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\r\n\tThe Japanese were revolutionizing quality improvement. As a result, Japan adopted a "total quality" approach to its strategies. In the United States, Total Quality Management (TQM) encompasses not only statistics but also approaches that encompass the entire organization. There were several subsequent quality-management initiatives.
\r\n\tIn 1986, Motorola developed Six Sigma to improve its business processes by minimizing defects. A philosophy that views all work as a process, which can be identified, measured, analyzed, improved, and controlled. Generally, "Six Sigma quality" is an indicator that a process is well controlled.
\r\n\tLean manufacturing (1988), also known as just-in-time manufacturing, derives from Toyota's 1930 operating model "The Toyota Way." Lean describes a set of management practices that reduce waste and increase productivity.
\r\n\tThe ISO 9000 series of quality-control standards appeared in 1987. ISO 9001 integrates a process-oriented approach into enterprise management based on the plan-do-check-act method. The quality movement has matured as we enter the 21st century. ISO 9000 was revised in 2000 to emphasize customer satisfaction. The fifth edition of ISO 9001, published in 2015, emphasizes risk-based thinking to improve the application of the process approach. In addition to the manufacturing sector, quality has moved into service, healthcare, education, and government. For example, through standards such as ISO/IEC 17025 for testing and calibration laboratories and ISO 15189 for medical laboratories.
\r\n\tMore recently, it has been recognized that the Fourth Industrial Revolution, Industry 4.0, best defines the present industry model. As its part, "Quality 4.0" refers to the future of quality and organizational excellence.
\r\n\tThe book will aim to introduce a comprehensive overview of the up-to-date models used in quality management systems by experts in the field.
",isbn:"978-1-80356-729-7",printIsbn:"978-1-80356-728-0",pdfIsbn:"978-1-80356-730-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"7c2744454ba90e8d6cf507e167cc3779",bookSignature:"Dr. Paulo Pereira and Dr. Sandra Xavier",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11527.jpg",keywords:"Leadership, Customer Focus, Improvement, Process Approach, Planning, CAPA, Audit, Management Review, Evaluation, Lean Sigma, DMAIC, DMADV",numberOfDownloads:8,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 24th 2022",dateEndSecondStepPublish:"June 8th 2022",dateEndThirdStepPublish:"August 7th 2022",dateEndFourthStepPublish:"October 26th 2022",dateEndFifthStepPublish:"December 25th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Pereira received his Ph.D. from the Catholic University of Portugal. He has over 20 years of experience as a consultant and auditor of quality management systems, and over 16 years of experience as a quality manager. He has been recruited as a quality and laboratory expert for seminars and professional laboratory meetings throughout Europe, Africa, and South America. Currently, he is the head of the R&D Department at the Portuguese Institute of Blood and Transplantation; a CLSI and EURACHEM fellow.",coeditorOneBiosketch:"Dr. Xavier received her Ph.D. from the University of Lisbon, Portugal. She has extensive experience in teaching and managing quality systems, and is a member of several Scientific Commissions and editorial boards.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"178637",title:"Dr.",name:"Paulo",middleName:null,surname:"Pereira",slug:"paulo-pereira",fullName:"Paulo Pereira",profilePictureURL:"https://mts.intechopen.com/storage/users/178637/images/system/178637.jpeg",biography:"Dr. Pereira received his Ph.D. in Biotechnology from the Catholic University of Portugal. He has been recruited as a quality and laboratory expert for seminars and professional laboratory meetings throughout Europe, Africa, and South America. He has more than twenty-five years of experience working in medical laboratories, having held key scientific leadership roles: 15+ years as a senior researcher; 10+ years as a consultant for a metrology laboratory based on ISO/IEC 17025 specifications and related standards; 20+ years as a consultant and auditor of quality management systems based on ISO 9001, ISO/IEC 17025, and ISO 15189 standards; 16+ years as the quality manager in the Portuguese Institute of Blood and Transplantation, including more than 6 years in national coordination; and 6+ years as a professor of Quality Assurance. Currently, he is the head of the R&D Department at the Portuguese Institute of Blood and Transplantation, Lisbon, Portugal. Dr. Pereira is the author of several peer-reviewed scientific articles and indexed books and chapters. He is an editor for several books. He serves as a member of several editorial boards. He is a member of the Clinical Laboratory and Standards Institute and Eurachem. 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Dr. Xavier is the author of several books’ chapter and scientific articles in indexed journals. She serves as a reviewer of scientific and technical articles in indexed journals. She is also a recognized speaker at various national and international meetings. She is a member of juries for the attribution of academic degrees. 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Normal human pregnancy is associated with profound alterations to the haemostatic balance wherein the pro-coagulant effects become dominant. A marked progressive increase in the concentration of pro-coagulant proteins (Factors VII, VIII, X, Von Willebrand factor, Fibrinogen) is observed in blood plasma at all stages of pregnancy. These changes coincide with decreases in natural anticoagulants and lower levels of natural fibrinolytic agents and are more pronounced in the uteroplacental circulation than in the systemic circulation during pregnancy [7]. In parallel, markers of platelet activation are markedly elevated throughout normal pregnancy suggestive of a functional role for platelet activation during normal gestation.
