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Systematic review of the impacts of electric vehicles on evolving transportation systems

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  • Electric vehicles (EVs) promise significant advancements, including high energy efficiency and the facilitation of grid-stabilizing technologies such as vehicle-to-grid. However, their increased adoption introduces challenges such as elevated congestion, compromised safety, and grid instability. These challenges stem from differences in acceleration and deceleration patterns between EVs and internal combustion engine vehicles (ICEVs), mismatches between charging station demand and grid supply, and potential cyberattacks on the communications of EVs with charging stations and local grids. To address these issues, novel mathematical and machine-learning models have been developed. These models incorporate both simulated and real-world traffic flow data, charging station distribution and utilization data, and in-vehicle energy management and driver assistance data. The outcomes include optimally planned routes for EVs to destinations and charging stations, stabilized power distribution systems during peak hours, enhanced security in EV-station-grid communication, more energy-efficient storage systems, and reduced range anxiety for EV drivers. This paper systematically reviews the emerging impacts of EVs on evolving transportation systems, highlighting the latest developments in these areas and identifying potential directions for future research. By reviewing these specific challenges and solutions, this paper aims to contribute to the development of more efficient and sustainable electrified transportation systems.
  • Atractylodes macrocephala Koidz. (common names 'Baizhu' in Chinese and 'Byakujutsu' in Japanese) is a diploid (2n = 2x = 24) and out-crossing perennial herb in the Compositae family, and has a long history of cultivation in temperate and subtropical areas of East Asia as it is widely used in traditional herbal remedies with multiple pharmacological activities[13]. The 'Pharmacopoeia of the People's Republic of China' states that 'Baizhu' is the dry rhizome of A. macrocephala Koidz. (Atractylodis Macrocephalae Rhizoma, AMR). However, in Japanese traditional medicine 'Baizhu' can be referred to both: A. japonica or A. macrocephala[4].

    A. macrocephala is naturally endemic to China and cultivated in more than 200 towns in China, belonging to Zhejiang, Hunan, Jiangxi, Anhui, Fujian, Sichuan, Hubei, Hebei, Henan, Jiangsu, Guizhou, Shanxi, and Shaanxi Provinces[3]. A. macrocephala grows to a height of 20–60 cm (Fig. 1). The leaves are green, papery, hairless, and generally foliole with 3–5 laminae with cylindric glabrous stems and branches. The flowers grow and aggregate into a capitulum at the apex of the stem. The corollas are purplish-red, and the florets are 1.7 cm long. The achenes, densely covered with white, straight hairs, are obconic and measure 7.5 mm long. The rhizomes used for medicinal purposes are irregular masses or irregularly curving cylinders about 3–13 cm long and 1.5–7 cm in diameter with an outwardly pale greyish yellow to pale yellowish color or a sparse greyish brown color. The periderm-covered rhizomes are externally greyish brown, often with nodose protuberances and coarse wrinkles. The cross-sections are white with fine dots of light yellowish-brown to brown secretion. Rhizomes are collected from plants that are > 2 years old during the spring. The fibrils are removed, dried, and used for medicinal purposes[5, 6].

    Figure 1.  Plant morphology of A. macrocephala.

    The medicinal properties of AMRs are used for spleen deficiency, phlegm drinking, dizziness, palpitation, edema, spontaneous sweating, benefit Qi, and fetal restlessness[7]. The AMR contains various functional components, among which high polysaccharide content, with a yield close to 30%[8]. Therefore, the polysaccharides of A. macrocephala Koidz. rhizome (AMRP) are essential in assessing the quality control and bioactivity of A. macrocephala. Volatile oil accounts for about 1.4% of AMR, with atractylon and atractylodin as the main components[9]. Atractylon can be converted to atractylenolide I (AT-I), atractylenolide II (AT-II), and atractylenolide III (AT-III) under ambient conditions. AT-III can be dehydrated to AT-II under heating conditions[10, 11]. AMRs, including esters, sesqui-, and triterpenes, have a wide range of biological activities, such as improving immune activity, intestinal digestion, neuroprotective activity, immune anti-inflammatory, and anti-tumor.

    In recent years, research on the pharmacological aspects of AMR has continued to increase. Still, the discovery of the main active components in AMR is in its infancy. The PAO-ZHI processing of AMR is a critical step for AMR to exert its functional effects, but also, in this case, further work is required. Studies on the biosynthesis of bioactive compounds and different types of transcriptomes advanced current knowledge of A. macrocephala, but, as mentioned, required more systematic work. Ulteriorly, an outlook on the future research directions of A. macrocephala was provided based on the advanced technologies currently applied in A. macrocephala (Fig. 2).

    Figure 2.  Current progress of A. macrocephala.

