The validity of proposed algorithms was tested using actual time-period and conventional algorithms. Jabbarpour et al. conducted a detailed review of the literature focused on applying ACO evolutionary algorithms for the optimization of vehicular traffic systems [90].
Rida et al. proposed ACO for real-time traffic light optimization problems at isolated signalized intersections [71]. Objective functions include minimizing the vehicle waiting time and increasing the traffic flow. The proposed model yielded robust performance compared to fixed time signal controller and other dynamic signal control strategies. Renfrew and Yu, in their studies, also reported that ACO demonstrated robust performance compared to actuated control in optimizing signal timing plan, particularly under high traffic demand [89, 91]. Srivastava and Sahana proposed a novel hybrid nested ACO model intending to reduce the vehicle waiting time at signalized intersections [92]. The proposed model was also compared with the hybrid nested GA model. Results showed that nested hybrid models outperformed traditional ACO and GA-based traffic control.
The traditional algorithms used for training carry some drawbacks of getting stuck in computational complexity and local minima. The artificial bee colony (ABC) algorithm is a revolutionary approach developed by Karaboga et al. [93]. ABC has good exploration capabilities in finding optimal weights during the training process [94]. ABC algorithm operates on the principle of foraging behavior of honeybees in seeking quality food. Each cycle of the search comprising three steps: sending employed bees onto the food source to measure nectar amount; selecting food source by onlookers once the information is shared by employed bees, and sending the scouts for discovering new food source [95].
ABC algorithm is widely used in optimizing traffic-related problems by previous researchers [60, 68, 96]. Zhao et al. investigated a typical intersection as a case study at Lanzhou city [60]. The green time length of each phase of the signal cycle and signal cycle were considered as decision variables. Favorable convergence was achieved using different setting parameters of the algorithm. The effect of signal cycle on control targets resulted that vehicle delays will increase with the signal cycle; however, the stops will decrease. In comparison to non-dominating sorting genetic algorithm and webster timing algorithm, ABC manifested better convergence. In another study, Dell’Orco et al. developed TRANSYT-7F to investigate network performance index (PI) for optimizing signal timing [96]. Results revealed that PI’s of the network in the case of ABC improved by 2.4 and 2.7% compared to genetic algorithm and hill-climbing method.
Cuckoo search (CS) is a recently developed metaheuristic algorithm developed by Yang and Deb [97], inspired by the natural breed parasitism of the cuckoo species. For understanding its working principle, consider that each bird lays one egg at a time and dumps it in a random nest which represents a single solution. The nest with high-quality eggs will be moved to the next generation. The number of host nests is fixed, and the egg laid by the cuckoo is discovered by the host bird. In this situation, the host bird either gets rid of the egg or abandons the nest by developing a new nest [98]. Few studies interpret CS as more efficient than PSO and GA [97].
Araghi et al. employed neural networks (NN) and adaptive neuro-fuzzy inference system (ANFIS) to optimize the results of CS in the case of intelligent traffic control [63]. The results were compared to that of the fixed time controller. It was revealed that the CS-NN and SC-ANFIS showed 44% and 39% improved performance against the fixed-time controller. Similarly, in another study, the authors evaluated the performance of ANFIS using CS for optimization of controlling traffic signals for an isolated intersection [70]. Improved performance of ANFIS-CS was obtained against fixed-time controller.
Bat algorithm (BA), initially developed by Xin-she yang in 2010, is inspired by the echolocation of microbats [99]. The working principle of BA encompasses three basic steps: bats use echolocation to sense the distance bifurcating the food and barrier; bats randomly fly with variable loudness and wavelength.; bats automatically adjust their wavelength and pulse depending upon the proximity of food/prey [100].
Srivastava, Sahana used BA to determine the wait time at a traffic signal for the discrete microscopic model [66]. The study was based on 12 nodes and four intersections. The results were compared to GA. Relatively higher performance was obtained for BA algorithm as compared to GA. Jintamuttha et al. carried experimental simulation for the green time of intersection for ten cycles per run [62]. The results of the experiment were optimized using BA. The average queue length and waiting time improved due to optimization.
The immune network algorithm (INA) or artificial immune system (AIS) is another useful optimization algorithm recently practiced for signal control optimization problems. As its name suggests, the working mechanism of this algorithm is inspired by the biological immune system. Immune cells have receptors that can detect harmful pathogens and activate antibodies to fight them, leading to their elimination [101]. Louati et al. applied INA to optimize queue, delay, and traffic throughput at signalized intersections under varying traffic demands [75]. It was found that INA outperformed traditional fixed-time adaptive traffic control strategies and validated the study results through VISSIM, a microscopic traffic simulation platform. In another study, Trabelsi et al. evaluated the performance of AIS to detect and rationally control anomalous traffic conditions through a network of signalized intersections [58]. Simulation results proved the adequacy and robustness of the proposed AIS-based signal control method.