It is often argued that this altered haemostatic status is required as the maternal coagulation system prepares for the challenges of parturition, and aims to minimize intrapartum blood loss. However, the alterations in the haemostatic system begin as early as the first trimester, suggesting a requirement for such changes in the proper progression of the early stages of pregnancy, in addition to their role in regulation of post-partum bleeding. For example, alterations in haemostasis enable the necessary changes in the uteroplacental vasculature to support the establishment of the trophoblast invasion of the spiral arteries of the uterus early in gestation [12].
The altered haemostatic status during normal pregnancy presents a number of physiological challenges in the vasculature and results in an increased risk of excessive thrombosis, especially within the uteroplacental circulation. This enhanced pregnancy-associated thrombotic risk may provide the mechanistic basis for many of the major pregnancy complications, such as pre-eclampsia, HELLP syndrome (Hemolysis, Elevated Liver enzymes and Low Platelet count) and intrauterine growth retardation (IUGR) [13].
Pre-eclampsia (PE) is a multifaceted disorder that complicates between 2 and 8% of all pregnancies. It is the most common causes of morbidity and mortality in mothers and babies in the Western world [14]. Clinical signs of disease, such as an elevated maternal blood pressure and proteinuria, become apparent as early as gestation week-20 [9]. Such symptoms can dictate urgent delivery of a preterm baby (<40 weeks). The underlying pathology appears to be a complex interaction of the placental and maternal tissues [15] that leads to generalized endothelial dysfunction. This heightens the normal shift of haemostatic equilibrium toward hypercoagulability. As part of this generalized hypercoagulability status in pregnancy, evidence for enhanced platelet activation, observed in normotensive pregnancies [18], are further increased in women with preeclampsia giving rise to the formation of platelet-derived microthrombi in smaller vessels [20] and an associated disseminated intravascular coagulation [21]. Similar to changes in the coagulation parameters, changes in platelet activation status are recorded before gestational week 13 in PE. Intrauterine growth retardation is often associated with PE, and is correlated with reduced uteroplacental blood flow caused by platelet-rich microthrombi.
The normal blood platelet count in healthy non-pregnant individuals varies between 150-400 X 109/L. Early studies into platelet parameters in normotensive pregnant women reported a progressive drop in the normal platelet count [22]. Indeed mild, non clinical, thrombocytopenia (platelets 100-150 x 109/L), is observed in up to 10% of all pregnancies [6]. It is likely that this is largely a haemodilution effect that results from the maternal blood volume expansion [23]. The decrease in platelet count is accompanied by an increase in mean platelet volume [24] and a notable change in the granule content [25].
Changes in platelet function, and platelet activation status, have been widely reported during pregnancy. However, measurement of platelet activation is complex. There is no one accepted index of platelet activation, although a number of tests and assays are used as surrogate markers of platelet activation in studies on gestational platelet activation. Usually a blood sample is collected from the patient and mixed with an appropriate anticoagulant before being transported to a laboratory for analysis. Platelets are assayed either in whole blood samples or are processed to remove red blood cells and plasma to yield platelet-rich plasma (PRP) or washed platelets (WP), respectively. Thereafter standard or high throughput assays are utilized as appropriate. Platelet aggregation assays are used frequently (Light Transmission Aggregometry; LTA) and can be performed in PRP or WPs [27]. Alternatively high throughput assays of platelet function can be used to assess multiple samples, or multiple agonists, simultaneously. The Moran group have recently developed a high throughput assay of platelet dense-granule secretion to permit extensive assessment of a dose-range of agonists on a relatively small blood sample. In addition, mobile diagnostic analysers of platelet function, such as the PFA-100, have been developed to assess the acute capacity of platelets to form thrombotic aggregates and can be performed in whole blood [31].