    A. macrocephala is distributed among mountainous regions more than 800 m above sea level along the middle and lower reaches of the Yangtze River (China)[5]. Due to over-exploitation and habitat destruction, natural populations are rare, threatened, and extinct in many locations[1,12]. In contrast to its native range, A. macrocephala is widely cultivated throughout China, in a total area of 2,000–2,500 ha, with a yield of 7,000 t of rhizomes annually[13]. A. macrocephala is mainly produced in Zhejiang, Anhui, and Hebei (China)[14]. Since ancient times, Zhejiang has been the famous producing area and was later introduced to Jiangxi, Hunan, Hebei, and other places[15]. Wild A. macrocephala is currently present in at least 14 provinces in China. It is mainly distributed over three mountain ranges, including the Tianmu and Dapan mountains in Zhejiang Province and the Mufu mountains along the border of Hunan and Jiangxi Provinces. A. macrocephala grows in a forest, or grassy areas on mountain or hill slopes and valleys at an altitude of 600–2,800 m. A. macrocephala grows rapidly at a temperature of 22–28 °C, and favors conditions with total precipitation of 300–400 mm evenly distributed among the growing season[16]. Chen et al. first used alternating trilinear decomposition (ATLD) to characterize the three-dimensional fluorescence spectrum of A. macrocephala[17]. Then they combined the three-dimensional fluorescence spectrum with partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor method (kNN) to trace the origin of Atractylodes samples. The results showed that the classification models established by PLS-DA and kNN could effectively distinguish the samples from three major Atractylodes producing areas (Anhui, Hunan, and Zhejiang), and the classification accuracy rate (CCR) of Zhejiang atractylodes was up to 80%, and 90%, respectively[17]. Zhang et al. compared the characteristics, volatile oil content, and chemical components of attested materials from six producing areas of Zhejiang, Anhui, Hubei, Hunan, Hebei, and Henan. Differences in the shape, size, and surface characteristics were reported, with the content of volatile oil ranging from 0.58% to 1.22%, from high to low, Hunan (1.22%) > Zhejiang (1.20%) > Anhui (1.02%) > Hubei (0.94%) > Henan (0.86%) > Hebei (0.58%)[18]. This study showed that the volatile oil content of A. macrocephala in Hunan, Anhui, and Hubei is not much different from that of Zhejiang, which is around 1%. A. macrocephala is a local herb in Zhejiang, with standardized cultivation techniques, with production used to reach 80%–90% of the country. However, in recent years, the rapid development of Zhejiang's real estate economy has reduced the area planted with Zhejiang A. macrocephala, resulting in a sudden decrease in production. Therefore, neighboring regions, such as Anhui and Hunan, vigorously cultivate A. macrocephala, and the yield and quality of A. macrocephala can be comparable to those of Zhejiang. The results were consistent with the data reports[18]. Guo et al. analyzed the differentially expressed genes of Atractylodes transcripts from different regions by the Illumina HiSeq sequencing platform. It was found that 2,333, 1,846, and 1,239 DEGs were screened from Hubei and Hebei, Anhui and Hubei, and Anhui and Hebei Atrexia, respectively, among which 1,424, 1,091, and 731 DEGs were annotated in the GO database. There were 432, 321, and 208 DEGs annotated in the KEGG database. These DEGs were mainly related to metabolic processes and metabolic pathways of secondary metabolites. The highest expression levels of these genes were found in Hubei, indicating higher terpenoid production in Hubei[19]. Other compounds were differentially accumulated in Atractylodes. Chlorogenic acid from Hebei was 0.22%, significantly higher than that from Zhejiang and Anhui[20]. Moreover, the content of neochlorogenic acid and chlorogenic acid decreased after processing, with the highest effect reported in Zhejiang, with the average transfer rate of neochlorogenic acid and chlorogenic acid reaching 55.68% and 55.05%[20]. All these changes would bring great help in distinguishing the origins of A. macrocephala.

    Medicinal AMR can be divided into raw AMR and cooked AMR. The processing method is PAO-ZHI; the most traditional method is wheat bran frying. The literature compared two different treatment methods, crude A. macrocephala (CA) and bran-processed A. macrocephala, and found that the pharmacological effects of AMR changed after frying with wheat bran, mainly in the anti-tumor, antiviral and anti-inflammatory effects[21]. The anti-inflammatory effect was enhanced, while the anti-tumor and antiviral effects were somewhat weakened, which may be related to the composition changes of the compounds after frying. The study of the content of AT-I, II, and III, and atractyloside A, in rat serum provided helpful information on the mechanism of wheat bran processing[22]. In addition to frying wheat bran, Sun et al. used sulfur fumigation to treat AMR[23]. They found that the concentration of different compounds changed, producing up to 15 kinds of terpenoids. Changes in pharmacological effects were related to treatment and the type of illumination[24,25]. Also, artificial light can improve the various biological functions. A. macrocephala grew better under microwave electrodeless light, with a chlorophyll content of 57.07 ± 0.65 soil and plant analyzer develotrnent (SPAD)[24]. The antioxidant activity of AMR extract treated with light-emitting diode (LED)-red light was the highest (95.3 ± 1.1%) compared with other treatments[24]. The total phenol and flavonoid contents of AMR extract treated with LED-green light were the highest at 24.93 ± 0.3 mg gallic acid equivalents (GAE)/g and 11.2 ± 0.3 mg quercetin equivalents (QE)/g compared with other treatments[24, 25]. Polysaccharides from Chrysanthemun indicum L.[26] and Sclerotium rolfsiisacc[27] can improve AMR's biomass and bioactive substances by stimulating plant defense and thus affect their efficacy. In summary, there are compositional differences between A. macrocephala from different origins. Besides, different treatments, including processing mode, light irradiation, and immune induction factors, which can affect AMR's biological activity, provide some reference for the cultivation and processing of A. macrocephala (Fig. 3).

