Food and bioactive compounds used in the treatment of T2DM.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"6597",leadTitle:null,fullTitle:"Elasticity of Materials - Basic Principles and Design of Structures",title:"Elasticity of Materials",subtitle:"Basic Principles and Design of Structures",reviewType:"peer-reviewed",abstract:'In the science of physics, elasticity is the ability of a deformable body (e.g., steel, aluminum, rubber, wood, crystals, etc.) to resist a distorting effect and to return to its original size and shape when that influence or force is removed. 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Kosyachenko"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"55410",title:"Functional and Biological Potential of Bioactive Compounds in Foods for the Dietary Treatment of Type 2 Diabetes Mellitus",doi:"10.5772/intechopen.68788",slug:"functional-and-biological-potential-of-bioactive-compounds-in-foods-for-the-dietary-treatment-of-typ",body:'Type 2 diabetes mellitus (T2DM), also called noninsulin-dependent diabetes, is a complex and multifactorial disease. This review describes T2DM in the framework of oxidative stress and the inflammatory process, since its main etiological factor is obesity. These mechanisms can lead to various metabolic alterations, which have been proposed to be part of their chronicity and complexity [1].
According to the World Health Organization (WHO), there are 350 million people with diabetes worldwide, whereas the International Diabetes Federation (IDF) estimates that by 2013, 382 million people worldwide were diagnosed with some type of diabetes. This figure is expected to increase to 592 million by 2035 [2].
As a response to the increase in diseases related to the modern lifestyle, functional foods, such as soybean, nopal, oats, and foods with high antioxidant and omega-3 content, were developed in Japan in the 1980s, and these have become important alternatives for improving nutrition and public health. Hence, research into the benefits or effects of functional foods on T2DM is crucial and can determine whether these can be a true alternative for the prevention and control of this pathology, as well as for associated metabolic effects.
The alteration of some cellular biochemical processes is mainly caused by factors such as over-nutrition and decreased physical activity in the individual, as for glucose metabolism, specifically hyperglycemia, which in turn triggers:
Cell overload of free fatty acids
Endothelial dysfunction
Insulin resistance in muscle
Impaired insulin secretion in the beta cells of the pancreas.
T2DM includes several alterations in metabolism, including hyperglycemia, insulin resistance, dyslipidemia, and chronic low-grade inflammation, and these alterations arise from oxidative stress [3].
Oxidative stress is defined as the biochemical imbalance caused by the overproduction of reactive species (RS) and free radicals (FR) that cause oxidative damage to membrane lipids, carbohydrates, proteins, and DNA. In people with T2DM, free radicals are found in high concentrations, causing damage to various organs, such as the heart and blood vessels. This has been described as a risk factor for the development of complications in this disease [4].
As mentioned above, the excess of FR leads to the oxidation of macromolecules, which in turn leads to lesions at the cellular level; among them, the following effects are described:
Lipids: During lipid peroxidation, unsaturated fatty acids react (in chains) with molecular oxygen and hydroperoxides are formed, which are degraded into various products, such as conjugated dienes, alkanes, aldehydes, and isoprostanes, among others. Damage from oxidation can affect both the lipids in cell membranes and those contained in plasma lipoproteins. In the first case, this would cause inadequate cellular functioning, which is presumed to be one of the causes of premature aging experienced by some individuals with diabetes [5].
In the case of plasma lipoproteins, damage to these in all known cases is derived from the oxidation of their lipids. Alterations of high density lipoproteins (HDL) and very low density lipoproteins (VLDL) can affect reverse cholesterol transport and clarification of plasma triglycerides, respectively [6].
On the other hand, the peroxidation of low-density lipoproteins (LDL) constitutes the major contribution of FR to the genesis and aggravation of atherosclerosis. Oxidative modifications of LDL confer greater atherogenic power on this macromolecule [6, 7].
It is also known that in diabetic patients with unacceptable metabolic control, there is greater susceptibility of LDL to oxidation and more oxidized LDL than in those with optimal control [6, 7].
Protein: The mechanisms of damage in each radical-generating system may be different and may also vary depending on the affected protein. Oxidative modification of proteins increases their degradability and susceptibility to proteolysis, probably due to their increased hydrophobicity, which implies more rapid ubiquitination and degradation by the lysosomal pathway. Likewise, the alteration of free radical proteolysis is manifested both in intracellular protein catabolism and in extracellular protein systems, especially in proteins of the extracellular matrix [8].
One protein that can undergo oxidative damage in people with T2DM is insulin. Oxidative damage causes chemical and structural changes in this hormone and, as a consequence, a loss of its biological function. It has been shown that human adipose tissue in the presence of oxidized insulin does not use glucose with the same efficiency as with native insulin [9].
Also, carbonyl stress can also affect insulin receptors, and the molecules involved in the cellular response are appropriate to insulin stimulation [9].
Deoxyribonucleic acid (DNA): There are many phenomena, associated with mutations and carcinogenesis, which are caused by damage to DNA. These include loss of expression or synthesis of a protein by damage to a specific gene, oxidative modifications of bases, fragmentations, stable interactions of DNA-proteins, chromosomal rearrangements, and demethylation of cytokines of the DNA that activates genes. The damage may be effected by such alterations; for example, via inactivation or loss of tumor suppressor genes, which may lead to the initiation, progression or both of carcinogenesis [10].
The above-described conditions are causes of metabolic alterations characteristic of T2DM. Also, oxidative stress present in people with T2DM is associated with the chronic hyperglycemia that characterizes this disease. Meanwhile, an excess of circulating glucose activates several metabolic pathways not very common in the organism, which leads to the generation of other metabolites, among which are oxygen FR [1, 4].
Regarding the sorbitol pathway, given the high circulating glucose levels in the blood, the metabolic pathway of the aldose reductase enzyme is followed: it is of low affinity to normal glucose concentrations, generates sorbitol from glucose and uses NADPH (nicotinamide adenine dinucleotide phosphate) as a cofactor. Because the antioxidant potential of glutathione depends on the NADPH supply (because glutathione requires it for regeneration), the flow of this cofactor by another route, such as that of sorbitol pathway, shifts the oxidant-antioxidant balance toward oxidative stress [11].
In turn, it has been shown that sorbitol affects the physiology of cells that do not use insulin-mediated transporters to take glucose (and which contain the enzyme aldose reductase), such as neurons, red blood cells, and the nephrons that undergo osmotic changes. In addition, the permeability of these cells may be altered due to the increase of sorbitol, leading to complications typical of T2DM [11].
Likewise, sorbitol has been linked to oxidative stress with low insulin levels in diabetic patients, since it has been shown that the beta cells of the pancreas are not immune to damage by FR. In this way, in patients who already have the disease, it is possible that symptoms worsen, since insulin secretion in the pancreas decreases because of interference of FR to the normal process of insulin production and secretion [1].
In addition to the increase in free radicals, there is also an increase in metabolic stress, which is the result of change in energy metabolism, in the level of mediators of inflammation and in the state of the antioxidant defense system. Therefore, the inflammatory process is also altered in patients with T2DM. Systemic inflammation is one of the most representative features of this type of diabetes, characterized by high systemic levels of pro-inflammatory cytokines damaging DNA and causing endothelial dysfunction, which causes microvascular and macrovascular complications in T2DM [1].
An antioxidant is a chemical entity that, at low concentrations and compared to the oxidant, retards or prevents the oxidation of a substrate, which includes lipids, proteins, carbohydrates, and DNA [12].
Antioxidants have been classified in different ways, of which the most used establish differences in chemical structure and biological function, dividing them into enzymatic and nonenzymatic [13].
Exogenous: These come from the diet and include vitamin E, vitamin C, and carotenoids (beta carotenes, lycopenes, and xanthines). Vitamin C is the most abundant water-soluble antioxidant in the blood, whereas vitamin E is the major lipophilic antioxidant. Selenium, the most toxic mineral included in our diet, acts together with vitamin E as an antioxidant [13].
Endogens: Antioxidant defenses consist of avoiding the univalent reduction of oxygen by enzymatic or nonenzymatic systems. A group of enzymes specialized in inactivating the reactive oxygen species (ROS) by different mechanisms has been described, such as catalase (CAT), glutathione peroxidase (GPX), and superoxide dismutase (SOD). Nonenzymatic antioxidants recognize amino acids, such as glycine, taurine, and the tripeptide glutathione [13].
In T2DM, a series of changes occur that indirectly indicate the existence of marked oxidative stress, due to the increase in formation of oxygen free radicals and the decrease of the plasma and intracellular levels of the antioxidants [4].
Carmeli et al. [14] confirmed that in people with T2DM, there is significantly decreased activity of the SOD enzyme as a consequence of high levels of hydrogen peroxide produced during the reaction, which inhibit the enzyme by negative feedback. Indeed, it was observed that an increase of SOD initially occurs in response to the high generation of the superoxide anion in the cell and its elimination by the enzyme. However, the intense production of this radical for a prolonged time exhausts the stimulation of enzymatic activity, since the product of the reaction can inhibit it.
With respect to the concentration of minerals (Cu2+ and Zn2+), Devi et al. [15] found that patients with T2DM had significantly higher serum and erythrocyte copper levels. In addition, plasma copper levels have been reported to be higher in patients with complications. In this sense, it has been hypothesized that alterations in copper metabolism contribute to the progression of pathologies related to diabetes, because glycosylated proteins have a higher affinity for transition metals such as copper.
Nsonwu et al. [16] found that serum zinc levels were significantly lower in people with T2DM. This apparent hypozincemia may be the result of urinary loss, decreased intestinal absorption of this mineral or both conditions.