Darmoul et al. employed multi-agent immune network (INAMAS) for optimal control and management of interrupted traffic flow at signalized intersections [102]. The proposed INAMAS models offered an intelligent mechanism that could explicitly capture the disturbance-related knowledge of traffic fluctuations. To demonstrate the efficacy of the proposed model, the authors compared its performance against two widely used signal control strategies, namely fixed-time control and LQF-MWM (longest queue first –maximal weight matching) algorithm. The suggested INAMAS scheme provided a competitive performance in terms of chosen performance indicators, i.e., vehicle queue and waiting times under extreme traffic conditions involving high traffic volume and block approaches. Figure 6a plots the average vehicle delay for all the three signal control strategies under various traffic scenarios [102]. For scenario 1 (moderate traffic congestion), the INAMAS algorithm produces approximately a 24% reduction in average delay values compared to the LQF-MWM strategy. For scenario 2 (high-density traffic), the proposed INAMAS optimizer decreased the average delay by nearly 32%. For scenario 3 (extreme congestion), the corresponding improvement by the INAMAS algorithm is about 28%. Figure 6b depicts the relationship between the total network delay and simulation time (in minutes) for all three signal optimization strategies [102]. It is evident from the results in Figure 6b that during the first 5 minutes, all the controllers have comparable performance. At the end of simulation analysis (after 5 hours), when the traffic density reaches 9600 vehicles per hour, the INAMAS controller achieved better performance compared to others, showing its superior capability to manage large and complex traffic networks.
(a) Comparison of average total delay per vehicle from various optimizers (b) cumulative network delay for scenario 1 for various optimizers Ref. [
Moalla et al., in their study, also demonstrated the robustness of AIS for controlling traffic at isolated signalized intersections [103]. However, the authors also emphasized that validation of the proposed AIS scheme is challenging and should be handled carefully. In another study, the author highlighted AIS-based traffic control’s significance for network-wide traffic management [104]. Comparative results with TRANSYT 7F showed the superior performance of AIS approach. Galvan-Correa et al. proposed a new metaheuristic known as the micro artificial immune systems (MAIS) to optimize vehicular emission and traffic flow in the city of Mexico [105]. The performance of the suggested MAIS technique was compared with several other metaheuristics, including GA, DE, SA, PSO. Results showed that MAIS achieved better results compared to most of the other metaheuristics. In a recent study, Qiao et al. proposed a novel hybrid algorithm, known as the Immune-Fireworks algorithm (IM-FWA) for effective traffic management on large-scale urban transportation networks [106]. The proposed hybrid algorithm was developed based on fireworks and artificial immune algorithms. A hierarchical strategy was proposed in the framework to avoid possible offsets conflicts and reasonable configuration of intersection offsets. Simulation results showed that the proposed IM-FWA could successfully overcome the shortcomings of FWA and AIS algorithms by providing a better and more rational signal timing plan to effectively reduce traffic flow delays.
The characteristic behavior of fireflies is animated by Yang [107] into a nature-inspired meta-heuristic swarm intelligent method called Bat Algorithm. In BA, all fireflies are assumed unisex, and attractiveness is proportional to their brightness, which in turn depends on the distance. Thus, the brightness can be considered a cost function, which is maximized in optimization.
Kwiecień, Filipowicz [studied optimizing costs controlled by queue capacity, maximal wait, and servers [76]. It was deduced that the use of FA could maximize the value of the objective function, and FA converges toward the optimal solution very quickly. Goudarzi et al. [108] investigated traffic flow volume by a probabilistic neural network method called deep belief network (DBN). FA was used to optimize the learning parameters of DBN. As a result, the proposed model predicted the traffic flow with higher accuracy compared to traditional models.
Gray wolf optimizer (GWO) is a new metaheuristic technique recently proposed by Mirjalili in 2014 [109]. GWO is inspired by the social hierarchy and hunting behavior of gray wolves. In GWO optimization, the wolves represent a solution set of candidate solutions. The hunting cycle in the GWO commences with the acquisition of a random population of candidate solutions (wolves) followed by identifying optimal prey’s locations using a cyclic process. GWO has several advantages compared with evolutionary approaches, easy programming and implementation, algorithm simplicity, no need for algorithm-specific parameters, and lower computational complexity [110]. In recent years, GWO has been increasingly used in diverse disciplines. However, studies on its applications in transportation and traffic engineering in general and traffic control and optimization in particular are very few.