In addition to standard assays of platelet function, platelet activation may be inferred, from the presence of activated cell adhesion molecules on the surface of isolated platelets. Such markers include the activated platelet integrin αIIbβ3 (assessed by the monoclonal antibody PAC-1 or fibrinogen-binding) or the enhanced surface expression of the α-granule protein P-Selectin (CD62P) or dense-granule derived protein, CD63 on the surface of circulating platelets.
Platelet granules are rich sources of bioactive agents that are selectively released in response to diverse platelet activating stimuli. Thus, evidence of the presence of secreted platelet-derived bioactive agents in plasma or urine can be used to determine if platelet activation has occurred
Proteins that are shed from the surface of activated platelets are also used as surrogate markers of platelet activation. These include soluble P-Selectin (CD62-P) and soluble CD40 Ligand (CD154). Activated platelets also release microparticles (MPs) [43]. However, MPs found in blood may originate from a number of different sources in addition to platelet α-granules. Hence they have not been widely used in assessments of platelet function in pregnancy.
Basal levels of P-Selectin and αIIbβ3 are progressively elevated on the surface of platelets during gestation, suggesting that an inherent activation of platelets occurs during normal pregnancy. In addition, the capacity of platelets to aggregate [31] and adhere [32] in response to various stimuli is enhanced in normotensive pregnant women but is somewhat reduced in platelets from women with preeclampsia [45]. Enhanced fibrinogen binding to circulating platelets, indicative of platelet activation, is also observed in pregnant women compared to non-pregnant subjects [34]. Plasma levels of βTG and PF4, secreted from platelet α-granules, and ADP/ adenosine, secreted from platelet dense granules are elevated during pregnancy.
Many indices of platelet activation have been shown to correlate with gestational age [32], though some, such as platelet responsiveness to agonist stimulation, peak at weeks 30-36 and decline thereafter (Table 1). Only a few studies have assessed platelet function at multiple time points throughout pregnancy and so, information is limited regarding the absolute indices of platelet activation and their temporal relationship to gestational events. However, in general it can be concluded that platelets are hyperactivated from as early as gestational week 10, in the first trimester of pregnancy.
The gestational changes in haemostatic and platelet responses are altered in women with PE. Accordingly, β-TG and PF4 are elevated in PE above levels observed in normotensive pregnancy. CD63 and P-Selectin levels are increased on the surface of platelets from patients with PE, indicative of recent secretion of dense and α-granules, respectively. However, the lack of elevated plasma levels of adenosine or ATP suggest that the secretory events were not acute [26]. The half-life of adenosine is less than 1 minute in plasma in contrast to β-TG, which has a half-life > 120 minutes. Thus, the data suggests that a slow sustained secretion from platelet granules occurs. This is distinct from the pattern that is expected following a thrombotic event where markers of dense granules and α-granule release occur acutely and simultaneously. Furthermore, the capacity of platelets from PE patients to acutely aggregate
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Platelet Aggregation (LTA; PFA-100) | \n\t\t\tResponsiveness is greater in normotensive pregnant women than in non pregnant . Platelet hyperaggragation peaks at weeks 20 & 30; declines at week 36 | \n\t\t\tPlatelet aggregation is decreased in both mildly and severely preeclamptic women in comparison with non-pregnant women . Increased numbers of platelets binding to fibrinogen are observed [20]. | \n\t\t
Surface expression of CD62P on Platelets | \n\t\t\tIncreases with gestational age | \n\t\t\tEvidence of activated, degranulated platelets in the circulation [34]. CD62P expression is accentuated in PIH<PE<severe PE | \n\t\t
Surface expession of CD63 on Platelets | \n\t\t\televated in normotensive women | \n\t\t\tFurther elevated in women with non-proteinuric and proteinuric pre-eclampsia [34]. Increased first-trimester CD63 expression is an independent risk factor for development of preeclampsia | \n\t\t
ATP Secretion | \n\t\t\tIncreases with gestational age [26]. Adenosine in plasma is elevated [46] | \n\t\t\tSignificantly lower in PE than in normotensive pregnant subjects [26] | \n\t\t
Thromboxane metabolites in plasma or Urine | \n\t\t\tIncreased in normotensive pregnancies . Elevated at 20 weeks and continues to increase thereafter | \n\t\t\tAn imbalance between the production of two metabolites of arachidonic acid, thromboxane and prostacyclin, that favors thromboxane. [53] Greater enhancement of TxA2 production in PIH than in normotensive pregnancies | \n\t\t