    Figure 3.  Origin, distribution and processing of A. macrocephala.

    The AMR has been reported to be rich in polysaccharides, sesquiterpenoids (atractylenolides), volatile compounds, and polyacetylenes[3]. These compounds have contributed to various biological activities in AMR, including immunomodulatory effects, improving gastrointestinal function, anti-tumor activity, neuroprotective activity, and anti-inflammatory.

    AMRP has received increasing attention as the main active component in AMR because of its rich and diverse biological activities. In the last five years, nine AMRP have been isolated from AMR. RAMP2 had been isolated from AMR, with a molecular weight of 4.354 × 103 Da. It was composed of mannose, galacturonic acid, glucose, galactose, and arabinose, with the main linkages of →3-β-glcp-(1→, →3,6-β-glcp-(1→, →6-β-glcp-(1→, T-β-glcp-(1→, →4-α-galpA-(1→, →4-α-galpA-6-OMe-(1→, →5-α-araf-(1→, →4,6-β-manp-(1→ and →4-β-galp-(1→[28]. Three water-soluble polysaccharides AMAP-1, AMAP-2, and AMAP-3 were isolated with a molecular weight of 13.8 × 104 Da, 16.2 × 104 Da, and 8.5 × 104 Da, respectively. Three polysaccharides were deduced to be natural pectin-type polysaccharides, where the homogalacturonan (HG) region consists of α-(1→4)-linked GalpA residues and the ramified region consists of alternating α-(1→4)-linked GalpA residues and α-(1→2)-linked Rhap residues. Besides, three polysaccharides were composed of different ratios of HG and rhamnogalacturonan type I (RG-I) regions[29]. Furthermore, RAMPtp has been extracted from AMR with a molecular weight of 1.867 × 103 Da. It consists of glucose, mannose, rhamnose, arabinose, and galactose with 60.67%, 14.99%, 10.61%, 8.83%, and 4.90%, connected by 1,3-linked β-D Galp and 1,6-linked β-D Galp residues[30]. Additionally, PAMK was characterized by a molecular weight of 4.1 kDa, consisting of galactose, arabinose, and glucose in a molar ratio of 1:1.5:5, with an alpha structure and containing 96.47% polysaccharide and small amounts of protein, nucleic acid, and uric acid[31]. Another PAMK extracted from AMR had a molecular weight of 2.816 × 103 Da and consisted of glucose and mannose in molar ratios of 0.582 to 0.418[32]. Guo et al. isolated PAMK with a molecular weight of 4.748 × 103 g/mol from AMR, consisting of glucose, galactose, arabinose, fructose, and mannose in proportions of 67.01%, 12.32%, 9.89%, 1.18%, and 0.91%, respectively[33]. In addition, AMP1-1 is a neutral polysaccharide fragment with a molecular weight of 1.433 kDa isolated from AMR. It consists of glucose and fructose, and the structure was identified as inulin-type fructose α-D-Glcp-1→(2-β-D-Fruf-1)7[34]. These reports indicated that, in general, polysaccharides are extracted by water decoction, ultrasonic-assisted extraction, enzyme hydrolysis method, and microwave-assisted extraction. The separation and purification are column chromatography, stepwise ethanol precipitation, and ultrafiltration. Their physicochemical properties and structural characterization are generally achieved by determining the molecular weight, determining the monosaccharide composition, analyzing the secondary structure, and glycosidic bond configuration of polysaccharides with Fourier transform infrared (FT-IR) and nuclear magnetic resonance (NMR). The advanced structures of polysaccharides can be identified by high-performance size exclusion chromatography-multiangle laser light scattering (HPSEC-MALLS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM) techniques (Table 1). AMRP has various physiological functions, including immunomodulatory effects, improving gastrointestinal function, and anti-tumor activity. The related biological activities, animal models, monitoring indicators, and results are summarized in Table 1.