Inflammation is a response of the body to exposure to infectious agents, antigenic stimuli or physical injury involving the nervous, vascular, and immune systems. Initially, it has a homeostatic function of protection or defense that is characterized by flushing, pain, swelling, edema, and lack of function in the affected area; however, if the process is inefficient and chronic, it becomes a pathophysiological process that favors the increase in FR and consequently oxidative stress [17].
In T2DM, there is a pathophysiological relationship with the chronic inflammatory process (CIP) by two mechanisms: one linked to obesity and the endocrine activity of adipose tissue, and the other involving the development of the immune response stimulated by generated AGEs because of the nonenzymatic glycosylation reaction of proteins [11].
The chronic inflammatory process is an alteration linked to obesity and T2DM, considering that adipose tissue, besides being an energy reserve, acts as a high activity endocrine gland, producing a wide variety of substances with effects at different levels in the body, including proinflammatory cytokines. In addition to secreting hormones, such as leptin, adiponectin, resistin and ghrelin, adipocytes synthesize and secrete cytokines associated with inflammation, such as IL-6 and TNF-α [18].
The mechanism by which the chronic process is linked to the development of diabetes mellitus occurs at the molecular level and implies insulin resistance. Briefly, the mechanism is as follows: when insulin binds to the extracellular alpha subunit of its receptor, it causes a conformational change that allows the binding of ATP to the intracellular beta subunit of the receptor. This promotes autophosphorylation of insulin and confers tyrosine kinase activity, which initiates tyrosine phosphorylation of intracellular proteins called insulin receptor substrate (IRS). IRS have a conserved region that, once activated, allows them to interact with other intracellular proteins, promoting the translocation of the glucose transporter (GLUT) to the cell membrane, with the subsequent entry of glucose [1, 19].
TNF-α causes an inhibition of the autophosphorylation of tyrosine residues of the insulin receptor and also causes the phosphorylation of a serine of the insulin receptor substrate (IRS). This in turn promotes the phosphorylation of a serine of the insulin receptor and inhibits the phosphorylation of tyrosine that is required to promote the cascade of signals for the capture of glucose; thus, this translates into insulin resistance. Also, it has been reported that IL-6 inhibits the signal of insulin transduction in the hepatocyte, which also causes insulin resistance [19].
Vozarova et al. [20] showed that markers of inflammation correlate with diabetes. The total leukocyte count is an indirect marker of inflammation and, specifically a higher neutrophil count than normal, is related to the insulin resistance characteristic of T2DM and cardiovascular diseases.
Inflammation of beta cells of the pancreas as a result of an autoimmune phenomenon has been recognized in type 1 diabetes mellitus and is increased in the pathogenesis of T2DM. Such inflammation is one of the pathways of the pathogenesis of T2DM and its complications [21, 22].
The main cell involved in the inflammatory process and in the insulin resistance of T2DM is the adipocyte, since insulin regulates glucose uptake and storage of triglycerides through these. Adipokines in turn also affect secretion and insulin resistance [23].
In particular, leptin, adiponectin, and resistin contribute to the dysfunction of the beta cells of the pancreas increasing insulin resistance. The adipose tissue also secretes dipeptidyl peptidase-4 (DPP-4) improving the degradation of glucagon in peptide 1 (GLP-1), which has an insulinotropic effect on beta cells [24].
On the other hand, the circulation of proinflammatory cytokines directly and indirectly affects the function of beta cells, increasing inflammation of the adipocyte. Cytokines such as TNF-α, beta-interleukin (IL-1β), and gamma interferon (IFN-γ) alter the regulation of intracellular calcium in beta cells and thus release insulin. In addition, TNF-α increases the expression of amyloid peptide (IAPP) in beta cells leading to accelerated death, which leads to insulin resistance [24].
Glucotoxicity, particularly lipotoxicity, increases fatty acids locally in the islets, and long chain fatty acids, especially palmitic acids, cause oxidative stress and the activation of N-terminal c-Jun kinases. These increase the secretion of adipokines, initiating a cycle that induces the dysfunction of the beta cells of the pancreas, which consequently increases inflammation [25].
The World Health Organization (WHO) estimates that 50% of patients with T2DM do not comply with experts’ recommendations regarding lifestyle and eating habits. In response to this problem, the science of nutrition faces a challenge: the search for new foods and/or food components that ensure health and reduce the risk of certain diseases. In addition, it could reduce future costs derived from the treatment of these diseases. At this point, the food industry plays a significant role, since it is the main producer and distributor of food [26, 27].
The concept of “functional food” was born as a convenient and economical solution for chronic health problems, being influential in many branches of science. Since 1984, the meaning of “functional food” has changed according to country and culture and has been defined and redefined over the past 30 years. A food may be considered “functional” if it has been satisfactorily demonstrated that, in addition to its nutritional effects, it beneficially affects one or more functions of the organism in a way that improves the state of health or well-being or reduces the risk of disease [27].
Therefore, in functional foods, two very important and different points are integrated. On the one hand, there is the science of nutrition, responsible for investigating and testing new compounds and/or foods that are being developed, and also, there is the industry, responsible for production and distribution of food that will eventually reach consumers [28].
In 1984, the Japanese government allocated funds for the study of functional foods or specific foods with therapeutic uses. Japan was the first country to use the definition of functional food as “fortified foods with special components that have beneficial physiological effects.” To be considered as such, there was a legal category of food called FOSHU. In order of importance, the food had to meet three nutritional requirements:
It should be constituted by natural ingredients.
It should be consumed as part of a daily diet.
It should be a food that when consumed presents a particular function in the human body, such as:
Subsequently, the term was adopted by Europe. In the United States, in 1994, the National Academy of Food Sciences and the Nutrition Board defined functional foods as “modified foods or ingredients that can improve health, beyond the nutrients they possess.” In 2004, the American Dietetic Association (ADA) issued an institutional document on functional foods, where they were defined as foods that have potential beneficial effects on health when consumed as part of a varied diet, at effective levels. The definition covers whole, fortified, enriched, or improved (designed) foods [30].
In 2012, FFC (Functional Food Center) announced the new concept of functional food as: “natural or processed foods containing essential or nonessential biologically active compounds, which in specific amounts provide a clinically proven and documented health benefit for the prevention, management, or treatment of a chronic disease.” This means that a functional food can be:
Natural food.
Food to which a component has been added.
Foods to which a component has been removed.
Foods to which the nature of one or more components has been changed.
Food in which the bioavailability of one or more of its components has been modified.
Any combination of the above possibilities [31].
At present, these foods are being greatly developed with emphasis on the following functions [31]:
Regulation of basic metabolic processes: Foods that improve metabolic efficiency are sought. Metabolic efficiency includes glycemia optimization and foods that improve this would produce moderate glucose peaks. This involves developing new ingredients such as hydrogenated carbohydrates or trehalose.
Defense against oxidative aggressions: The paradoxical relationship (i.e., respiration) is known, and certain toxic or harmful reactions occur, such as those occurring in the presence of reactive oxygen species (ROS) that act as powerful antioxidants. These possibly contribute to the appearance of aging processes, heart disease, cancer, cataracts and degenerative pathologies of the nervous system, such as those that occur in Parkinson’s and Alzheimer’s. The organic processes that defend against ROS can be complemented by several substances widespread in numerous foods, such as vitamin E, C, and carotenoids, as well as polyphenols of plant origin, which could reinforce the panoply of functional foods against oxidative aggression.
Circulatory system: Functional foods may play a role in the different predisposing factors of cardiovascular diseases: arterial hypertension, vessel integrity, dyslipidemias, oxidized lipoproteins, elevated levels of homocysteine, increased blood coagulation, and low circulating vitamin K concentrations. Thus, blood lipids can be modified by the presence of certain fatty acids, fiber, and antioxidants, such as flavonoids in the diet. Vegetable components, such as phytosterols, may be able to lower LDL-cholesterol (LDL-C). The overall vascular integrity could also benefit from an increased concentration of folates, vitamin B6 and B12 in the diet, which will reduce plasma concentrations of homocysteine.
Digestive system: The balance and variety of the microbial flora in the intestine are important factors in the maintenance of health. Prebiotics, probiotics, and symbiotics are considered as functional foods in this balance of the predominant flora in the intestine.
Currently, several foods with potential roles in the treatment of T2DM are associated. Mainly, the roles of nopal, soy, and oats are recognized because of their hypoglycemic, insulinomimetic and lipid-lowering effects and of bioactive compounds such as antioxidants and omega-3 fatty acids. Oxidative stress and chronic inflammation are present in fresh fruits and vegetables, teas, and blue fish, respectively. The latter, in clinical studies, are treated as compounds characterized as nutraceuticals, given the low bioavailability they possess as part of a food matrix.
The nopal belongs to the family of cactuses, which are fleshy, thickened, and spiny plants, and to the genus Opuntia, which is characterized by extended petals with an articulated stem. Opuntia streptacantha is the best studied of this genus and is more cultivated in arid and semi-arid zones of the Mexican territory [32].
Scientific evidence on nopal has shown a correlation between ethnomedical uses and experimental results, since people use this food as an alternative or combined treatment with T2DM drugs [32].
Pharmacological research of the nopal as a hypoglycemic agent began in 1964 and was continued in 1979 by the now-extinct Mexican Institute for the Study of Medicinal Plants (IMEPLAM). Researchers at this institute found that different preparations of liquefied raw nopal, administered by a nasogastric tube to rabbits with hyperglycemia induced by pancreatectomy or by administration of aloxane, produced a hypoglycemic effect. Four years later, Ibanez and Meckes (1983) showed that a semipurified fraction of fresh stem juice of O. streptacantha given to normoglycemic rabbits or with induced hyperglycemia produced a significant decrease in blood glucose and triglyceride levels [33].