Plaasma levels of β-Tg/ PF4 (secreted from platelet α-granules) | \n\t\t\tElevatedin normotensive pregnancy; Peaks at gestational weeks 30, & 36 | \n\t\t\tGreater than in normotensive pregnancy | \n\t\t
Soluble CD62P in plasma | \n\t\t\tElevated by 24 weeks | \n\t\t\tNo difference vs normotensive pregnancy | \n\t\t
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Other haemostatic changes that occur in systemic and uteroplacental circulations during normotensive pregnancies appear to be accentuated in patients with preeclampsia [7]. For example, thromboxane A2 biosynthesis is increased in PE above that observed in normotensive pregnancy and appears to correlate with disease severity. It is preceded by a decreased endogenous production of the endothelial-derived eicosanoid, Prostaglandin I2 (PGI2) [55]. The altered ratio of these thrombo-regulatory eicosanoids appears to play a critical pathological role in preeclampsia. In addition, blood-borne microparticles (MPs) are elevated in preeclampsia and have demonstrable adverse effects on endothelial function. Isolated MPs from preeclamptic women, though not those from normotensive pregnant women, have been shown to downregulate endothelial NO production. NO is a critical regulator of platelet activation, and a regulator of the sensitivity of vascular endothelial cells to trophic agents. By altering the endothelial response and reducing the endogenous production of NO in pre-eclamptic vascular beds, MPs in preeclamptic women can markedly affect platelet activation and vascular remodeling. Thus in preeclamptic women, many elements of the delicate balance governing platelet activation appear to be dysregulated, predisposing towards premature and excessive platelet secretion. It is tempting to suggest that this dysregulation underlies the pathophysiological mechanisms in PE.
It is postulated therefore that platelets are hyper-reactive in normal pregnancy, and are primed to undergo granule-release at appropriate times and vascular locations. Thus, platelet passage through the placental vasculature can cause acute platelet activation and secretory events, which result in the release of bioactive mediators from platelet α-granules. The nature of these bioactive mediators and their functional roles in normal, and preeclamptic, pregnancies remain to be elucidated. Women at risk of developing preeclampsia may have an abnormality in their platelet activation or a temporal dysregulation of these events resulting in atypical vascular events. In fact, increased first-trimester CD63 expression on platelets is an independent risk factor for development of preeclampsia, confirming that early subclinical platelet defects occur in this population.
It has been strongly suggested that platelets contribute to protective mechanisms against excessive bleeding during childbirth. Whilst this is indeed relevant, it seems premature to start such thrombotic preparations as early is gestational week 10-12. Thus, platelets must play a role into other gestational requirements. Any roles for platelets are likely to result from their activation in the circulation. However, the mechanisms underlying platelet activation in pregnancy remain largely unknown.
A number of studies have indicated that platelets are hyperresponsive during pregnancy. This may be explained by the increased gestational production of the platelet-activating prostaglandin, thromboxane A2 (TXA2] by platelets. TXA2 can enhance platelet responsiveness to low levels of physiological activation. Thus increases in ambient TXA2 may prime platelets for activation by other agonists. Indeed platelets generate more intracellular calcium in response to standard pro-thrombotic stimuli as pregnancy progresses. In parallel, a reduced synthesis of inhibitory cyclic AMP is observed in platelets from pregnant donors. This reduced production of platelet cAMP may be secondary to altered endothelial function during pregnancy whereby endothelial cells release less of the inhibitory regulators of platelet function (Nitric Oxide and PGI2). Overall, the sensitivity of platelets to activation is enhanced during pregnancy by a parallel increase in ambient pro-thrombotic agents, TxA2 and calcium, and a decrease in anti-thrombotic influences such as intraplatelet cAMP. It is possible that such changes may be initiated by the elevated progesterone level extant during pregnancy, as it has previously been noted that cyclical changes in progesterone during the luteal phase of the ovarian cycle similarly affects platelet function and activation status. Platelets are therefore ‘primed’ to response to stimulation by various gestational adaptions.