    Table 1.  Components and bioactivity of polysaccharides from Atractylodes macrocephala Koidz. Rhizome.
    Pharmacological activitiesDetailed functionPolysaccharides informationModelDoseTest indexResultsRef.
    Immunomodulatory effectsRestore immune
    function
    /Chicken models
    (HS-induced)
    200 mg/kgOxidative index;
    Activities of mitochondrial complexes and ATPases;
    Ultrastructure in chicken spleens;
    Expression levels of cytokines, Mitochondrial dynamics- and apoptosis-related genes
    Alleviated
    the expression of
    IL-1 ↑,TNF-α ↑, IL-2 ↓, IFN- γ ↓; mitochondrial dynamics- and anti-apoptosis-related genes ↓; pro-apoptosis-related genes ↑;
    the activities of mitochondrial complexes and ATPases ↓ caused by HS
    [35]
    Regulate the immune function/Chicken models
    (HS-induced)
    200 mg/kgiNOS–NO activities;
    ER stress-related genes;
    Apoptosis-related genes;
    Apoptosis levels
    Alleviated NO content ↑; activity of iNOS ↑ in the chicken spleen; GRP78, GRP94, ATF4, ATF6, IRE ↑; caspase3 ↑; Bcl-2 ↓ caused by HS[36]
    Relieve immunosuppressionCommercial AMR powder (purity 70%)Geese models
    (CTX-induced)
    400 mg/kgSpleen development;
    Percentages of leukocytes in peripheral blood
    Alleviated the spleen damage;
    T and B cell proliferation ↓; imbalance of leukocytes; disturbances of humoral; cellular immunity caused by CTX
    [37]
    Active the lymphocytesCommercial AMR powder (purity 95%)Geese models
    (CTX-induced)
    400 mg/kgThymus morphology;
    The level of serum GMC-SF, IL-1b, IL-3, IL-5;
    mRNA expression of CD25, novel_mir2, CTLA4 and CD28 signal pathway
    Maintain normal cell morphology of thymus;
    Alleviated GMC-SF ↓, IL-1b ↓, IL-5↓, IL-6↓, TGF-b↓; IL-4 ↑, IL-10 ↑; novel_mir2 ↓, CD25↓, CD28↓ in thymus and lymphocytes caused by CTX
    [38]
    Alleviate immunosuppressionCommercial AMR powder (purity 70%)Geese models
    (CTX-induced)
    400 mg/kgThymus development;
    T cell proliferation rate;
    The level of CD28, CD96, MHC-II;
    IL-2 levels in serum;
    differentially expressed miRNAs
    Alleviated thymus damage;
    T lymphocyte proliferation rate ↓; T cell activation ↓; IL-2 levels ↓ caused by CTX;
    Promoted novel_mir2 ↑; CTLA4 ↓; TCR-NFAT signaling pathway
    [39]
    Alleviates T cell activation declineCommercial AMR powder (purity 95%)BALB/c female mice (CTX-induced)200 mg/kgSpleen index;
    Morphology, death, cytokine concentration of splenocytes;
    Th1/Th2 ratio, activating factors of lymphocytes;
    T cell activating factors;
    mRNA expression level in CD28 signal pathway
    Improved the spleen index;
    Alleviated abnormal splenocytes morphology and death; Balance Th1/Th2 ratio; IL-2 ↑, IL-6 ↑, TNF-α ↑, IFN-γ ↑; mRNA levels of CD28, PLCγ-1, IP3R, NFAT, AP-1 ↑
    [40]
    Immunoregulation and ImmunopotentiationCommercial AMR powder (purity 80%)BMDCs (LPS-induced);
    Female BALB/c mice (ovalbumin as a model antigen)
    /Surface molecule expression of BMDCs;
    Cytokines secreted by dendritic cell supernatants;
    OVA-specific antibodies in serum;
    Cytokines in serum;
    Lymphocyte immunophenotype
    Expression of CD80 and CD86 ↑; IL-1β ↑, IL-12 ↑, TNF-α↑ and IFN-γ ↑; OVA-specific antibodies in serum ↑; Secretion of cytokines ↑; Proliferation rate of spleen lymphocytes ↑; Activation of CD3+CD4+ and CD3+CD8+ lymphocytes[46]
    Increase immune-response capacity of the spleen in miceCommercial AMR powder (purity 70%)BALB/c female mice100, 200, 400 mg/kgSpleen index;
    Concentrations of cytokines;
    mRNA and protein expression levels in TLR4 signaling
    In the medium-PAMK group:
    IL-2, IL-4, IFN-c, TNF-a ↑; mRNA and protein expression of TLR4, MyD88, TRAF6, TRAF3, NF-κB in the spleen ↑
    [41]
    Immunological activityCommercial AMR powder (purity 80%)Murine splenic lymphocytes (LPS or PHA-induced)13, 26, 52, 104, 208 μg/mLT lymphocyte surface markersLymphocyte proliferation ↑;
    Ratio of CD4+/CD8+ T cells ↑
    [47]
    Immunomodulatory activityTotal carbohydrates content 95.66 %Mouse splenocytes
    (Con A or LPS-induced)
    25, 50, 100 μg/mLSplenocyte proliferation;
    NK cytotoxicity;
    Productions of NO and cytokines;
    Transcription factor activity;
    Signal pathways and receptor
    Promoted splenocyte proliferation; Cells enter S and G2/M phases; Ratios of T/B cells ↑; NK cytotoxicity ↑; Transcriptional activities of NFAT ↑; NF-κB, AP-1 ↑; NO, IgG, IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-6, IL-10, IL-12p40, IL-12p70, IL-13, IFN-γ, TNF-α, G-CSF, GM-CSF, KC, MIP-1α, MIP-1β, RANTES, Eotaxin ↑[42]
    Promote the proliferation of thymic epithelial cellsContents of fucrhaara, galactose, glucose, fructose,
    and xylitol: 0.98%, 0.40%, 88.67%, 4.47%, and 5.47%
    MTEC1 cells50 μg/mLCell viability and proliferation;
    lncRNAs, miRNAs, and mRNAs expression profiles in MTEC1 cells
    The differential genes were 225 lncRNAs, 29 miRNAs, and 800 mRNAs; Genes enriched in cell cycle, cell division, NF-κB signaling, apoptotic process, and MAPK signaling pathway[44]
    Immunomodulatory activityMW: 4.354 × 103 Da;
    Composed of mannose, galacturonic acid, glucose, galactose and arabinose;
    The main linkages are →3-β-glcp-(1→, →3,6-β-glcp-(1→, →6-β-glcp-(1→, T-β-glcp-(1→,
    →4-α-galpA-(1→, →4-α-galpA-6-OMe-(1→, →5-α-araf-(1→, →4,6-β-manp-(1→ and →4-β-galp-(1→
    CD4+ T cell50, 100, 200 μg/mLMolecular weight;
    Monosaccharide composition;
    Secondary structure;
    Surface topography;
    Effect on Treg cells
    Treg cells percentage ↑; mRNA expressions of Foxp3, IL-10 and IL-2 ↑; STAT5 phosphorylation levels ↑; IL-2/STAT5 pathway[28]
    Immunostimulatory activityMW of AMAP-1, AMAP-2, and AMAP-3 were 13.8×104 Da, 16.2×104 Da and 8.5×104 Da;
    HG region consists of α-(1→4)-linked GalpA residues
    RAW264.7 cells (LPS-induced)80, 40, 200 μg/mLMolecular weight;
    Total carbohydrate;
    Uronic acid contents;
    Secondary structure;
    Monosaccharide composition;
    Immunostimulatory activity
    RG-I-rich AMAP-1 and AMAP-2 improved the release of NO[29]
    Immunomodulatory effectMW: 1.867×103 Da;
    Contents of glucose, mannose, rhamnose,