Trejo-González et al. [34] performed a study in rats with streptozotocin-induced diabetes, who were given a simultaneous administration of O. fuliginosa (1 mg/kg) and insulin for 7 days. This induced decreased blood glucose and glycosylated hemoglobin to normal values. These values were maintained when insulin was withdrawn and only the cactus extract was administered.
Laurenz et al. [35] found that in pigs with chemically induced diabetes, oral administration of 250–500 mg/kg of O. lindheimeri extract maintained blood glucose at normal levels but did not modify the glycemia of nondiabetic pigs.
Frati-Munari et al. [36] administered 100 g of roasted cactus to both healthy and obese subjects with or without T2DM, 20 min before meals three times a day for 10 days, produced a significant decrease in total cholesterol, triglycerides, and total weight in nondiabetic obese subjects and type 2 diabetes obese subjects and in the glycemia of diabetic subjects. These results suggest that the effects observed with nopal are due to their fiber content. The fiber content is a mixture of lignin, cellulose, hemicellulose, pectin, mucilage and gums, which are capable of decreasing the gastrointestinal absorption of various nutrients and, consequently, decreasing blood levels of cholesterol, triglycerides, and glucose due to lack of absorption.
The group of Frati-Munari et al. [37] performed another study in patients with induced hyperglycemia and showed that the same dose as in the previous study of 100 g of roasted cactus, given to healthy volunteers, 20 min before starting the oral glucose tolerance test, prevented blood glucose elevation at 120 and 180 min and decreased blood insulin concentration. To explain this latter effect, a possible inhibitory action of the fiber on the gastric peptide was mentioned. This substance normally increases the sensitivity of the insulin receptor and induces the release of this hormone in the islets of Langerhans. Unfortunately, neither of these hypotheses have been experimentally studied.
In a subsequent study, it was reported that fresh nopal blotch, whose species was not identified, administered orally to healthy individuals, did not modify the basal glucose or blood insulin concentration. In contrast, an antihyperglycemic action was described in healthy individuals with orally, but not intravenously, induced hyperglycemia. These results suggest that liquefied cactus would only have an antihyperglycemic effect if it is ingested prior to food intake; this effect would prevent the complications of T2DM [37].
The same research group also showed that the decrease in blood glucose in individuals with type 2 diabetes is in direct proportion to the administered doses of roasted cactus. This effect which the authors called “acute hypoglycemia” is believed to be independent of that produced by the fiber at the level of the gastrointestinal tract [38].
This group also found that extracts of fresh crude nopal had virtually no “hypoglycemic” effect when given to type 2 diabetic patients under fasting conditions, whereas roasted cactus produced a “hypoglycemic” effect in the same type of patients but not in normoglycemic healthy subjects. These results call into question whether fresh nopal smoothies, which are consumed by much of the Mexican population, have any beneficial effect, especially if consumers are not diabetic [39].
In conclusion, nopal has different effects in the body. However, although it appears that this plant prevents glycemia elevation and has an insulinomimetic effect and lowers blood glucose levels below normal values, these effects only occur under certain conditions, such as the use of large doses (100–500 g) of roasted cactus.
Porrata et al. [40] emphasized the importance of a fiber-rich diet for the control of T2DM. In 6 months, 25 adults with T2DM treated with antihyperglycemic agents and a macrobiotic vegetarian diet with a majority of whole grains, vegetables, legumes, and green tea showed beneficial effects. These were evident in improved blood glucose control, decreased insulin requirements, slowed glucose absorption, increased peripheral tissue sensitivity to insulin, lowered cholesterol levels and triglycerides, controlled body weight and lowered blood pressure.
It was also observed that insulin has been shown to have a marked lipid-lowering effect in individuals with obesity and dyslipidemia. It has been recommended that 9 g/day of insulin for 4 weeks is sufficient to achieve a favorable effect on the lipid profile [40].
Soybean (Glycine max) is a species of the leguminous family (Fabaceae) cultivated for its seeds, which have medium oil and high protein content. Its composition is based on 40% protein and 20% oil. It is considered as the legume with the highest contribution of protein and its consumption produces hypoglycemic and hypolipidemic benefits, among others [41].
Céspedes et al. [42] conducted a study with 40 patients with T2DM to evaluate the effect of soy protein in this pathology. All patients received three servings of soy protein weekly as a nutritional contribution and performed physical exercises. The effect of the soy protein-enriched diet was highly significant for HDL cholesterol, suggesting that it could participate in the control of plasma concentrations of this lipoprotein by helping metabolic control of dyslipidemia, which is known to be a metabolic alteration characteristic of T2DM.
Garrido et al. [43] stated that soy consumption could confer benefits in the prevention of cardiovascular diseases, risk factors of which are T2DM, obesity, and corresponding dyslipidemias. In 2000, the state agency for the US Food and Drug Administration (FDA) allowed the use of a “health claim” for soy protein, associating consumption of this protein with a low saturated fat diet, with a decreased risk of cardiovascular disease. This measure was based on studies included in a meta-analysis of 38 controlled clinical studies using soy protein from the above, and it was concluded that the substitution of animal protein for soy protein significantly decreased total cholesterol, LDL-cholesterol and triglycerides without affecting HDL-cholesterol (HDL-C), and the effects were higher in subjects with higher basal cholesterol.
Each subject received six randomly tested foods: a standard glucose drink or a commercial low-carbohydrate soy drink (Ades Natural Light and Ades Chocolate Light), peanuts, a high-carbohydrate soy milk, or fiber drink. Before each session, the subjects were weighed and interviewed. Only water was allowed to be consumed during fasting, no caffeinated food was allowed. The subjects did not consume legumes and were not allowed to drink alcoholic beverages. The results showed that soy beverages should contain at least 6.25 g of protein per serving and that four servings per day should be consumed for a long time to see a possible beneficial effect on the blood lipid concentration. It is also recommended that soy products have a low concentration of maltodextrins and, if possible, contain soluble fiber to maintain low glycemic indexes and be usable in obese or diabetic patients. The consumption of soy protein (0.5 g/kg/day) in diabetic patients with renal impairment reduces the excretion of urinary albumin and increases HDL cholesterol, as well as improving glomerular filtration [44].
Oat is an annual herbaceous plant, belonging to the grass family. The most cultivated species are Avena sativa and Avena byzantina. It is rich in proteins of high biological value, fats and a large number of vitamins and minerals. It is the cereal with the greatest proportion of vegetable fat; 54% unsaturated fats and 46% linoleic acid. It also contains readily absorbed carbohydrates in addition to calcium, zinc, copper, phosphorus, iron, magnesium, potassium and sodium. In addition, it contains vitamins B1, B2, B3, B6 and E and contains a good amount of fiber, which is less important than nutrients, but contributes to good intestinal functioning [45].
Cabrera Llano and Cárdenas Ferrer [46] stated that in the past 30 years, multiple studies have shown that the administration of dietary fiber could reduce blood glucose levels in patients with both type 1 and type 2 diabetes.
The American Diabetes Association (ADA) continues to recommend a fiber intake between 20 and 35 g/day, both soluble and insoluble, to maintain better glycemic and insulin control, with the soluble fraction being the most effective in glycemic control [47].
The mechanisms proposed are delayed gastric emptying; decrease in glucose uptake by being trapped by fiber viscosity and thus less accessible to the action of pancreatic amylase and short chain fatty acid production; and propionate influences gluconeogenesis by reducing the hepatic production of glucose. Butyrate acts by reducing peripheral resistance to insulin by reducing the production of TNFα. Insulin resistance is one of the most important factors involved in the metabolic syndrome [48].
It is also important to take into account that insulin has, in addition to its metabolic action, an effect on vascular endothelium that facilitates the progression of atherogenesis. Therefore, it is proposed that oat hypoglycemic function is important in patients with T2DM and can be an alternative for the treatment of this. However, the hypolipidemic effect of oats is also noteworthy [48].
Regarding the lipid-lowering effect of oats, Kerckhoffs et al. [49] stated that daily consumption of approximately 3 g of soluble fiber can decrease total cholesterol by 0.13 mmol/L in normocholesterolemic and 0.41 mmol/L in hypercholesterolemic drugs, which would be a mechanism of prevention for one of the metabolic alterations of T2DM.
Ruiz et al. [50] carried out a study whose objective was to determine the effect of Avena sativa on the lipid profile of patients between 20 and 60 years old with diagnoses of dyslipidemias. Patients consumed 60 g of liquefied oats in water daily for 3 months, and total cholesterol, triglycerides, and LDL were measured at the beginning at 4 and 12 weeks. The results showed statistically significant decreases in total and LDL-C, without major changes in HDL-C and triglycerides.
Furthermore, a study performed by Raasmaja et al. [51] evaluated the effect of drink with symbiotic on the reduction of cholesterol, triglycerides, and glucose control by in vivo analysis with a model of 24 rats with genetic obesity exhibiting similar effects to the metabolic syndrome. These rats were randomly divided into three groups: group 1 control (water), group 2 (symbiotic), and group 3 (malted oats). Measurements of glucose, total cholesterol, and triglycerides in blood plasma were taken for 3 months on six occasions. The results showed that rats that consumed symbiotic beverages had decreased glucose, triglycerides, and weight. However, groups 1 and 3 showed a greater reduction of cholesterol in comparison with group 2. Therefore, it was concluded that the consumption of a symbiotic drink based on malted oats and Lactobacillus casei exerted a positive effect on the reduction of glucose and triglycerides in addition to showing a tendency for decreased weight. This type of drink may be a safe alternative for patients with T2DM since, in addition to glucose control, it exerts a lipid-lowering effect and a decrease in body weight.