Teng et al. were the first to use a hybrid gray wolf and grasshopper algorithm (GWGHA) algorithm for timing optimization of traffic lights [111]. The obtained solutions were simulated in a microscopic traffic simulator package SUMO. The performance of the proposed GWGHA hybrid algorithm was compared with other metaheuristics like GWO, GOA, PSO, and SPSO2011. Results indicated that the proposed hybrid algorithm provided better solutions than its counterparts because it utilizes the feature of GWO for accelerating the convergence speed while using GOA to diversify the population. In another recent study, Sabry and Kaittan proposed a novel hybrid algorithm consisting of gray wolf and fuzzy proportional-integral (GW-FPI) for active vehicle queue management in an urban context [59]. The proposed traffic controller was compared with PI through repeated MATLAB simulations. Study results indicated the stable and robust performance of the proposed hybrid controller for queue management in a dynamic transport network with varying traffic flow demands.
This section surveys the previous works that applied trajectory-based metaheuristics techniques) for traffic signal control and optimization. As the name suggests, these algorithms form search trajectories in solution space and iteratively improve the single solution in its neighborhood. Their exploration process starts from a random initial solution generated by another algorithm. At each stage, the current solution is replaced by a better offspring population. Trajector-based metaheuristics are mainly characterized by their internal memory sorting the state of search, candidate solution generator, and selection policy for candidate movement through generations. Table 3 summarizes the previous works that applied trajectory-based search metaheuristics, hybrid metaheuristics, and others for traffic signal control and optimization.
1 | SA-GA | ✓ | [112] | ||||||
2 | IM-FWA | ✓ | [106] | ||||||
3 | ISA-GA | ✓ | [113] | ||||||
4 | SA | ✓ | ✓ | [114] | |||||
5 | HS | ✓ | [115] | ||||||
6 | HS | ✓ | ✓ | ✓ | [116] | ||||
7 | JAYA | ✓ | [117] | ||||||
8 | TS | ✓ | [118] | ||||||
9 | TS-ABC | ✓ | [68] | ||||||
10 | TS | ✓ | [119] | ||||||
11 | PSO-TS | ✓ | [120] | ||||||
12 | WCO | ✓ | [121] | ||||||
13 | GHW-GHA | ✓ | ✓ | [111] | |||||
14 | JAYA | ✓ | [122] | ||||||
15 | GW-FPI | ✓ | [59] |
Summary of previous studies on traffic signal optimization using trajectory-based metaheuristics, hybrid metaheuristics, and others.
Tabu Search (TS) is a metaheuristic introduced by Fred Glover in 1986 to overcome the local search (LS) problem of existing methods [123]. TS allows the LS heuristic to diversify the search for solution space outside the local optima [124]. One of the important features of TS is its memory function, which can restrict few search directions for a more detailed LS, thereby making it easier to avoid local optimum solutions. By combining the greedy concept and randomization, the TS algorithm could provide an efficient solution to many optimization problems. In literature, only a few studies have focused on the application of Tabu search for signal control optimization. Hu and Chen proposed traffic signal control based on a novel greedy randomized tabu search (GRTS) algorithm considering travel time as the primary optimization objective [118]. GRTS results were compared with a GA-based traffic control scheme using data from a real city network to demonstrate the benefits of the proposed method. Numerical simulation results revealed that over 25% reduction in travel time might be achieved under medium to high traffic demands. In another study, Karoonsoontawong and Woller applied reactive tabu search (RTS) for simultaneous solutions of traffic signal optimization and dynamic user equilibrium problems on two transport networks in a simulated environment [119]. Three different variants of RTS were investigated based on deterministic or probabilistic neighborhood definitions. The performance of all the RTS variants was evaluated using three criteria such as solution quality, CPU time, and convergence speed. Simulation results showed that the RTS approach could provide promising results in terms of improving the overall network performance.
In a recent study, Hao et al. proposed a hybrid tabu search-artificial bee colony (TS-ABC) algorithm for robust optimization of signal control parameters in undersaturated traffic conditions at isolated signalized intersections [68]. This study considered two performance indexes such as average delay and mean-square error of average delay. The proposed signal control optimizer was validated using field data from an intersection in the city of Zhangye, China. Numerical simulation results compared with GA showed that the proposed TS-ABC is better in reducing the traffic delay under varying and heterogeneous traffic conditions. Chentoufi and Ellaia also proposed a hybrid particle swarm and tabu search (PSO-TS) for adaptive traffic lights timing optimization on real-time isolated signalized intersections in the context of Moroccan cities [120]. The authors also highlighted the significance of integrating the proposed PSO-TS model and VISSIM to achieve optimum average delay estimates. Simulation results demonstrated the superior efficiency of the PSO-TS technique against the traditional static models under oversaturated traffic conditions.