    arabinose and galactose: 60.67%, 14.99%, 10.61%, 8.83% and 4.90%
    SMLN lymphocytes25
    μg/ml
    Molecular weight;
    Monosaccharide composition;
    Ultrastructure;
    Intracellular Ca2+concentration;
    Target genes;
    Cell cycle distribution
    [Ca2+]i ↑; More cells in S and G2/M phases; IFN-γ ↑, IL-17A ↑; mRNA expressions of IL-4 ↓[30]
    Macrophage activationTotal carbohydrates content 95.66 %RAW264.7 macrophages (LPS-induced)25, 50, 100 μg/mLPinocytic activity;
    Phagocytic uptake;
    Phenotypic characterization;
    Cytokine production;
    Bioinformatics analysis;
    Transcription factor inhibition
    IL-6, IL-10 and TNF-α ↑; CCL2 and CCL5 ↑; Pinocytic and phagocytic activity ↑; CD40, CD80, CD86, MHC-I, MHC-II ↑; NF-κB and Jak-STAT pathway[43]
    Immunomodulatory effectTotal carbohydrates content 95.66 %SMLN lymphocytes25, 50, 100 μg/mLCytokine production;
    CD4+ and CD8+ lymphocytes;
    Target genes;
    Bioinformatics analysis;
    T and B lymphocyte proliferation;
    Receptor binding and blocking
    IFN-γ, IL-1α, IL-21, IFN-α, CCL4, CXCL9, CXCL10 ↑; CD4+ and CD8+subpopulations proportions ↑;
    c-JUN, NFAT4, STAT1, STAT3 ↑;
    67 differentially expressed miRNAs (55 ↑ and
    12 ↓), associated with immune system pathways; Affect T and B lymphocytes
    [45]
    Improving gastrointestinal functionRelieve enteritis and improve intestinal
    flora disorder
    Commercial AMR powder (purity 70%);
    Contents of fucrhaara, galactose, glucose, xylitol, and fructose: 0.98%, 0.40%, 88.67%, 4.47%, and 5.47%
    Goslings (LPS-induced)400 mg/kgSerum CRP, IL-1β, IL-6, and TNF-α levels;
    Positive rate of IgA;
    TLR4, occludin, ZO-1, cytokines, and immunoglobulin mRNA expression;
    Intestinal flora of gosling excrement
    Relieved IL-1β, IL-6, TNF-α levels in serum ↑; the number of IgA-secreting cells ↑; TLR4 ↑; tight junction occludin and ZO-1 ↓; IL-1β mRNA expression in the small intestine ↑; Romboutsia ↓ caused by LPS[48]
    Ameliorate ulcerative colitisMW: 2.391 × 104 Da;
    Composed of mannose, glucuronic acid, glucose and arabinose in a molar ratio of 12.05:6.02:72.29:9.64
    Male C57BL/6J mice (DDS-induced)10, 20, 40 mg/kg bwHistopathological evaluation;
    Inflammatory mediator;
    Composition of gut microbiota;
    Feces and plasma for global metabolites profiling
    Butyricicoccus, Lactobacillus ↑;
    Actinobacteria, Akkermansia, Anaeroplasma, Bifidobacterium, Erysipelatoclostridium, Faecalibaculum, Parasutterella,
    Parvibacter, Tenericutes, Verrucomicrobia ↓;
    Changed 23 metabolites in fecal content; 21 metabolites in plasma content
    [49]
    Attenuate ulcerative colitis/Male SD rats (TNBS-induced);
    Co-culture BMSCs and IEC-6 cells
    540 mg/kg
    (for rats);
    400 μg/mL (for cell)
    Histopathological analysis;
    Cell migration;
    Levels of cytokines
    Potentiated BMSCs’ effect on preventing colitis and homing the injured tissue, regulated cytokines;
    BMSCs and AMP promoted the migration of IEC
    [52]
    Against intestinal mucosal injuryMW: 3.714 × 103 Da;
    Composed of glucose, arabinose, galactose, galacturonic acid, rhamnose
    and mannose with molar ratios of 59.09:23.22:9.32:4.70:2.07:1.59
    Male C57BL/6 mice (DDS-induced)100 mg/kgIntestinal morphology;
    IL-6, TNF-α and IL-1β in serum;
    mRNA expression;
    Intestinal microbiota
    Alleviated body weight ↓; colon length ↓; colonic damage caused by DSS;
    Over-expression of TNF-α, IL-1β, IL-6 ↓; Infiltration of neutrophils in colon ↓; Mucin 2 ↑;
    Tight junction protein Claudin-1 ↑;
    Harmful bacteria content ↓;
    Beneficial bacteria content ↑
    [50]
    Against intestinal injuryTotal carbohydrates 95.66 %IECs (DDS-induced)5, 25, 50 μg/mLCell proliferation and apoptosis;
    Expression levels of intercellular TJ proteins;
    lncRNA screening
    Proliferation and survival of IECs ↑;
    Novel lncRNA ITSN1-OT1 ↑;
    Blocked the nuclear import of phosphorylated STAT2
    [51]
    Anti-tumor activityInduce apoptosis in transplanted H22 cells in miceMW: 4.1× 103 Da;
    Neutral heteropolysaccharide composed of galactose, arabinose, and glucose with α-configuration (molar ratio, 1:1.5:5)
    Female Kunming mice100 and 200 mg/kg (for rats)Secondary structure;
    Molecular weight;
    Molecular weight;
    Thymus index and Spleen index;
    Lymphocyte Subpopulation in peripheral blood;
    Cell cycle distribution
    In tumor-bearing mice CD3+, CD4+, CD8+ ↓;
    B cells ↑
    [31]
    Regulate the innate immunity of colorectal cancer cellsCommercial AMR powder (purity 70%)C57BL/6J mice (MC38 cells xenograft model)500 mg/kgExpression of pro-inflammatory cytokines and secretionIL-6, IFN-λ, TNF-α, NO ↑ through MyD88/TLR4-dependent signaling pathway;
    Survival duration of mice with tumors ↑;
    Prevent tumorigenesis in mice
    [54]
    Induce apoptosis of Eca-109 cellsMW: 2.1× 103 Da;
    Neutral hetero polysaccharide composed
    of arabinose and glucose (molar ratio, 1:4.57) with pyranose rings and α-type and β-type glycosidic linkages
    Eca-109 cells0.25, 0.5, 1, 1.5, 2.00 mg/mLCell morphology;
    Cell cycle arrest;
    Induction of apoptosis
    Accelerate the apoptosis of Eca109 cells[53]
    '/' denotes no useful information found in the study.
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    To study the immunomodulatory activity of AMRP, the biological models generally adopted are chicken, goose, mouse, and human cell lines. Experiments based on the chicken model have generally applied 200 mg/kg doses. It was reported that AMRP protected the chicken spleen against heat stress (HS) by alleviating the chicken spleen immune dysfunction caused by HS, reducing oxidative stress, enhancing mitochondrial function, and inhibiting cell apoptosis[35]. Selenium and AMRP could improve the abnormal oxidation and apoptosis levels and endoplasmic reticulum damage caused by HS, and could act synergistically in the chicken spleen to regulate biomarker levels[36]. It indicated that AMRP and the combination of selenium and AMRP could be applied as chicken feed supplementation to alleviate the damage of HS and improve chicken immunity.