Dietary antioxidants play an important role in the defense against aging and chronic diseases such as T2DM, as these substances inactivate free radicals involved in oxidative stress and prevent its propagation. As previously described, T2DM is characterized by a chronic oxidative state. Therefore, the inclusion of antioxidants in the diet contributes to counteracting the effects of the oxidative state on the organism [52].
Supplementation of the diet with natural antioxidants may have a beneficial effect in improving the morbidity and mortality of diabetic patients, so that they could prevent and delay the development of chronic complications of T2DM [53].
Yusuf et al. [54] performed a study to evaluate the possible effects of antioxidants in the prevention and treatment of T2DM complications. In most studies, vitamin E was isolated or in combination. The doses of vitamin E used were 300–1800 IU/day, generally in the form of alpha-tocopherol. However, there were no significant data demonstrating a beneficial effect of vitamin E in the prevention of T2DM, but a beneficial role of vitamin E in endothelium-dependent vasodilation was observed in subjects with cardiovascular risk, such as diabetes. This directly associates improvement of function of endothelial activity with the reduction of oxidative stress, supporting that the benefit of vitamin E on endothelial function depends in part on its antioxidant effects.
Geohas et al. [55] evaluated metabolic effects of supplementation of chromium in different doses or chromium combined with biotin in a total of 216 type 2 diabetic patients. The study showed a reduction of glycosylated hemoglobin of up to 2%, postprandial glycemia, fructosamine, insulinemia, total cholesterol, HDL/LDL ratio, triglycerides, and atherogenicity index.
In addition, Lu et al. [56] found certain metabolic benefits for patients with T2DM by supplementing the diet with 3000 mg/day of vitamin C in a clinical trial. The metabolic benefits in the vitamin C group were manifested as a tendency to decrease glycosylated hemoglobin and total cholesterol, although there were no changes in the levels of interleukins, C-reactive protein, or in the oxidation of LDL-cholesterol particles.
Moreover, Porrata et al. [40] showed that the consumption of a large amount of green tea in the diet was related to the metabolic control of T2DM, due to the polyphenols it contains. These substances are considered as the main active ingredients in the protection against oxidative damage and in the anti-inflammatory activities of T2DM. They can also increase the activity of insulin, demonstrating an increase of insulin in vitro of more than 15 times. This potentiating activity is attributed to the epigallocatechin gallate contained in green tea.
This study described the benefits of tea on hypercholesterolemia and hypertriglyceridemia, which are metabolic alterations related to T2DM. This antilipemic effect of tea is due to the action of polyphenols leading to a decrease in the absorption of fats, as well as reduced fat storage in the liver and heart [40].
Likewise, Montano et al. [57] conducted a study of 22 patients (nine with T2DM), giving them 100 mg orally of coenzyme Q10 twice a day for 12 weeks. This resulted in a significant decrease in cholesterol and LDL levels, as well as glycosylated hemoglobin levels.
Long-chain polyunsaturated fatty acids (PUFAs) are dietary components that participate in multiple physiological processes, where they play a structural role in the phospholipids of cell membranes and are substrates for the synthesis of various physiological mediators. Within the PUFAs are two main groups: the omega-3 (ω-3) and omega-6 (ω-6) fatty acids. These are essential fatty acids (EFAs) for humans because the enzymatic machinery necessary to biosynthesize them is absent [58].
The first exponent of omega-3 fatty acids is α-linolenic acid which, via desaturases and elongases, can be transformed into eicosapentaenoic acid (EPA) and subsequently into docosahexaenoic acid (DHA) [59].
Food sources of α-linolenic acid are foods of plant origin, especially oils (soybean, flax, canola, among others) and nuts (almond, walnut, peanut, among others). The nutritional source of PUFAs derived from these is food of animal origin. Arachidonic acid (AA) is found in meats (beef, lamb, and pork). EPA and DHA are found in both marine animals and vegetables, particularly in fish with a high fat content, such as tuna, horse mackerel, and salmon, among others. AA, EPA, and DHA are important structural components of membrane phospholipids and are the substrates for the formation of a series of lipid derivatives called eicosanoids (derived from 20 carbon atoms in the case of AA and EPA) and docosanoids (derived from 22 carbon atoms, in the case of DHA), which exert important actions in cellular metabolism [60, 61].
Clinical and epidemiological evidence from multiple studies allows us to establish that ω-3 PUFAs are ideal therapeutic candidates for the prevention and/or treatment of a number of pathologies, especially those where inflammation plays a major role in its development as T2DM [62, 63].
Dietary supplementation with EPA and DHA can reduce the production of pro-inflammatory cytokines such as interleukin-1, interleukin-6, interleukin-8, and tumor necrosis factor-α (TNF-α), which are released when macrophages and monocytes are activated. Although these cytokines are potent activators of immune function, the excess activity of these substances contributes to pathological inflammation [64, 65].
Petrova et al. [66] obtained the first data that showed the cardioprotective effects of ω-3 PUFAs. This arose from studies performed in Eskimos (Inuits), who, despite having a high fat intake (more than 30% of energetic requirements), presented a very low incidence of cardiovascular diseases, identifying animals of marine origin (mammals and fish rich in these lipids) as the dietary source of these fats. These results were confirmed in studies carried out in populations with similar diets, which showed a low incidence of cardiovascular diseases.
Manerba et al. [67] conducted a study demonstrating that fish oils lowered plasma cholesterol and TG levels through the inhibition of very low-density lipoproteins (VLDL) and TG biosynthesis in the liver and unchanged biosynthesis of high density lipoproteins (HDL). They also indicated that ω-3 PUFAs have a number of potentially beneficial effects on smooth vascular muscles, by reducing intracellular calcium loss and decreasing smooth muscle cell proliferation (through the inhibition of growth) and increased production of nitric oxide. It is known that one of the main metabolic complications of a patient with T2DM is dyslipidemia, and ω-3 is considered as an alternative treatment for T2DM and, because of this, can be used to treat dyslipidemias.
Manerba et al. [67] also stated that the beneficial effects on cardiovascular health attributed to ω-3 PUFAs are the result of the following mechanisms: decreased plasma TG and LDL cholesterol, increased HDL cholesterol, decreased blood pressure, reduced platelet aggregation, and decreased incidence of arrhythmias.
Geleijnse et al. [68] noted that the type and form of fish preparation determine the cardioprotective effects of ω-3 PUFAs. The consumption of fish rich in ω-3 PUFAs (tuna, horse mackerel and salmon, among others) produced a significant decrease in the risk of presenting cardiac ischemia. This effect is observed when the fish is consumed roasted or baked, but not when consumed fried.
Nasiff-Hadad and Meriño [69] performed a review of the beneficial and detrimental effects of omega-3 fatty acids in subjects with T2DM, arterial hypertension and dyslipidemias, and their effects on hemostasis and other organs and systems. It was concluded that the ingestion of blue meat fish two or three times a week should be a dietary recommendation for the whole population and that the consumption of fish oils in moderate doses (up to 3 g/day) is beneficial for subjects with T2DM, hypertension and/or dyslipidemias as an adjuvant treatment. In these cases, this diet would also decrease platelet aggregation and reduce the synthesis of chemical mediators of inflammation. However, high doses of fish oils may be harmful to glycemic control, high blood pressure in susceptible persons and serum levels of LDLs and HDLs.
Table 1 shows a summary of the doses of the main foods or bioactive compounds used for the treatment of T2DM and which have updated evidence for their effects.
Food/bioactive compound | Dose | Effect | Reference |
---|---|---|---|
Nopal | 300 g/day (roasted) | Significant decrease in total cholesterol, triglycerides, body weight, and glycemia | [39] |
Insulin | 9 g/day by 4 weeks | Improvement of the lipid profile | [40] |
Soy protein | 0.5 g/kg/day | Reduction of urinary albumin excretion, increase in HDL cholesterol and improve glomerular filtration | [44] |
Soluble fiber | 3 g/day | Total cholesterol reduction | [49] |
25–30 g/day | Delayed gastric emptying, decreased glucose uptake and short-chain fatty acid production | [47] | |
Liquefied oats with water | 60 g/day | Significant decrease in total cholesterol and LDL | [50] |
Vitamin E | 300–1800 UI/day (α-tocoferol) | Improvement of endothelial function directly with the reduction of oxidative stress | [54] |
Vitamin C | 3000 mg/day | Decreased glycosylated hemoglobin and total cholesterol | [56] |
Q10 coenzyme | 100 mg/day (oral administration) | Significant decrease in the levels of cholesterol, LDL and glycosylated hemoglobin | [57] |
Omega-3 | 3000 mg/day | Decreased platelet aggregation and reduced synthesis of chemical mediators of inflammation | [69] |
Food and bioactive compounds used in the treatment of T2DM.
T2DM is a complex disease with world prevalence, with important oxidative and proinflammatory components, in which lies its chronicity and complication. Nutrition based on the biological effects of food, beyond its nutritional component, is a dietary alternative that has repercussions on the health status and quality of life of patients with T2DM.