The molecular mechanisms underlying the direct activators of platelets in the placental beds remain unclear, but two independent activators have been identified. Firstly, circulating primed, platelets adhere to the extracellular matrix in the uteroplacental vascular beds and are activated to release various soluble factors to regulate trophoblastic vascular infiltration and differentiation. Secondly, local generation of thrombin, and its interaction with platelet thrombin receptors (PARs) is critical for efficient gestation [62]. However, although generated locally from the plasma coagulation cascade, this thrombin does not participate in fibrin cross-linking, the usual culmination step in blood coagulation. Instead, it directly activates platelet PAR receptors and induces platelets to release their stored contents.
The capacity and sensitivity of platelets to secrete their granular contents therefore is enhanced as pregnancy progresses. Indeed, substantial evidence that this occurs is presented in Table 1. Moreover, data from the Moran laboratory confirms that alterations in platelet function are evident as early as 10-12 gestational weeks (unpublished data; MAO, NM).
As discussed above, the gestational role of platelets in pregnancy is likely to be mediated via the secretion of the cargo from platelet α-granules. This cargo is comprised of cytokines and other bioactive agents, stored in the abundant platelet granules and includes coagulation proteins such as Fibrinogen and Factors V and XIII. Up to 800 different proteins have been identified in the platelet α-granule proteome. Moreover, the contents of platelet α-granules can be selectively released in response to discrete activation signals.
In normotensive pregnancy, maternal platelets adhere to the extracellular matrix in the uteroplacental vasculature and are activated in the spiral arteries. As a consequence of this activation of maternal platelets, elevated levels of platelet derived cytokines and bioactive agents are released, that assist and enable trophoblastic arterial infiltration. In addition, platelet derived biomolecules drive morphological changes of trophoblasts and enable angiogenesis in the placental beds. This enables localized physiological vascular remodeling to ensure the appropriate development of embryonic and maternal placental circulations. The precise nature of the platelet releasate in these circumstances has not been fully explored to date. However, there is some evidence that the nature of the releasate differs between normotensive and pre-eclamptic subjects,
Thus, the platelet activation detected in pregnancy (Table 1) is an indicative record of α-granule secretion events in the placental vasculature. Importantly, no overt thrombosis is observed in normotensive pregnancies, despite the localized platelet activation and secretion. The requirement for localized platelet regulation of vascular remodeling probably underlies the need for platelets to be primed at early gestational stages in normal pregnancy. The consequent elevation in markers of platelet activation, as shown in Table 1, is simply a confirmation that platelet secretion events have occurred, rather than being suggestive of a pro-thrombotic role for platelets during normal gestation.
Platelets serve as mobile stores of active biomolecules that can be transported around the body via the vasculature. This function of platelets is well defined in cancer, where platelet stored biomolecules can be released in a bespoke manner by circulating tumour cells to enable the development of a novel, bespoke network of new blood vessels to supply a growing metastatic tumour with suitable nutrients and waste removal functions (See Chapter 6: Mitrugno et al). It is tempting to speculate therefore, that platelets store a relevant and bespoke collection of bioactive compounds during pregnancy. Thus platelet α-granules serve as vectors of biological messages for vascular homeostasis during pregnancy. Similarly, platelet-derived MPs may serve to deliver bespoke cocktails of bioactive molecules to uteroplacental vascular beds to facilitate the required vascular changes to support gestation. In support of this, MPs from women with preeclampsia, compared to those from normotensive pregnant women, showed greater pro-inflammatory effects on the vascular wall, inducing vascular hyporeactivity in small blood vessels [65].
However, the key question then becomes one of how the body balances the dual needs of requiring primed platelets to release their cargo at relevent vascular beds in the dynamic, fast-flowing environment of the uteroplacental vasculature, whilst preventing inappropriate thrombosis from occurring; yet maintaining the potential to respond to thrombotic needs should they arise elsewhere in the body.
Pregnancy-specific glycoproteins (PSGs) are a family of soluble cell adhesion proteins found in the plasma at various stages of pregnancy. They are largely derived from trophoblastic cells during pregnancy and are abundantly expressed in maternal blood [66]. There are ten human PSG genes (PSG1 - PSG9, PSG11). Several recent studies have indicated that individual PSGs have immunoregulatory, pro-angiogenic, and anti-platelet functions, though their precise functions during pregnancy remain largely speculative.