Simulated Annealing (SA), developed by Kirkpatrick et al. is inspired by the statistical mechanics of annealing in solids [125]. For understanding, consider a change in temperature, which causes a change in energy and movement of particles in solids. There is a sequence of decreasing temperature in annealing until criteria are met [126].
Li, Schonfeld [112] reported traffic signal time optimization using metaheuristic capabilities of SA with GA. It was concluded that SA-GA models outperform in optimization compared to individual SA and GA models. Similar results were reported by Song et al. in evaluating the optimized model for reducing traffic emissions on arterial roads [113]. Oda et al. [114] employed SA to optimize traffic signal timing and reported its improved performance as compared to traditional models.
This section reviews the previous works that applied some other metaheuristics for traffic signal control and optimization. These include the harmony search algorithm, water cycle algorithm, and Jaya algorithm. Table 3 summarizes the previous works that applied trajectory-based search metaheuristics, hybrid metaheuristics, and others for traffic signal control and optimization.
The metaheuristic harmony search (HS) algorithm simulates the natural musical improvisation process where the musicians aim to achieve a near-perfect state of harmony [127]. In the HS algorithm, the candidate solution population is known as harmony memory (HM), where every single solution in solution space is referred to as “harmony,” which belongs to the “
In another study, Ceylan and Ceylan adopted a hybrid harmony search algorithm and TRANSYT hill-climbing algorithm (HSHC-TRANS) for solving stochastic equilibrium network design (SEQND) in the context of optimal traffic signal setting problems [128]. The effectiveness of HSHC-TRANS was evaluated against HS and GA in terms of network performance index (PI). Results showed that the proposed hybrid model yielded about 11% in the network’s PI compared to the GA-based model. In another study, Gao et al. addressed the urban traffic signal scheduling problem (TSSP) using a discrete harmony search (DHS) with an ensemble of local search [115]. The primary objective was to minimize the network-wide total delay under a pre-defined finite horizon. Extensive simulation experiments were carried out using traffic data from a partial transport network in Singapore. Comparative analysis showed that the HS algorithm as a meta-heuristic achieved better performance compared to fixed-cycle traffic signal control (FCSC). Dellorco et al. also investigated the applicability of HS for signal control optimization on the two-junction network with the fixed flow on the links [116]. A comparative analysis of HS with GA and HC algorithms showed that HS resulted in a better network’s PI compared to its counterparts. Afterward, the validity of the proposed HS algorithm was assessed by applying it to a test network.
The Jaya algorithm is a recently proposed metaheuristic initially introduced by R.V. Rao [129]. The word Jaya comes from Sanskrit, which means “victory.” In the Jaya algorithm, the search strategy always attempts to be victorious by reaching the optimal and best solution, and thus it is named “Jaya.” It is arguably one of the simplest and easy-to-implement metaheuristics. The main benefit of Jaya for optimization problems lies in the fact that this algorithm requires only common controlling parameters such as population size and the number of iterations and does not require any additional algorithm-specific constraints/parameters. While this algorithm has been successfully used for several scheduling and optimization problems in recent years, its applications in the domain of traffic scheduling and management are relatively scarce.
A recent study conducted by Gao et al. compared the performance of Jaya algorithms with other metaheuristics (like water cycle algorithm (WCO), genetic algorithm (GA), artificial bee colony, and harmony search (HS), and hybrid ABC-LS) for solving traffic light scheduling problem [121]. Simulation results showed all the algorithms achieved competitive results; however, the hybrid algorithm attained better accuracy and convergence. The proposed models were also tested on real-time traffic and phase data from a network of intersections in the Jurong area of Singapore. In another study, the authors proposed an improved Jaya algorithm for solving traffic light optimization problems in the context of large-scale urban transport networks [122]. The chosen performance index was to minimize the network-wide total traffic delay within a given time horizon. To enhance the search performance in the local search space, a neighborhood search operator was proposed. Experiments were carried out using traffic data for a case study from the Singapore transport network. Study results demonstrated the robustness and better performance of proposed improved Jaya algorithms against standard Jaya algorithm and exiting traffic light control scheme. In another follow-up study, Gao et al. studied large-scale urban traffic lights scheduling problems using three different metaheuristics, namely Jaya, WCO, and HS [117]. The objective function was to optimize the delay time of all vehicles network-wise under a fixed time horizon. This study also proposed a feature search operator (FSO) to improve the search performance of proposed metaheuristics. To examine the efficacy of proposed methods, experiments were carried out using real-time traffic data. It was concluded that metaheuristic-based traffic control could significantly improve the network performance compared to existing traffic control strategies. Numerical simulation results showed that in comparison to feature-based search (FBS), operator for all algorithms improved the total vehicle delay time by more than 26% in their worst case scenarios.