A diet based on the use of antioxidants, omega-3, or foods, such as soybean, nopal and oats, contributes to a better status of the metabolic imbalance produced in T2DM, as a product of carbohydrate metabolism, oxidative stress and inflammatory processes, with significant improvement in the biochemical and clinical markers that characterize this disease. In addition, the design of new policies and educational materials for this population should have a new direction, based on the functional potential of food, where studies have shown effective doses to counteract the chronicity and presence of complications.
In recent years, behaviorally oriented activity-based travel demand models (ABMs) have received much attention, and the significance of these models in the analysis of travel demand is well documented in the literature [1, 2]. These models are found to be consistent and realistic in several fundamental aspects. They possess some significant advantages over the simple aggregated trip-based travel demand models [3]. To achieve this, ABMs consider the linkage among activities and travel for an individual as well as different people within the same household and place more attention to the constraints of time and space. In other words, these models are capable of integrating both the activity, time, and spatial dimensions. The comprehensive advantages of activity-based models in comparison to the trip-based models have been discussed in previous papers [4, 5, 6, 7, 8]. Activity-based models are suitable for a wider variety of transportation policies involving individual decisions such as congestion pricing and ridesharing. More especially, enabling the relationship between activity and behavioral pattern of trip making is one of the main reasons for the shift from the aggregate-level in trip based models to disaggregate-level provided by ABMs [9].
\nActivity-based travel demand models (ABMs) can be classified into two main groups: Utility maximization-based econometric models and rule-based computational process models (CPM). Utility maximization-based econometric models apply different econometric structures such as logit, probit, hazard-based, and ordered response models. While the logit models rely on different assumptions about the distribution of the error terms in the utility functions, hazard-based models use the duration of activity based on end-of-duration occurrence to generate activity schedules [10]. Rule-based computational process models apply different sets of condition-action rules and focus on the implementation of daily travel and ordering activities to mimic individuals’ behavior when constructing schedules. In addition to the aforementioned models, other approaches can be employed either in combination with these models or separately to develop activity-based models. Examples include agent-based and time-space prism approaches. While an agent-based approach allows agents to learn, modify, and improve their interactions with other agents as well as their dynamic environment, time-space prisms are utilized to capture spatial and temporal constraints under which individuals construct the patterns of their activities and trips. Figure 1 exhibits critical elements of ABM such as activity generation, activity scheduling, and mobility choices. It also provides a comparison among the notable existing travel demand models regarding their different elements. The development of activity-based travel demand models has been reviewed comprehensively in previous studies [10, 11]. Table 1 provides a summary of the literature on the evolution of these models over time by introducing the notable existing developed models and highlighting their limitations.
\nComponents of activity-based travel demand models.
ABM type + year of proposal | \nExamples | \nModel limitations | \n
---|---|---|
Constraint-based models 1967 | \nPESASP [12] | \nConsider only individual accessibility, rather than household-level accessibility Some system features, like open hours and travel times, are considered fixed [11] | \n
CARLA [13] | \n||
BSP [14] | \n||
MAGIC [15] | \n||
GISICAS [16] | \n||
Utility maximization-based models 1978 | \nPortland METRO [17] | \n\n\n | \n
San Francisco SFCTA [18] | \n||
New York NYMTC [19] | \n||
Columbus MORPC [20] | \n||
Sacramento SACOG [21, 22] | \n||
CEMDAP [23, 24] | \n||
FAMOS [25] | \n||
CT-RAMP [26] | \n||
Computational process models 2000 | \nALBATROSS [27, 28] | \nFocus more on scheduling and sequencing of activities than the underlying rules in decision-making [11] | \n
TASHA [29, 30] | \n||
ADAPTS [31, 32, 33] | \n||
Feathers [34] | \n||
Agent-based modeling 2004 | \nALBATROSS [27, 28] | \n\n
| \n
Feathers [34] | \n||
MATSim [36] | \n||
TRANSIMS [37] | \n||
SimMobilitiy [38] | \n||
POLARIS [39] | \n
ABM evolution over time.
Despite the existence of many models as listed in Table 1, ABM’s abilities in reflecting behavioral realism are still limited [40]. The capability of ABM models in predicting individual travel movements can be evaluated from two perspectives of input (data) and output (applicability). Activity schedules are an essential input into the ABM model. From an input point of view, the necessity of deriving activity schedules from dynamic resources together with their challenges will be reviewed. From the applicability perspective, the application of ABM output in integration with dynamic traffic assignment (DTA) models, transferring to a new geographical context, and why and how it is applied in transport planning management will also be discussed. To this end, the first part of this paper will review the new real-time data resources revealing the pattern and traces of traveler’s mobility at a large scale and over an extended period of time. The big data enables new ABM models to reflect mobility behavior on an unprecedented level of detail while collecting data over a longer period (e.g., more than one typical day) would improve the behavioral realism in trip making [41]. The second part of this paper looks into the applicability of ABM models. This part includes (i) gap investigation in enriching ABMs by integrating time-dependent OD matrices produced by ABMs with dynamic traffic assignment; (ii) investigation of ABMs’ applicability in transferring from one region to another; and (iii) enriching the capability of ABMs by moving beyond the transportation domain to other such as environment and management strategies.
\nThe remainder of the paper is organized as follows. Section 2 introduces new data sources such as mobile phone call data records, transit smart cards, and GPS data where the influence of new data sources on the planning of activities, formation, and analysis of the travel behavior of individuals will be investigated. This section also introduces activity-based travel demand models, which generates activity-travel schedules longer than a typical day. Section 3 describes the existing experiences in transferring utility-based and CPM activity-based travel demand models from one geographical area to another. This section also reviews the integration of ABM models with dynamic traffic assignment and other models such as air quality models. The possibility of using activity-based models in travel demand management strategies with a focus on car-sharing and telecommuting are considered as examples. The last section concludes the paper and identifies remaining challenges in the area of activity-based travel demand modeling.
\nThis section provides an overview of the role of big data in replacing the traditional data sources, and the changes in activity-based travel demand models given these newly available data.
\nIt is more than half a century that transportation planners try to understand how individuals schedule their activities and travel to improve urban mobility and accessibility. The evolution of travel demand modeling from trip-based to activity-based highlighted the need for high-resolution databases including sociodemographic and economic attributes of individuals and travel characteristics. Today, with the rapid advancements in computation, technology, and applications, the intelligent transportation systems (ITS) have revolutionized the analysis of travel behavior by having more accurate data, removing human errors, and making use of the vast amount of available data [42]. Tools such as GPS devices, smartphones, smart card data, and social networking sites all have the potential to track the movements and activities of individuals by recording and retaining the relevant data continuously over time. Most of the traditional travel survey data are rich in detail. However, it can result in biased travel demand models because of incomplete self-reports and inaccurate scheduling patterns. Therefore, in this section, the common tools used in collecting big data are introduced and the progress made in the area of extracting big data sources is discussed.
\nA call detail record (CDR) is a data record produced by a telephone exchange and consists of spatiotemporal information on the recent system usage [40], which can track people’s movements. This CDR data can be processed and applied in activity-based travel demand modelings to better understand human mobility and obtain more accurate origin-destination (OD) tables [43]. The first attempt using CDR data was a study of Caceres et al. [44], who applied mobile phone data to generate OD matrices. Their concept was then formalized by Wang et al. [45] to obtain transient OD matrices by counting trips for each pair of the following calls from two different telephone (cell) towers at the same hour. Afterward, using the shortest path algorithm, OD trips are assigned to the road network. In the area of urban activity recognition, Farrahi et al. [46] applied two probabilistic methods (i.e., Latent Dirichlet Allocation (LDA) and Author Topic Models, ATM) to cluster CDR trajectories according to their temporal aspects to discover the home and work activities. Considering the spatial aspect of CDR data, Phithakkitnukoon et al. [47] applied auxiliary land use data and geographical information database to find possible activities around a certain cell tower. And considering both the temporal-spatial aspect of CDR, Widhalm et al. [48] used an undirected relational Markov network to infer urban activities. They extracted activity patterns for Boston and Vienna by analyzing cell phone data (activity time, duration, and land use). Their results show that trip sequence patterns and activity scheduling observed from datasets were compatible with city surveys as well as the stability of generated activity clusters across time. In a more recent study, [49] an unsupervised generative state-space model is applied to extract user activity patterns from CDR data. Furthermore, it has been shown that the method of CDR sampling is as significant as survey sampling. For example, in one study [50], CDR and survey data is used during a period of six months to investigate the daily mobility for Paris and Chicago. The result shows that 90% of travel patterns observed in both surveys are compatible with phone data. In another similar study [51], a probabilistic induction was proposed using motifs (daily mobility network), time of day activity sequence, and land use classification to produce activity types. CDR data of Singapore was used by Jiang et al. [52] to produce activity-based human mobility patterns.
\nIn the context of activity-based transport modeling, Zilske et al. [53] replaced travel diaries with CDRs as input data for agent-based traffic simulation. They first generated the synthetic CDR data, then the MATSim simulation software was used to identify every observed person as an agent to convert call information into activity. They fused the CDR data set with traffic counts in their next paper [54], to reduce the Spatio-temporal uncertainty.
\nIn summary, the findings reported from different studies indicated the major implications of mobile phone records on the estimation of travel demand variables including travel time, mode and route choice as well as OD demand and traffic flow estimation; however; in practice, the information generated from CDR data are yet to be used widely in simulation models. This is mainly because of the conflict between either level of resolution or format and completeness of model and data [55].