Recently Shanley et al demonstrated that PSG1 had a high affinity for binding to the major platelet integrin αIIbβ3. It competes with fibrinogen for binding to this integrin, thereby inhibiting local platelet aggregation [27]. However, the capacity of platelets to secrete the contents of their α-and dense granules is unaffected by PSG1. Thus PSG1 enables the secretory responses of circulating platelets, whilst attenuating their thrombotic tendencies.
In parallel, a role for PSG1 in the activation of the anti-inflammatory cytokine, transforming growth factor-beta (TGF) has recently been established. TGF-β regulates many biological events essential for the successful completion of pregnancy including trophoblast invasion and proliferation, angiogenesis, and tolerance to the fetal to the fetal allograft during pregnancy. Of note, platelets serve as a major storage site for TGF-β and release it in response to platelet-activating stimuli. Plasma concentrations of active TGF-β are significantly higher in preeclamptic women than in normotensive pregnant women [70]. In the presence of a high concentration of PSG1, TGF-β, released from primed platelets in the maternal uteroplacental circulation, is activated and enabled to exert its vascular remodeling effects. Yet due to the high locally-produced concentrations of PSG1, aggregation of platelets does not occur.
Thus PSG1 can simultaneously inhibit platelet aggregation and enable the release of platelet granular-contents including TGF-β, to promote vascular remodeling. This strongly supports the contention that the role of platelet activation during pregnancy is to permit the local delivery of cytokines, via the secretion of α-granule contents, rather than the more widely accepted role of inducing thrombotic events. It is likely that complications of pregnancy such as preeclampsia arise when the balance between the thrombotic function of platelets and their secretory functions is disturbed.
As discussed previously, platelets are primed for activation at early gestational stages. One of the mechanisms of platelet priming is their enhanced ability to synthesize the platelet-specific prothrombotic eicosanoid TxA2, in an environment where opposing antithrombotic influences, namely PGI2 and NO, are downregulated. Measuring TxA2 levels during pregnancy can therefore yield important information on platelet status during gestation.
The concentrations of TxA2 observed in normotensive pregnancy are significantly raised above levels seen in normal healthy non-pregnant donors. In fact, gestational TxA2 levels equate to pathological concentrations identified in cardiovascular patients (Table 2). Yet, despite these pathological levels of circulating prothrombotic eicosanoids, there is little evidence for enhanced platelet-rich thrombotic events during normotensive pregnancy, confirming therefore, that the intended physiological purpose of the elevated platelet activation is not thrombotic in nature. Together, these studies suggest an underlying physiological balance during pregnancy to prime platelets for activation whilst regulating thrombosis.
The need to understand the physiological and molecular mechanisms underlying the enhanced gestational platelet activation is underscored by observations that platelet activation is further accentuated in patients with PE and that pathological thrombotic events can occur if the balance is disturbed.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Non-pregnant, healthy subjects | \n\t\t\tBasal levels approx. 275 pg/mg creatinine | \n\t\t\t\n\t\t |
Normotensive, pregnant subjects | \n\t\t\t3 fold | \n\t\t\t\n\t\t |
Patients with Preeclampsia | \n\t\t\t | \n\t\t\t\n\t\t |
Patients with Ischemic disease | \n\t\t\t | \n\t\t\t\n\t\t |
Please add caption
Thus, bioactive agents are released from platelets in a controlled, systematic way during pregnancy to enable required hemostatic changes in the uteroplacental vasculature. However, platelet aggregation and thrombus formation are not required, or could be contraindicated during pregnancy, and are therefore suppressed by PSG1 [27] or similar soluble proteins.