Figure 7a depicts the relationships between total network delay time (sec) and sampling intervals for a typical urban traffic network with 100 junctions from the west Jurong area in Singapore [117]. Minimum (min.), average (avg.) and maximum (max.) total delay values each for 30 repeats and five sampling intervals (5, 10, 15, 20, and 30 sec) are reported. It is evident from the results that a sampling period of 15 seconds yielded the best results, which were then adopted for subsequent experiments. Figure 7b shows the relative percentage improvement in network performance (reduction in network delay) for standard Jaya algorithm with improved Jaya (iJaya), and Jaya with FBS operator (iJaya+FBS) for a sample 11 cases of traffic network from the same study [117]. Compared to standard Jaya, the iJaya yielded the improvements in range for 0–6% for min., avg., and max. Results, while iJaya+FBS algorithm resulted in corresponding improvement values between 9 and 11%. Figure 7c depicts the percentage improvement of IWCA and IWCA+FBS algorithms relative to standard WCA optimizer. The IWCA improved the standard WCA in terms of min., avg., and max. Results for 11 test cases in the range of 2–8%, while the corresponding improvement for IWCA+FBS algorithm is approximately 20–24%. Figure 7d shows the network performance improvement of standard HS and HS + FBS algorithms for the same network of traffic junctions [117]. The improvement for HS + FBS algorithm compared to standard HS optimizer are between 2 and 12% for min., avg., and max. Results for the considered cases.
(a) Results comparison with different sampling times for network of 100 junctions, (b) the % improvement of iJaya and iJaya+FBS with standard Jaya, (c) the % improvement IWCA and IWCA+FBS with standard Jaya, (d) the % improvement HS + FBS and standard HS. Ref. [
Figure 8 presents the graphical comparison among the three optimization algorithms (iJaya+FBS, IWCA+FBS, and HS + FBS) in terms of the average relative percentage deviation (ARPD) of the resulting network delay time values [117]. It is clear from the results that the IWCA+FBS algorithm with an average delay reduction of 28.54% outperformed the iJaya+FBS and HS + FBS having the corresponding values of 28.22% and 27.84%, respectively. Further, all the algorithms yielded an improvement of at least 26% in the worst-case scenarios.
ARPD improvements comparison for different optimizers. Reprinted with permission from Ref. [
The water cycle algorithm (WCA) is another recently proposed metaheuristic whose search mechanism is inspired by the natural water cycle process, where streams and rivers flow down the hill to reach the sea [130]. The surface run-off model is imitated in WCA for updating the current candidate solutions and the generation of new offspring. The effectiveness of WCA has been explored for various applications such as truss structures, constrained and unconstrained engineering design problems [130, 131, 132, 133]. However, very few studies have used WCO for traffic control, management, and optimization.
A recent study by Gao et al. proposed the application WCO for traffic signal scheduling and optimization based on actual traffic data from a case study in Singapore [121]. WCO was compared with four other metaheuristics and a hybrid algorithm (ABC-LS), considering the network delay as the main optimization objective. Numerical simulation results proved the benefits of adopting metaheuristic-based traffic control strategies instead of existing fixed traffic light schemes. In another study, Gao et al. compared WCO with the Jaya algorithm and Harmony search using the field traffic data from the same transportation network. The performance metric minimized the network-wide total traffic delay within a given time horizon [117]. The study proposed a neighborhood search operator to enhance the search performance of all the algorithms in the local search space. Study results showed that WCA, with an average better improvement of in network-wide delay (28.54%), outperformed HS (28.22%) and Jaya algorithm (27.84%).
Traffic control and management using metaheuristics have emerged as an effective solution to mitigate urban congestion. This study provided a comprehensive review of state-of-art research on traffic signal optimization using different metaheuristics approaches. The surveyed literature is categorized based on the nature of applied metaheuristics, i.e., swarm intelligence (SI) techniques, evolutionary algorithms, trajectory-based metaheuristics, and others. Although numerous metaheuristics have been employed for signal optimization, GA, PSO, ACO, and ABC algorithms have been widely explored. Various traffic signal parameters such as cycle length, green splits, offsets, and phasing sequence are considered decision variables to solve signal control optimization problems. Similarly, studies have considered several optimization objectives such as delay, number of stops, travel time, throughput, queue, fuel consumption, exhaust emissions to address the problem. Some studies have adopted single-objective optimization, while others have attempted to solve traffic signal control as a multi-objective optimization problem. However, little work has been done to understand the correlations between the conflicting objectives which is vital for traffic engineers and decision-makers to evaluate their relative importance. Based on the presented survey work, the following passages present key challenges, research gaps, and future research directions in this area.