\nSmart card systems with on- and off-boarding information gained much popularity in large public transport systems all over the world, and have become a new source of data to understand and identify the Spatio-temporal travel patterns of the individual passengers. The smart card data are investigated in various studies such as activity identification, scheduling, agent-based transport models, and simulation [56]. Besides, in other studies [57, 58, 59] smart card data was used as an analysis tool in investigating the passenger movements, city structure, and city area functions. Similarly, in the recent study [60], a visual analysis system called PeopleVis was introduced to examine the smart card data (SCD) and predict the travel behavior of each passenger. They used one-week SCD in the city of Beijing and found a group of “familiar strangers” who did not know each other but had lots of similarities in their trip choices. Zhao et al. [61] also investigated the group behavior of metro passengers in Zhechen by applying the data mining procedure. After extracting patterns from smart card transaction data, statistical-based and clustering-based methods were applied to detect the passengers’ travel patterns. The results show that a temporally regular passenger is very probable to be a spatially regular passenger. The disaggregated nature of smart card data represents suitable input to multi-agent simulation frameworks. For example, the smart card data is used to generate activity plans and implement an agent-based microsimulation of public transport in two cities of Amsterdam and Rotterdam [62]. An agent-based transport simulation is developed for Singapore’s public transport using MATSim environment [63]. Unlike Bouman’s study, they considered the interaction of public transport with private vehicles. The study of Fourie et al. [64] was another research work to present the possibility of integrating big data algorithms with agent-based transport models. Zhu [65] compared one-week transaction data of smart cards in Shanghai and Singapore. They found feasibility in generating continuous transit use profiles for different types of cardholders. However, to have a better understanding of the patterns and activity behaviors, in addition to collecting the data from smart cards, one should integrate them with other data set.
\nIn travel demand modeling, it is important to have accurate and complete travel survey data including trip purpose, length, and companions, travel demand, origin and destination, and time of the day. Since the 1990s, the global positioning system (GPS) became popular for civil engineering applications, especially in the field of transportation as it provides a means of tracking some of the above variables. In the literature, methods of processing the GPS data and identifying activities can be classified according to different approaches such as rule-based and Bayesian model [66]; fuzzy logic [67]; multilayer perceptron [68]; and support vector machine learning [69]. Nevertheless, the disadvantages of using GPS data include the cost, sample size limitation, and the need to retrieve and distribute GPS devices to participate. Since smartphones are becoming one of the human accessories while equipped with a GPS module, they can be considered as a replacement of the GPS device to gather travel data. In this regard, CDR from smartphones is used [70] to estimate origin-destination matrices, or a smartphone-based application is used [71] to map the semiformal minibus services in Kampala (Uganda) and to count passenger boarding and alighting [72]. In the Netherlands, the Mobidot application is developed for analyzing the mobility patterns of individuals. To deduce travel directions and modes, this application uses the real-time data gathered by sensors of smartphones including GPS, accelerometer, and gyroscope sensors to compare them with existing databases [73].
\nApplying smartphones as a replacement of GPS however, holds several restrictions including the draining of smartphone battery and it is not possible to record travel mode and purpose.
\nToday transport modelers, planners, and managers have started to benefit from the popularity of social networking data. There are different kinds of social media data such as Twitter, Instagram, and LinkedIn data, which consist of normal text, hash-tag (#), and check-in data. As hash-tag and check-in data are related to an activity, location or event, they can be used as meaningful resources in analysis of destination/origin of the activity [74]. According to the literature, social media has a great influence on different aspects of travel demand modeling [75]. Using social media instead of traditional data collection methods was investigated in different studies [76]. The way of processing these data to extract useful information is challenging as investigated in different studies [77, 78]. Various studies [79, 80, 81, 82] also examined social media data to understand the mobility behavior of a large group of people. Testing the possibility of evaluating the origin-destination matrix based on location-based social data was researched [83] or in another similar studies [84, 85] where Twitter data was used to estimate OD matrices. The comparison between this new OD with the traditional values produced by the 4-step model proved the great potential of using social media data in modeling aggregate travel behavior. Social media data can be used in other areas such as destination choice modeling [86], recognizing activity [87], understanding the patterns of choosing activity [80, 88, 89], and interpreting life-style behaviors via studying activity-location choice patterns [90].
\nMost existing travel demand modelers have applied the household survey data during the period of one day to construct activity schedules. However, longer periods such as one week or one month gained substantial importance during recent years. For simulating everyday travel behavior and generating schedules, a one-week period provides more comprehensive coverage because it includes weekdays and weekends and represents the weekly routines of individuals in making trips. Periods longer than one week can further provide detail on personal behavior as well as various usage of modes in different ways. So far only a few travel demand models covered a typical week as a studied period. For example rhythm in activity-travel behavior based on the capacity of one week was presented by applying a Kuhn-Tucker method [41]. Few works have been concentrating on the generation of multiple-day travel dataset. For example, by using large data and surveys, Medina developed two discrete choice models for generating multi-day travel activity types based on the likeliness of the activity [91]. a sampling method based on activity-travel pattern type clustering [92] was proposed to extract multi-day activity-travel data according to single-day household travel data. The results show similarities in distributions of intrapersonal variability in multi-day and single-day. MATSim is a popular agent-based simulation for ABM research [93, 94], however, it is not appropriate for modeling the multi-day scenarios because MATSim uses the co-evolutionary algorithm to reach the user equilibrium which is a time consuming particularly for multi-day plans. To solve these problems, Ordonez [95] proposed a differentiation between fixed and flexible activities. Based on different time scales, Lee examined three levels of travel behavior dynamics, namely micro-dynamics (24 hours), macro-dynamics (lifelong travel behavior), meso-dynamics (weekly/monthly/yearly basis) by applying different statistical models [96]. A learning day-by-day module in another agent-based simulation software SimMobility is proposed [97]. Furthermore, ADAPTS is one of the few activity-based travel demand models which depends on activity planning horizon data for a longer period than one day, for example, one week or one month [33].
\nAs highlighted by the above literature review, applying one-day observation data in travel demand modeling provides an inadequate basis of understanding of complex travel behavior to predict the impact of travel demand management strategies. So multi-day data are needed to refine this process. Previously, it was not easy to collect multi-day data, however, today thanks to advantages to technology it is possible to extract data from GPS, smartphones, smart cards, etc. with no burden for the respondent. Models built based on GPS data have been found to be more accurate and precise due to having fewer measurement errors. Collecting call detail records from mobile phones provide modelers with large trip samples and origin-destination matrices, while smart card data are more useful in terms of validation.
\nWe now turn to the recent advances and ongoing research in ABM focused on testing and enhancing geographical transferability and capacity to predict a broader range of impacts than flows and performance of the transport network.
\nThe spatial transferability of a travel demand model happens when the information or theory of a developed model of one region is applied to a new context [98]. Transferability can be used not only as a beneficial validation test for the models but also to save the cost and time required to develop a new model. Validation of a model by testing spatial transferability beside other various methods such as base-year and future-year data set is a test of validity which represents the capability of activity-based models in predicting travel behavior in a different context [99]. The exact theoretical basis and behavioral realism of activity-based travel demand model make them more appropriate for geographic transferability in comparison to traditional trip-based models [100]. Testing the transferability of ABM was first investigated by Arentze et al. [101]. They examined the possibility of transferring the ALBATROSS model at both individual and aggregate levels for two municipalities (Voorhout and Apeldoorn) in the Netherlands by simulating activity patterns. The results were satisfactory except for the transportation mode choice. In the United States, the CT-RAMP activity-based model which was developed for the MORPC region then transferred to Lake Tahoe [102]. In another study, one component of the ADAPTS model showed the potential for having good transferability properties [31]. The transferability of the DaySim model system developed for Sacramento to four regions in California and two other regions in Florida was investigated in [103]. The results show that the activity generation and scheduling models can be transferred better than mode and location choice models. The CEMDAP model developed for Dallas Fort Worth (DFW) region was transferred to the southern California region [104]. Outside the U.S., the TASHA model system developed for Toronto was transferred to London [105], and also in another study [106] the transferability of TASHA to the context of the Island of Montreal was assessed. Activity generation, activity location choice, and activity scheduling were three components of TASHA that transferred from Toronto to Montreal. In general, TASHA provided acceptable results at (macro and meso-level) for work and school activities even in some cases better results for Montreal in comparison to Toronto area. The possibility of developing a local area activity-based transport demand model for Berlin by transferring an activity generation model from another geographical area (Los Angeles) and applying the traffic counts of Berlin was investigated [107]. In their research, the CEMDAP model was applied to achieve a set of possible activity-travel plans, and the MATSim simulation was then used to generate a representative travel demand for the new region. The results were quite encouraging, however, the study indicated a need for further evaluation. In one recent study [108], an empirical method was used to check the transferability of ABMs between regions. According to their investigations, the most difficult problems with transferability caused by parameters of travel time, travel cost, land use, and logsum accessibilities. They suggested that in the transferability of the ABM from another region, agencies should be aware of finding a region within the same state or with similar urban density, or preferably both in order to improve the results. The possibility of transferring the FEATHERS model to Ho Chi Minh in Vietnam is investigated [109]. FEATHERS initially is developed for Flanders in Belgium. After calibration of FEATHERs sub-models, testing results using different indicators confirmed the success of transferring the FEATHER’s structure to the new context.
\nAt the theoretical level, a perfect transferable model contributes to the transferability of its underlying behavioral theory, model structure, variable specification and coefficient to the new context. However, perfect transferability is not easy to achieve due to different policy and planning needs as well as the size of the regions, and the availability of data and other resources. Although the results of several transferred ABM model systems seem to have worked reasonably, it is equally important to assess how much accuracy is important in transferring models and how best and where to transfer models from.