    The general application dose in the goose model is also 200 mg/kg, and the main injury inducer is cyclophosphamide (CTX). AMRP alleviated CTX-induced immune damage in geese and provided stable humoral immune protection[37]. Little is known about the role of AMRP in enhancing immunity in geese through the miRNA pathway. It was reported that AMRP alleviated CTX-induced decrease in T lymphocyte activation levels through the novel _mir2/CTLA4/CD28/AP-1 signaling pathway[38]. It was also reported that AMRP might be achieved by upregulating the TCR-NFAT pathway through novel_mir2 targeting of CTLA4, thereby attenuating the immune damage induced by CTX[39]. This indicated that AMRP could also be used as goose feed supplementation to improve the goose's autoimmunity.

    The typical injury inducer for mouse models is CTX, and the effects on mouse spleen tissue are mainly observed. BALB/c female mice were CTX-induced damage. However, AMRP increased cytokine levels and attenuated the CTX-induced decrease in lymphocyte activation levels through the CD28/IP3R/PLCγ-1/AP-1/NFAT signaling pathway[40]. It has also been shown that AMRP may enhance the immune response in the mouse spleen through the TLR4-MyD88-NF-κB signaling pathway[41].

    Various cellular models have been used to study the immune activity of AMRP, and most of these studies have explored the immune activity with mouse splenocytes and lymphocytes. Besides, the commonly used damage-inducing agents are LPS, phytohemagglutinin (PHA), and concanavalin A (Con A).