The review has shown that most of the previous works have adopted a single metaheuristic method for TSC optimization. However, very few studies have investigated the applicability of hybrid or ensemble metaheuristics for solving TSC optimization problems. In general, hybrid techniques are more useful than traditional metaheuristics. Hence, the application of hybrid metaheuristics for signal optimization could be a promising research direction.
Traditional evolutionary algorithms and swarm intelligence optimizers could yield acceptable solutions. However, the performance of these optimization techniques may be compared with recent state-of-the-art optimization approaches such as Teaching Learning Based Optimization Algorithm (TLBOA), Gravitational Search Algorithms (GSA), Rock Hyraxes Swarm Optimization (RHSO), hyper-heuristics, which are not explored yet for traffic signal optimization problems.
The literature review also noted that most previous studies were focused on single-objective optimization; however, traffic engineers often have to deal with multiple conflicting objectives to optimize the performance at the network level. Alternatively, for multiobjective optimization, the vast majority of existing works introduce weights for different objectives and consequently tackle signal optimization as a signal objective optimization problem. To optimize different performance indicators along optimal paretofront, multiple objectives have to be properly optimized. Developing an optimizer for multi-objective scenarios remains a challenging issue and is worth exploring in future studies.
Objective functions based on energy consumption and exhaust emissions have become a topic of increasing interest in recent years. From the reviewed literature, it was concluded that various approximate fuel consumptions and emission models were used for signal control optimization. Application of such approximate models could lead to an un-realistic traffic light setting. Future studies should consider the calibration of fuel consumption and emission models for a given network.
It was also evident from the presented literature that there is a shortage of research on statistical performance evaluation of proposed metaheuristics. Therefore, it would be interesting to explore the statistical analysis of such optimization strategies in terms of worst, average, and best results. Likewise, statistical significance tests may be conducted to compare the performance among various metaheuristics in solving signal optimization problems.
Lack of appropriate validation protocol is another important issue. Some studies have employed mere traffic simulation platforms to assess the validity of applied metaheuristics, while others have used them for isolated intersection scenarios or small traffic networks. Network optimization has become popular in recent years. For achieving desired improvements at the network level, the methods should be tested for large-scale complex transportation networks.
The surveyed literature also indicated that most previous studies considered only vehicular traffic and neglected the pedestrian traffic in solving the TSC problem using metaheuristics. It is important to consider all forms of traffic and driving systems to improve the overall efficiency of the transport system.
The surveyed literature also revealed that many studies develop metaheuristic-based traffic control considering specific traffic demand conditions, neglecting the other potential scenarios. It is essential to consider all ranges of traffic flow conditions (undersaturated, saturated, and oversaturated flow conditions) and traffic disturbances in developing metaheuristic to address TSC optimization problems.
The accuracy and reliability of the signal timing plan obtained using metaheuristics are highly dependent on the accuracy of traffic flow prediction models. In recent years, with rapid advances in computational power, big data technology has been successfully used for accurate traffic flow prediction. Therefore, the application of metaheuristics coupled with big data technology for traffic signal optimization appears to be another interesting research direction.
The authors acknowledge the support of the King Fahd University of Petroleum and Minerals, KFUPM, Dhahran Saudi Arabia, and Qassim University, Burudah, Saudi Arabia, for Supporting this study.
“The authors declare no conflict of interest.”