Our limited knowledge of how platelets might contribute to complications of pregnancy evolves from our understanding of the nature of the delicate balance between platelet-activating forces during normotensive pregnancy and the need to prevent thrombosis in the uteroplacental vascular beds. Thus, under the influence of gestational progesterone, endothelial cells produce less NO and PGI2 and platelets produce more TXA2. This tilts the haemostatic balance towards platelet activation and thrombosis. Exposed extracellular matrix proteins in the placental vascular beds bind and activate the primed platelets inducing secretion of their α-granule contents. Any tendency for platelets to aggregate is however balanced by the local production of high concentrations of PSG1 [27], which prevents integrin mediated platelet aggregation. In parallel, local generation of Thrombin, the procoagulant, proteolytic enzyme of the coagulation cascade, occurs in normotensive pregnancy [7]. Yet its function is not to generate fibrin, as would be expepcted in the coagulation cascade. Instead, thrombin in the placental beds acts directly on platelet thrombin receptors to induce platelet release from α-granules. The fibrinogenic actions of thrombin are moderated by Thrombomodulin (TM) and are essential for successful development of the placenta [62]. Interestingly, TM is also stored in platelet α-granules, comprising up to 10% of total granule stores, and is released in response to platelet activation [74]. Absence of TM causes fatal arrest of placental morphogenesis in mice, but this action is not related to its ability to affect fibrin formation, leading the authors to conclude that TM regulates the capacity of thrombin to activate platelets [62]. Moreover, the experiments of Sood and coleagues demonstrate that the delicate balance between the required functions of platelets in pregnancy and their regulation, to prevent unwanted thrombosis, can all-too-easily be shifted to cause placental failure and complications of pregnancy.
Therefore we suggest that platelets are not minor participants in the gestational events of pregnancy. Instead, they are active mediators of the complex regulatory system which has several, as yet uncharacterized mechanisms. They serve as vectors for vascular homeostasis during pregnancy, that co-ordinate a delicate balance between delivering relevant and potent biological messages through their granule-delivery system, whilst avoiding platelet-related thrombotic events.
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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Common urticaria is generally easy to diagnose; however, urticarial syndromes should be considered in cases where lesions persist for greater than 24–36 h, the location of lesions has bilateral symmetry, urticarial lesions are accompanied by additional elementary lesions, and/or the patient presents with additional systemic symptoms. Additionally, urticarial syndromes should be considered for patients with typical urticarial lesions that do not respond to systemic antihistamine treatment. Hyperpigmentation or bruising can be observed following resolution of urticarial syndromes. Many cutaneous and systemic diseases can cause urticarial syndromes. Systemic causes of urticarial syndromes can affect multiple organ systems and may be accompanied by systemic symptoms such as fever, asthenia, and arthralgia. Clinicopathologic correlation is essential for the accurate diagnosis of urticarial syndromes. In this chapter, cutaneous and systemic etiologies of urticarial syndromes are reviewed.",book:{id:"5800",slug:"a-comprehensive-review-of-urticaria-and-angioedema",title:"Urticaria and Angioedema",fullTitle:"A Comprehensive Review of Urticaria and Angioedema"},signatures:"Hilal Gokalp and Isil Bulur",authors:[{id:"193580",title:"M.D.",name:"Hilal",middleName:null,surname:"Gokalp",slug:"hilal-gokalp",fullName:"Hilal Gokalp"},{id:"201704",title:"Dr.",name:"Isil",middleName:null,surname:"Bulur",slug:"isil-bulur",fullName:"Isil Bulur"}]},{id:"33020",title:"Anti-Tumour Necrosis Factor-α Induced Systemic Lupus Erythematosus",slug:"anti-tnf-alpha-induced-sle",totalDownloads:3202,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"816",slug:"systemic-lupus-erythematosus",title:"Systemic Lupus Erythematosus",fullTitle:"Systemic Lupus Erythematosus"},signatures:"Hani Almoallim and Hadeel Khadawardi",authors:[{id:"65113",title:"Dr.",name:"Hani",middleName:null,surname:"Almoallim",slug:"hani-almoallim",fullName:"Hani Almoallim"},{id:"123886",title:"Dr.",name:"Hadeel",middleName:null,surname:"Khadawardi",slug:"hadeel-khadawardi",fullName:"Hadeel Khadawardi"}]},{id:"54506",title:"Pseudoangioedema",slug:"pseudoangioedema",totalDownloads:1777,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Angioedema is a rapid, localized and temporary subcutaneous edema, which