    In one study, the immunoreactivity of AMRP was studied in cultured mouse splenocytes. LPS and Con A served as controls. Specific inhibitors against mitogen-activated protein kinases (MAPKs) and NF-κB significantly inhibited AMRP-induced IL-6 production. The results suggested that AMRP-induced splenocyte activation may be achieved through TLR4-independent MAPKs and NF-κB signaling pathways[42]. Besides, AMRP isolated from AMR acting on LPS-induced RAW264.7 macrophages revealed that NF-κB and Jak-STAT signaling pathways play a crucial role in regulating immune response and immune function[43]. RAMP2 increased the phosphorylation level of STAT5 in Treg cells, indicating that RAMP2 could increase the number of Treg cells through the IL-2/STAT5 signaling pathway[28]. Furthermore, the relationship between structure and immune activity was investigated. Polysaccharides rich in RG-I structure and high molecular weight improved NO release from RAW264.7 cells. Conversly, polysaccharides rich in HG structure and low molecular weight did not have this ability, indicating that the immunoreactivity of the polysaccharide may be related to the side chain of RG-I region[29]. Moreover, the effect of AMRP on the expression profile of lncRNAs, miRNAs, and mRNAs in MTEC1 cells has also been investigated. The differentially expressed genes include lncRNAs, Neat1, and Limd1. The involved signaling pathways include cell cycle, mitosis, apoptotic process, and MAPK[44].

    Xu et al. found that AMRP affects supramammary lymph node (SMLN) lymphocytes prepared from healthy Holstein cows. Sixty-seven differentially expressed miRNAs were identified based on microRNA sequencing and were associated with immune system pathways such as PI3K-Akt, MAPKs, Jak-STAT, and calcium signaling pathways. AMRP exerted immunostimulatory effects on T and B lymphocytes by binding to T cell receptor (TCR) and membrane Ig alone, thereby mobilizing immune regulatory mechanisms within the bovine mammary gland[45].

    AMRP can also be made into nanostructured lipid carriers (NLC). Nanoparticles as drug carriers can improve the action of drugs in vivo. NLC, as a nanoparticle, has the advantages of low toxicity and good targeting[46]. The optimization of the AMRP-NLC preparation process has been reported. The optimum technologic parameters were: the mass ratio of stearic acid to caprylic/capric triglyceride was 2:1. The mass ratio of poloxamer 188 to soy lecithin was 2:1. The sonication time was 12 min. The final encapsulation rate could reach 76.85%[47]. Furthermore, AMRP-NLC interfered with the maturation and differentiation of bone marrow-derived dendritic cells (BMDCs). Besides, AMRP-NLC, as an adjuvant of ovalbumin (OVA), could affect ova-immunized mice with enhanced immune effects[46].

    AMRP also has the effect of alleviating intestinal damage. They are summarized in Table 1. The common damage-inducing agents are lipopolysaccharide (LPS), dextran sulfate sodium (DDS), and trinitrobenzene sulfonic acid (TNBS). A model of LPS-induced enteritis in goslings was constructed to observe the effect of AMRP on alleviating small intestinal damage. Gosling excrement was analyzed by 16S rDNA sequencing to illuminate the impact of AMRP on the intestinal flora. Results indicated that AMRP could maintain the relative stability of cytokine levels and immunoglobulin content and improve intestinal flora disorder[48]. Feng et al. used DDS-induced ulcerative colitis (UC) in mice and explored the alleviating effects of AMRP on UC with 16S rDNA sequencing technology and plasma metabolomics. The results showed that AMRP restored the DDS-induced disruption of intestinal flora composition, regulated the production of metabolites such as short-chain fatty acids and cadaveric amines, and regulated the metabolism of amino acids and bile acids by the host and intestinal flora[49]. A similar study has reported that AMRP has a protective effect on the damage of the intestinal mucosal barrier in mice caused by DSS. It was found that AMRP increased the expression of Mucin 2 and the tight junction protein Claudin-1. In addition, AMRP decreased the proportion of harmful bacteria and increased the potentially beneficial bacteria content in the intestine[50]. The protective effect of AMRP on DSS-induced damage to intestinal epithelial cells (IECs) has also been investigated. The results showed that AMRP promoted the proliferation and survival of IECs.

    In addition, AMRP induced a novel lncRNA ITSN1-OT1, which blocked the nuclear import of phosphorylated STAT2 and inhibited the DSS-induced reduced expression and structural disruption of tight junction proteins[51]. AMRP can also act in combination with cells to protect the intestinal tract. The ulcerative colitis model in Male Sprague-Dawley (SD) rats was established using TNBS, and BMSCs were isolated. IEC-6 and BMSCs were co-cultured and treated by AMRP. The results showed that AMRP enhanced the prevention of TNBS-induced colitis in BMSCs, promoted the migration of IEC, and affected the expression of various cytokines[52]. These reports indicated that the 16S rDNA sequencing technique could become a standard method to examine the improvement of gastrointestinal function by AMRP.

    AMRP has anti-tumor activity and other biological activities. AMRP can induce apoptosis in Hepatoma-22 (H22) and Eca-109 cells and modulate the innate immunity of MC38 cells. For instance, the anti-tumor effects of AMRP were investigated by constructing a tumor-bearing mouse model of H22 tumor cells. AMRP blocked the S-phase of H22 tumor cells and induced an immune response, inhibiting cell proliferation[31]. In addition, AMRP can inhibit cell proliferation through the mitochondrial pathway and by blocking the S-phase of Eca-109 tumor cells[53]. AMRP affects MC38 tumor cells, and the anti-tumor effect of AMRP was investigated with Toll-like receptor 4 (TLR4) KO C57BL/6 mice and the construction of the MC38 tumor cell xenograft model. AMRP significantly inhibited the development of MC38 cells in mice and prolonged the survival of tumor-bearing mice. AMRP activity was diminished in TLR4 KO mice. Combined with the immunoblotting assay results, it was shown that TLR4 regulated the MyD88-dependent signaling pathway, which has a critical effect on the anti-tumor effect of AMRP[54].