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Besides, these parts are not only about cutting-edge technologies, but also related with conventional methods and their new applications in colored wastewater treatment area briefly.",book:{id:"5086",slug:"textile-wastewater-treatment",title:"Textile Wastewater Treatment",fullTitle:"Textile Wastewater Treatment"},signatures:"Serkan Arslan, Murat Eyvaz, Ercan Gürbulak and Ebubekir Yüksel",authors:[{id:"170083",title:"Associate Prof.",name:"Murat",middleName:null,surname:"Eyvaz",slug:"murat-eyvaz",fullName:"Murat Eyvaz"},{id:"176699",title:"Dr.",name:"Ercan",middleName:null,surname:"Gürbulak",slug:"ercan-gurbulak",fullName:"Ercan Gürbulak"},{id:"176700",title:"MSc.",name:"Serkan",middleName:null,surname:"Arslan",slug:"serkan-arslan",fullName:"Serkan Arslan"},{id:"176701",title:"Prof.",name:"Ebubekir",middleName:null,surname:"Yüksel",slug:"ebubekir-yuksel",fullName:"Ebubekir Yüksel"}]},{id:"42001",doi:"10.5772/53777",title:"Cyclodextrins in Textile Finishing",slug:"cyclodextrins-in-textile-finishing",totalDownloads:5486,totalCrossrefCites:19,totalDimensionsCites:39,abstract:null,book:{id:"3137",slug:"eco-friendly-textile-dyeing-and-finishing",title:"Eco-Friendly Textile Dyeing and Finishing",fullTitle:"Eco-Friendly Textile Dyeing and Finishing"},signatures:"Bojana Voncina and Vera Vivod",authors:[{id:"33838",title:"Prof.",name:"Bojana",middleName:null,surname:"Voncina",slug:"bojana-voncina",fullName:"Bojana Voncina"}]},{id:"41409",doi:"10.5772/53911",title:"Surface Modification Methods for Improving the Dyeability of Textile Fabrics",slug:"surface-modification-methods-for-improving-the-dyeability-of-textile-fabrics",totalDownloads:7061,totalCrossrefCites:13,totalDimensionsCites:36,abstract:null,book:{id:"3137",slug:"eco-friendly-textile-dyeing-and-finishing",title:"Eco-Friendly Textile Dyeing and Finishing",fullTitle:"Eco-Friendly Textile Dyeing and Finishing"},signatures:"Sheila Shahidi, Jakub Wiener and Mahmood Ghoranneviss",authors:[{id:"58854",title:"Dr.",name:null,middleName:null,surname:"Shahidi",slug:"shahidi",fullName:"Shahidi"}]},{id:"68157",doi:"10.5772/intechopen.87968",title:"Introductory Chapter: Textile Manufacturing Processes",slug:"introductory-chapter-textile-manufacturing-processes",totalDownloads:4473,totalCrossrefCites:14,totalDimensionsCites:26,abstract:null,book:{id:"8892",slug:"textile-manufacturing-processes",title:"Textile Manufacturing Processes",fullTitle:"Textile Manufacturing Processes"},signatures:"Faheem Uddin",authors:[{id:"228107",title:"Prof.",name:"Faheem",middleName:null,surname:"Uddin",slug:"faheem-uddin",fullName:"Faheem Uddin"}]}],mostDownloadedChaptersLast30Days:[{id:"68157",title:"Introductory Chapter: Textile Manufacturing Processes",slug:"introductory-chapter-textile-manufacturing-processes",totalDownloads:4488,totalCrossrefCites:16,totalDimensionsCites:27,abstract:null,book:{id:"8892",slug:"textile-manufacturing-processes",title:"Textile Manufacturing Processes",fullTitle:"Textile Manufacturing Processes"},signatures:"Faheem Uddin",authors:[{id:"228107",title:"Prof.",name:"Faheem",middleName:null,surname:"Uddin",slug:"faheem-uddin",fullName:"Faheem Uddin"}]},{id:"41411",title:"Textile Dyes: Dyeing Process and Environmental Impact",slug:"textile-dyes-dyeing-process-and-environmental-impact",totalDownloads:20676,totalCrossrefCites:101,totalDimensionsCites:320,abstract:null,book:{id:"3137",slug:"eco-friendly-textile-dyeing-and-finishing",title:"Eco-Friendly Textile Dyeing and Finishing",fullTitle:"Eco-Friendly Textile Dyeing and Finishing"},signatures:"Farah Maria Drumond Chequer, Gisele Augusto Rodrigues de Oliveira, Elisa Raquel Anastácio Ferraz, Juliano Carvalho Cardoso, Maria Valnice Boldrin Zanoni and Danielle Palma de Oliveira",authors:[{id:"49040",title:"Prof.",name:"Danielle",middleName:null,surname:"Palma De Oliveira",slug:"danielle-palma-de-oliveira",fullName:"Danielle Palma De Oliveira"},{id:"149074",title:"Prof.",name:"Maria Valnice",middleName:null,surname:"Zanoni",slug:"maria-valnice-zanoni",fullName:"Maria Valnice Zanoni"},{id:"153502",title:"Ph.D.",name:"Farah",middleName:null,surname:"Chequer",slug:"farah-chequer",fullName:"Farah Chequer"},{id:"153504",title:"MSc.",name:"Gisele",middleName:null,surname:"Oliveira",slug:"gisele-oliveira",fullName:"Gisele Oliveira"},{id:"163377",title:"Dr.",name:"Juliano",middleName:null,surname:"Cardoso",slug:"juliano-cardoso",fullName:"Juliano Cardoso"},{id:"163393",title:"Dr.",name:"Elisa",middleName:null,surname:"Ferraz",slug:"elisa-ferraz",fullName:"Elisa Ferraz"}]},{id:"49647",title:"Fiber Selection for the