\nOne of the advantages of the activity-based travel demand models over trip-based models is its capability to generate various performance indicators such as emission, health-related indicators, social exclusion, well-being, and quality of life indicators. Application of disaggregate models for the area of emission and air quality analysis was introduced by Shiftan [110] who investigated the Portland activity-based model in comparison to trip-based models. In another study [111], the same author integrated the Portland activity-based model with MOBILE5 emission model to study the effects of travel demand techniques on air quality. Regarding the integration of ABM with the emission model, the Albatross ABM model was coupled with MIMOSA (macroscopic emission model) [112] considering the usage of fuel and the amount of produced emission as a function of travel speed. A study in [113] added one dispersion model (AUROTA) to the previous integration of Albatross and MIMOSA to predict the hourly ambient pollutant. Albatross linked with a probabilistic air quality system was employed [114] in air quality assessment study. TASHA was another activity-based model, which has been extensively employed in air quality studies. For example, this model was integrated [29, 115] with MOBILE6.2 to quantify vehicle emissions in Toronto. In their study, EMME/2 was used in the traffic assignment part. The previous research was improved [116] by replacing EMME/2 with MATSim as an agent-based DTA model. This TASHA-MATSim chain was used in the research [117] with the integration of MOBILE6.2C (emission model) and CALPUFF (dispersion model). OpenAMOS linked with MOVES emission model [118], and ADAPTS linked with MOVES [119] together with Sacramento ABM model [120] are among recent studies which represented the application of activity-based models in analyzing the impacts of vehicular emissions.
\nHuman well-being and personal satisfaction play an important role in social progression [121]. To understand the theory behind human happiness, transport policies concentrated on the concept of utility as a tool to increase activity, goods, and services [122, 123]. The issue of well-being as a policy objective is addressed in the literature and measured through various indicators, which show personal satisfaction and growth. For example, in the study by Hensher and Metz [124, 125], saving time which leads to engagement in more activities was introduced as one of the benefits of measuring transport performance. Spatial accessibility was another benefit of travel that provides a range of activities that can be reasonably reached by individuals [126]. A dynamic ordinal logit model was developed [127] based on the collected data on happiness for a single activity in Melbourne. The authors found different activity types, which have different influences on the happiness that each individual experienced. Well-being can be integrated into activity-based models based on random utility theory. In terms of modeling, a framework was introduced [122] considering well-being data to improve activity-based travel demand models. According to their hypothesis, well-being is the final aim of activity patterns. They applied a random utility framework and considered well-being measures as indicators of the utility of activity patterns, and planned to test their framework empirically by adding well-being measurement equations to the DRCOG’s activity-based model.
\nThe above literature review showed the importance of applying traffic models to generate input data to other models such as the air quality model. The accuracy of emission models is highly dependent on the level of detail in transport demand model inputs. Activity-based and agent-based models are supposed to describe reality more accurately by providing more detailed traffic data. Beyond measurement of air quality, well-being and health have drawn increasing attention. The health impact of changes in travel behavior, health inequalities, and social justice can be assessed within the activity-based platform [128]. With the help of geospatial data acquisition technologies like GPS, behavioral information with health data can be integrated into the development of an activity-based model to provide policies that affect the balance of transport and well-being.
\nIn parallel with the travel demand modeling, on the supply side, the conventional supply models used to be static, which import constant origin-destination flows as an input and produce static congestion patterns as an output. Consequently, these models were unable to represent the flow dynamics in a clear and detailed manner. Dynamic traffic assignment (DTA) models have emerged to address this issue and are capable of capturing the variability of traffic conditions throughout the day. It is evident that the shift of analysis from trips to activities in the demand modeling, as well as, the substitution of the static traffic assignment with dynamic traffic assignment in the supply side, can provide more realistic results in the planning process. Furthermore, the combination of ABM and DTA can better represent the interactions between human activity, their scheduling decision, and the underlying congested networks. Nevertheless, according to the study of [11], the integration of ABM with DTA received little attention and still requires further theoretical development. There are different approaches to the integration of ABM and DTA, which started with a sequential integration. In this type of integration, exchanging data between two major model components (ABM and DTA) happens at the end of the full iteration, to generate daily activity patterns for all synthetic population in an area of study, the activity-based model is run for the whole period of a complete day. The outputs of the ABM model which are lists of activities and plans are then fed into the DTA model. The DTA model generates a new set of time-dependent skim matrices as inputs to ABM for the next iteration. This process is continued until the convergence will be reached in the OD matrices output. Model systems applying the sequential integration paradigm can be found in most of the studies in the literature. For example, Castiglione [129] integrated DaySim which is an activity-based travel demand model developed for Sacramento with a disaggregate dynamic network traffic assignment tool TRANSIMS router. Bekhor [130] investigated the possibility of coupling the Tel Aviv activity-based model with MATSim as an agent-based dynamic assignment framework. Hao [116] integrated the TASHA model with MATSim. Ziemke [107] integrated CEMDAP, which is an activity-based model with MATSim to check the possibility of transferring an activity-based model from one geographic region to another. Lin [131] introduced the fixed-point formulation of integrated CEMDAP as an activity-based model with an Interactive System for Transport Algorithms (VISTA). Based on the mathematical algorithm of household activity pattern problem (HAPP), ABM and DTA were integrated [132] by presenting the dynamic activity-travel assignment model (DATA) which is an integrated formulation in the multi-state super network framework.
\nIn the sequential integration, the ABM and DTA models run separately until they reach convergence. At the end of an iteration, these models perform data exchange before iterate again. Therefore, this kind of integrated framework cannot react quickly and positively to network dynamics and is unable to adapt to real-time information available to each traveler. In addressing this limitation, integrated models that adopt a much tighter integration framework have been developed recently. This approach is quite similar to the sequential approach, however; the resolution of time for ABM simulation is one minute rather than 24 hours (complete day). Relating to this level of dynamic integration, Pendyala [133] investigated the possibility of integrating OpenAMOS which is an activity-travel demand model with DTA tool name MALTA (Multiresolution Assignment and loading of traffic activities) with appropriate feedback to the land-use model system. For increasing the level of dynamic integration of ABM and DTA models, dynamic integration having pre-trip enroute information with full activity-travel choice adjustments has been introduced. In this level of ABM & DTA integration, it is assumed that pre-trip information is available for travelers about the condition of the network. It means that travelers are capable of adjusting activity-travel choices since they have access to pre-trip and Enroute travel information. Another tightly integrated modeling framework was proposed in [134] to integrate ABM (openAMOS) and DTA (DTALite) to capture activity-travel demand and traffic dynamics in an on-line environment. This model is capable of providing an estimation of traffic management strategies and real-time traveler information provision. Zockaie et al. [135] presented a simulation framework to integrate the relevant elements of an activity-based model with a dynamic traffic assignment to predict the operational impacts related to congestion pricing policies. Auld et al. [39] developed an agent-based modeling framework (POLARIS) which integrates dynamic simulation of travel demand, network supply, and network operations to solve the difficulty of integrating dynamic traffic assignment, and disaggregate demand models. A summary of the current literature on ABM and DTA integration is presented in Table 2.
\nPaper | \nABM structure | \nDTA Structure | \nMethod of integration | \nInsights | \n
---|---|---|---|---|
[137] | \nKutter Model developed for the city of Berlin | \nMultiagent Simulation (MATSim) | \nSequential | \nDiscuss the disadvantages of the integration of ABM and DTA using OD matrices and link travel times | \n
[138] | \nTASHA model | \nMultiagent Simulation (MATSim) | \nSequential | \nShow the advantages of the microsimulation approach over conventional methodologies relying heavily on temporal or spatial aggregation | \n
[139] | \nCEMDAP | \n(VISTA) | \nSequential | \nShow the impacts of multiple time interval portioning and varying step size on reaching faster and more stable convergence results | \n
[130] | \nTel Aviv activity-based model | \nMulti-agent Simulation (MATSim) | \nSequential | \nShow improved run times, the full activity list can be used directly, without creating origin-destination matrices | \n
[129, 140] | \nDaySim ABM model developed for the Sacramento and Jacksonville | \nDisaggregate dynamic network assignment tool (TRANSIMS) | \nSequential | \nRunning time limitations prevent the models to realistically represent the impacts of network events or disruptions on activity-travel patterns | \n
[141] | \nAgent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) developed for the Chicago region | \nDisaggregate dynamic network assignment tool (TRANSIMS) | \nSequential | \nChoosing smaller time steps in the interaction of ABM and DTA makes integration more accurate | \n
[133] | \nSimulator of travel, route, activity, vehicles, emission and land use (SimTRAVEL) that integrates land-use, activity-based travel demand with DTA models | \nDynamic integration | \nShow the proposed model is capable of simulating the behavioral pattern of human activity in space, time, and networks | \n|
[134] | \nABM (openAMOS) and DTA (DTALite) | \nDynamic integration | \nShow the model is capable of providing an estimation of traffic management strategies and real-time traveler information provision | \n|
[132] | \nFormulation of a dynamic activity-travel assignment (DATA) model in the multi-state supernetwork framework combining ABM and DTA | \nDynamic integration | \nShow the power of the model to capture multi-modal and multi-activity trip chaining at equilibrium states while sensitive to policy interventions | \n|
[142] | \nIntegrated ABM-DTA framework to consider congestion pricing in a large-scale network | \nDynamic integration | \nA user-based approach to evaluate equilibrium conditions | \n|
[39] | \nPOLARIS, which executes a continuous exchange of information between the ABM and DTA components | \nDynamic integration | \nThe resulting gains in computational efficiency and performance allow planning models to include previously separate aspects of the urban system | \n|
[92] | \nAdvanced demand models (InSITE ABM) | \nTime-sensitive traffic network model (DTALite) | \nSequential | \nShow the efficiency of the model over the static assignment-based ABM capturing behavioral changes at a finer time resolution | \n
[143] | \nThe ABM (CT-RAMP) | \nDTA (DynusT) | \nSequential | \nEvaluate different convergence measurements: ABM demand, DTA in terms of a gap of costs | \n
A summary of the empirical literature on ABM and DTA integration.