targets the lips, eyelids, gastrointestinal and respiratory mucosa resulting in abdominal pain, asthma and even serious life‐threatening conditions like airway obstruction. There are several other disorders such as allergic contact dermatitis, drug rash with eosinophilia and systemic symptoms (DRESS), superior vena cava syndrome (SVCS), orofacial granulomatosis and so on, which manifest with subcutaneous swelling and masquerade as angioedema and are known as ‘pseudoangioedema’ in the literature. Knowledge of pseudoangioedema for healthcare professionals is crucial to avoid potentially serious results of misdiagnosis such as further investigations, unnecessary applications and delayed diagnosis. We aim to discuss differential diagnosis of angioedema and help physicians recognize the typical features of angioedema and its differential diagnosis in this chapter.",book:{id:"5800",slug:"a-comprehensive-review-of-urticaria-and-angioedema",title:"Urticaria and Angioedema",fullTitle:"A Comprehensive Review of Urticaria and Angioedema"},signatures:"Sevgi Akarsu and Ecem Canturk",authors:[{id:"182444",title:"Prof.",name:"Sevgi",middleName:null,surname:"Akarsu",slug:"sevgi-akarsu",fullName:"Sevgi Akarsu"},{id:"200700",title:"Dr.",name:"Ecem",middleName:null,surname:"Canturk",slug:"ecem-canturk",fullName:"Ecem Canturk"}]},{id:"54456",title:"Urticaria and Angioedema",slug:"urticaria-and-angioedema",totalDownloads:1572,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Urticaria is a common mast cell–mediated dermatosis presenting with pruritic erythematous superficial plaques also known as hives or wheals. Angioedema is an acute condition manifesting as localized edema affecting the skin and mucous membranes. In contrast with urticaria, itching is often absent, the skin appears normal and the edema occurs in deeper dermal and subcutaneous tissues in angioedema. Spontaneous urticaria can either be acute lasting less than 6 weeks or chronic with a duration of more than 6 weeks. In acute urticaria cases, an underlying cause, mostly medications, foods and infections, may be found in approximately 50% of patients. However, spontaneous urticaria is generally idiopathic. First-line treatment option for both acute and chronic urticaria is non-sedating H1 antihistamines. Patients with recalcitrant disease are candidates for therapy with corticosteroids, immunosuppressives or omalizumab treatment. There are two different mechanisms causing angioedema. The first is mast cell mediated and is considered to be part of the spectrum of spontaneous or inducible urticarias. Patients present with angioedema alone or angioedema combined with urticaria. The second is bradykinin-induced angioedema, as observed in the hereditary angioedema and angiotensin-converting enzyme (ACE) inhibitor–induced angioedema.",book:{id:"5800",slug:"a-comprehensive-review-of-urticaria-and-angioedema",title:"Urticaria and Angioedema",fullTitle:"A Comprehensive Review of Urticaria and Angioedema"},signatures:"Burhan Engin, Muazzez Çiğdem Oba and Server Serdaroğlu",authors:[{id:"64793",title:"Prof.",name:"Burhan",middleName:null,surname:"Engin",slug:"burhan-engin",fullName:"Burhan Engin"},{id:"64794",title:"Mr.",name:"Server",middleName:null,surname:"Serdaroglu",slug:"server-serdaroglu",fullName:"Server Serdaroglu"},{id:"194868",title:"Dr.",name:"M Çiğdem",middleName:null,surname:"Oba",slug:"m-cigdem-oba",fullName:"M Çiğdem Oba"}]},{id:"54611",title:"Chronic Inducible Urticaria Part I",slug:"chronic-inducible-urticaria-part-i",totalDownloads:1318,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Urticaria is a common mast cell‐driven disease which is characterized by red, itchy swellings. Urticaria, that persists more than 6 weeks in a repetitive manner (each lesion disappearing in <24 h), is called chronic urticaria. Chronic urticaria can be either spontaneous without the need of a trigger, or inducible in which with a known trigger the lesions can be provoked. Chronic inducible urticarias include the physical urticarias and some other forms such as cholinergic urticaria.",book:{id:"5800",slug:"a-comprehensive-review-of-urticaria-and-angioedema",title:"Urticaria and Angioedema",fullTitle:"A Comprehensive Review of Urticaria and Angioedema"},signatures:"Murat Borlu, Salih Levent Cinar and Demet Kartal",authors:[{id:"37023",title:"Prof.",name:"Murat",middleName:null,surname:"Borlu",slug:"murat-borlu",fullName:"Murat Borlu"},{id:"205625",title:"Dr.",name:"Salih Levent",middleName:null,surname:"Cinar",slug:"salih-levent-cinar",fullName:"Salih Levent Cinar"},{id:"205630",title:"Dr.",name:"Demet",middleName:null,surname:"Kartal",slug:"demet-kartal",fullName:"Demet Kartal"}]}],onlineFirstChaptersFilter:{topicId:"1036",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:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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