Production of Nonwovens",slug:"fiber-selection-for-the-production-of-nonwovens",totalDownloads:10568,totalCrossrefCites:9,totalDimensionsCites:17,abstract:"The most significant feature of nonwoven fabric is made directly from fibers in a continuous production line. While manufacturing nonwovens, some conventional textile operations, such as carding, drawing, roving, spinning, weaving or knitting, are partially or completely eliminated. For this reason the choice of fiber is very important for nonwoven manufacturers. The commonly used fibers include natural fibers (cotton, jute, flax, wool), synthetic fibers (polyester (PES), polypropylene (PP), polyamide, rayon), special fibers (glass, carbon, nanofiber, bi-component, superabsorbent fibers). Raw materials have not only delivered significant product improvements but also benefited people using these products by providing hygiene and comfort.",book:{id:"5062",slug:"non-woven-fabrics",title:"Non-woven Fabrics",fullTitle:"Non-woven Fabrics"},signatures:"Nazan Avcioglu Kalebek and Osman Babaarslan",authors:[{id:"119775",title:"Prof.",name:"Osman",middleName:null,surname:"Babaarslan",slug:"osman-babaarslan",fullName:"Osman Babaarslan"},{id:"175829",title:"Dr.",name:"Nazan",middleName:null,surname:"Kalebek",slug:"nazan-kalebek",fullName:"Nazan Kalebek"}]},{id:"41409",title:"Surface Modification Methods for Improving the Dyeability of Textile Fabrics",slug:"surface-modification-methods-for-improving-the-dyeability-of-textile-fabrics",totalDownloads:7063,totalCrossrefCites:13,totalDimensionsCites:36,abstract:null,book:{id:"3137",slug:"eco-friendly-textile-dyeing-and-finishing",title:"Eco-Friendly Textile Dyeing and Finishing",fullTitle:"Eco-Friendly Textile Dyeing and Finishing"},signatures:"Sheila Shahidi, Jakub Wiener and Mahmood Ghoranneviss",authors:[{id:"58854",title:"Dr.",name:null,middleName:null,surname:"Shahidi",slug:"shahidi",fullName:"Shahidi"}]},{id:"55424",title:"Textile Reinforced Structural Composites for Advanced Applications",slug:"textile-reinforced-structural-composites-for-advanced-applications",totalDownloads:3876,totalCrossrefCites:9,totalDimensionsCites:16,abstract:"Textile-reinforced composites are increasingly used in various industries such as aerospace, construction, automotive, medicine, and sports due to their distinctive advantages over traditional materials such as metals and ceramics. Fiber-reinforced composite materials are lightweight, stiff, and strong. They have good fatigue and impact resistance. Their directional and overall properties can be tailored to fulfill specific needs of different end uses by changing constituent material types and fabrication parameters such as fiber volume fraction and fiber architecture. A variety of fiber architectures can be obtained by using two- (2D) and three-dimensional (3D) fabric production techniques such as weaving, knitting, braiding, stitching, and nonwoven methods. Each fiber architecture/textile form results in a specific configuration of mechanical and performance properties of the resulting composites and determines the end-use possibilities and product range. This chapter highlights the constituent materials, fabric formation techniques, production methods, as well as application areas of textile-reinforced composites. Fiber and matrix materials used for the production of composite materials are outlined. Various textile production methods used for the formation of textile preforms are explained. Composite fabrication methods are introduced. Engineering properties of textile composites are reviewed with regard to specific application areas. The latest developments and future challenges for textile-reinforced composites are presented.",book:{id:"5921",slug:"textiles-for-advanced-applications",title:"Textiles for Advanced Applications",fullTitle:"Textiles for Advanced Applications"},signatures:"Nesrin Sahbaz Karaduman, Yekta Karaduman, Huseyin Ozdemir\nand Gokce Ozdemir",authors:[{id:"175839",title:"Ph.D.",name:"Nesrin",middleName:null,surname:"Sahbaz Karaduman",slug:"nesrin-sahbaz-karaduman",fullName:"Nesrin Sahbaz Karaduman"},{id:"201620",title:"Dr.",name:"Yekta",middleName:null,surname:"Karaduman",slug:"yekta-karaduman",fullName:"Yekta Karaduman"},{id:"201621",title:"Dr.",name:"Hüseyin",middleName:null,surname:"Özdemir",slug:"huseyin-ozdemir",fullName:"Hüseyin Özdemir"},{id:"201622",title:"Dr.",name:"Gökce",middleName:null,surname:"Özdemir",slug:"gokce-ozdemir",fullName:"Gökce Özdemir"}]}],onlineFirstChaptersFilter:{topicId:"1376",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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