The above discussion illustrates that most of the model integration platforms between ABM + DTA work based on sequential integration. This loose coupling platform is the most straightforward and popular approach albeit is not responsive to network short-term dynamics and real-time information. Efforts to develop a comprehensive simulation model that can account for all components of dynamic mobility and management strategies continue. Further developments will have to deal with the implementation of an integrated ABM + DTA platform on a large network to support decision-makers, focus on the integration between activity-based demand models and multimodal assignment [136] as well as reducing computational efforts via better data exchange procedure and improving model communication efficiency. Defining practical convergence criteria is another issue which needs further investigations. Fully realistic convergence is normally never happened in sequential integration due to applying a pre-defined number of feedback loops in order to save model runtime.
\nTravel demand management (TDM) strategies are implemented to increase the efficiency of the transportation system and reduce traffic-related emissions. Some examples include mode shift strategies (encouraging people to use public transport) [144], time shift (to ride in off-peak hours, congestion pricing), and travel demand reduction [145] (using shared mobility service or teleworking). Shared transport services including car sharing, bike sharing, and ridesharing have been implemented in most of the transport planning systems across the world. Applying activity-based travel demand models to study the optimal fleet size can be found in different studies in the literature [146, 147]. Parking price policies and their impacts on car sharing were investigated using MATSim in [148]. Results show shared vehicles use more efficient parking spaces in comparison to private vehicles. In the first attempt to model car sharing on more than one typical day [149] the agent-based simulation (mobitopp) was extended with a car-sharing option to study the travel behavior of the population in the city of Stuttgart in one week. In the recent study of [150], car sharing was integrated into an activity-based dynamic user equilibrium model to show the interaction between the demand and supply of car sharing. Among all the TDM strategies, telecommuting can be implemented in a shorter time [151, 152, 153]. The results of these studies present a reduction in vehicle-kilometers-traveled (VKT) during peak hours mainly because telecommuters change their trip timetable during these times. This plan rescheduling is also investigated and addressed in different studies [154] based on the statistical analysis of worker’s decisions about choice and frequency of telecommuting. While the plan rescheduling leads to reducing commute travel, the overall impacts of telecommuting on the formation of worker’s daily activity-travel behavior is challenging. For example, this policy reduced total distance traveled by 75% on telecommuting days while telecommuting could reduce the total commute distance up to 0.8% and 0.7% respectively [151, 155]. Based on the adoption and frequency of telecommuting, a joint discrete choice model of home-based commuting was developed for New York city using the revealed preference (RP) survey [156]. Their results show a powerful relationship among individuals’ attributes, households’ demographics, and work-related factors, and telecommuting adoption and frequency decisions. A similar study [157] estimated the telecommuting choice and frequency by using a binary choice model and ordered-response model respectively. In terms of using activity-based modeling, [158] POLARIS activity-based framework was applied to research telecommuting adoption behavior and apply MOVES emission simulator model to assess the consequences of implementing this policy on air quality. Their results show that considering 50% of workers in Chicago with flexible working time hours in comparison to the base case with 12% flexible time hour workers, telecommuting can reduce Vehicle Mile Traveled (VMT) and Vehicle Hour Traveled (VHT) by 0.69% and 2.09% respectively. This policy reduces greenhouse gas by up to 0.71% as well. Pirdavani et al. [159] investigated the impact of two TDM scenarios (increasing fuel price and considering teleworking) on traffic safety. In this work, FEATHERS model, which is an activity-based model, was applied to produce exposure matrices to have a more reliable assessment. The results show the positive impacts of two scenarios on safety (Figure 2).
\nTravel demand management policies within the activity-based platform.
The above section explores the relationship between transport demand management policies and travel behavior in the ABM context. The use of an activity-based travel demand model provides flexibility to employ a range of policy scenarios, and at the same time, the results are as detailed as possible to obtain the impact of policies on a disaggregated level. The finding highlights the importance of implementing different transportation policies management together to reach the most appropriate effect in terms of improving sustainability and the environment. The discussion emphasizes the need for considering more comprehensive transportation and environmental policies concerning sustainability to tackle travel planning in light of the increasingly diverse and complex travel patterns.
\nThe use of activity-based models to capture complex underlying human’s travel behavior is growing. In this paper, we began by introducing the components of activity-based models and the evolution of the existing developed ABM models. In the first part of this paper, the new resources of data for travel demand analysis were introduced. In the new era of travel demand modeling, we need to deal with a dynamic, large sample, time-series data provided from new devices, and as a result manage observation covering days, weeks, and even months. The outcome of the recent works revealed that since activity-based models originated from the concept of individual travel patterns rather than aggregate flows, they highly suited to these new big data sources. These big datasets, which document human movements, include the information about mobility traces and activities carried out. Based on the in-depth and critical review of the literature, it is clear that while these big datasets provide detailed insight into travel behavior, challenges remain in extracting the right information and appropriately integrating them into the travel demand models. In particular, extracting personal characteristics and trip information like trip purpose and mode of transport are still open problems as these big data resources which provide space-time traces of trip-maker behaviors. Research works along these lines have been started as it was reviewed in the first part; however, further researches should be conducted to handle the uncertainty of big data mobility traces in the modeling process. Also, new methods should be investigated to validate the results for each step of the data analysis and mining. The possibility of fusing data from different available datasets needs further investigation. For instance, to understand the mode inference both data from the smart card and CDRs can be analyzed simultaneously. Another challenging issue regarding the application of this rich new data in transport modeling is that the need for methodologies to extract useful information needed regarding the traveler’s in-home and out-of-home activity patterns, which highlights the combination of data science, soft computing-based approaches, and transport research methods. It requires new Different algorithms such as statistical, genetic, evolutionary, and fuzzy as well as different techniques including advanced text and data mining, natural language processing, and machine learning.
\nThe spatial transferability of activity-based travel demand models remains an important issue. Generally, it is found that the transferability of these models is more feasible than trip-based models, especially between two different regions with similar density or even between two areas in the same state. To date, most of the transferability research in activity-based travel demand modeling is motivated by a desire to save time, and very few studies that applied spatial transferability of activity-based models have undertaken rigorous validation of the results. While literature showed successful model transferability in terms of transferring activity/tour generation, time-of-day choice components, more studies are required on the model transferability regarding mode and location choice models as well as the validation test of activity-based models in different levels, i.e., micro, meso, and macro models.
\nAs part of the second section of this study, this paper reviewed the progress made in the integration of activity-based models with dynamic traffic assignment.
\nBased on the literature, although evolution has occurred in DTA models, the loose coupling (sequential method) between ABM and DATA models still dominate the field. Two main challenges remain, namely poor convergence quality and excessively long run time. Replacing MATSim as a dynamic traffic assignment tool with other route assignment algorithms in recent years was a technical solution to loose coupling, which considered route choice as another facet of a multi-dimensional choice problem. MATSim provides not only an integration between the demand and supply side, but it can also act as a stand-alone agent-based modeling framework. However; MATSim potential drawbacks include being based on unrealistic assumptions of utility maximization and perfect information. To remove these unrealistic rational behavioral assumptions, applying other approaches such as a new innovative method of behavioral user equilibrium (BUE) is needed. This method helps trip-makers to reach certain utility-level rather than maximize the utility of their trip making [160]. Work along this approach has started (e.g., [161]).
\nThe capability of activity-based models in generating other kinds of performance indicators in addition to OD matrices was also reviewed. Literature proved activity-based models generate more detailed results as inputs to air quality models, however; error rises from the accuracy of the information has a relevant impact on the process of integration. So it is necessary to do a comprehensive analysis of the uncertainties in traffic data. Literature proved that despite of the improvements in such disaggregate frameworks and the capability of these models in replicating policy sensitive simulation environment; there is yet to develop the best and perfect traffic-emission-air quality model. While the issue of health has drawn extensive attention from many fields, activity-based travel demand models have proved to have the potential to be used in estimating health-related indicators such as well-being. However, very few studies have been found to investigate the theories required to extend the random utility model based on happiness. While it is proved that mobility and environment have direct impacts on transport-related health [162], investigations on how travel mode preferences and air pollution exposure are related in this context are needed. Another area of research within ABM platform which is yet to be studied is the relationship between individual exposure to air pollution and mobility, especially in space, and time.
\nIn the last part of this paper, the capability of activity-based models in the analysis of traffic demand management was investigated. Generally, the influence of telecommuting on both travel demand and network operation is still incomplete. Very few studies were found in which activity-based framework is used to simulate the potential impacts of telecommuting on traffic congestion and network operation where the real power of activity-based models lie.
\nIn conclusion, while there are still open problems in activity-based travel demand models, there has been a lot of progress being made which is evidenced by the various recent and on-going researches reviewed in this paper. The review showed that by applying different methodologies in the modeling of different aspects of activity-based models, these models are becoming more developed, robust, and practical and become an inevitable tool for transport practitioners, city planners, and policy decision-makers alike.
\nThe research work presented in this paper was supported by the Australian Government-Department of Education under Research Training Program (RTP Stipend) award.
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