    AMR contains a large number of bioactive compounds. Among them, small molecule compounds include esters, sesquiterpenes, and other compounds. These small molecule compounds have significant pharmacological activities, including anti-tumor, neuroprotective, immunomodulatory, and anti-inflammatory. In the last five years, small molecule compounds have been increasingly identified (Fig. 4), with atractylenolides as the main component of AMR extracts[11]. Atractylenolides are a small group of sesquiterpenoids. Atractylenolides include AT-I, AT-II, and AT-III, lactones isolated from AMR.

    Figure 4.  Structure of small molecule compounds with bioactivities from AMR. Atractylenolide I (1); Atractylenolide II (2); Atractylenolide III (3); 3β-acetoxyl atractylenolide I (4); 4R,5R,8S,9S-diepoxylatractylenolide II (5); 8S,9S-epoxyla-tractylenolide II (6); Atractylmacrols A (7); Atractylmacrols B (8); Atractylmacrols C (9); Atractylmacrols D (10); Atractylmacrols E (11); 2-[(2E)-3,7-dimethyl-2,6-octadienyl]-6-methyl-2,5-cyclohexadiene-1,4-dione (12); 8-epiasterolid (13); (3S,4E,6E,12E)-1-acetoxy-tetradeca-4,6,12-triene-8,10-diyne-3,14-diol (14); (4E,6E,12E)-tetradeca-4,6,12-triene-8,10-diyne-13,14-triol (15); 1-acetoxy-tetradeca-6E,12E-diene-8, 10-diyne-3-ol (16); 1,3-diacetoxy-tetradeca-6E, 12E-diene-8,10-diyne (17); Biatractylenolide II (18); Biepiasterolid (19); Biatractylolide (20).

    The anti-tumor activity was mainly manifested by AT-I and AT-II, especially AT-I (Table 2). Anti-tumor activity has been studied primarily in vivo and in vitro. However, there is a lack of research on the anti-tumor activity of atractylenolide in human clinical trials. The concentration of atractylenolide applied on cell lines was < 400 μM, or < 200 mg/kg on tumor-bearing mice.

    Table 2.  Anti-tumor activity of atractylenolides.
    TypesSubstancesModelIndexDoseSignal pathwayResultsRef.
    Human colorectal cancerAT-IIIHCT-116 cell;
    HCT-116 tumor xenografts bearing in nude mice
    Cell viability;
    Cell apoptotic;
    mRNAs and protein
    expressions of Bax, Bcl-2, caspase-9 and caspase-3
    25, 50, 100, 200 μM (for cell);
    50, 100,
    200 mg/kg (for rats)
    Bax/Bcl-2 signaling pathwayPromoting the expression of proapoptotic related gene/proteins; Inhibiting the expression of antiapoptotic related gene/protein; Bax↑; Caspase-3↓; p53↓; Bcl-2↓[55]
    Human gastric carcinomaAT-IIHGC-27 and AGS cell
    Cell viability;
    Morphological changes;
    Flow cytometry;
    Wound healing;
    Cell proliferation, apoptosis, and motility
    50, 100, 200, 400 μMAkt/ERK signaling pathwayCell proliferation, motility↓; Cell apoptosis↑; Bax↑;
    Bcl-2↓; p-Akt↓; p-ERK↓
    [56]
    Mammary
    tumorigenesis
    AT-IIMCF 10A cell;
    Female SD rats (NMU-induced)
    Nrf2 expression and nuclear accumulation;
    Cytoprotective effects;
    Tumor progression;
    mRNA and protein levels of Nrf2;
    Downstream detoxifying enzymes
    20, 50, 100 μM (for cell);
    100 and 200 mg/kg (for rats)
    JNK/ERK-Nrf2-ARE signaling pathway;
    Nrf2-ARE signaling pathway
    Nrf2 expressing↑; Nuclear translocation↑; Downstream detoxifying enzymes↓; 17β-Estradiol↓; Induced malignant transformation[57]
    Human colon adenocarcinomaAT-IHT-29 cellCell viability;
    TUNEL and Annexin V-FITC/PI double stain;
    Detection of initiator and
    executioner caspases level
    10, 20, 40, 80, 100 μMMitochondria-dependent pathwayPro-survival Bcl-2↓; Bax↑; Bak↑; Bad↑; Bim↑; Bid↑; Puma↑[58]
    Sensitize triple-negative
    TNBC cells to paclitaxel
    AT-IMDA-MB-231 cell;
    HS578T cell;
    Balb/c mice (MDA-MB-231 cells-implanted)
    Cell viability
    Transwell migration
    CTGF expression
    25, 50, 100 μM (for cell);
    50 mg/kg (for rats)
    /Expression and secretion of CTGF↓; CAF markers↓; Blocking CTGF expression and fibroblast activation[59]
    Human ovarian cancerAT-IA2780 cellCell cycle;
    Cell apoptosis;
    Cyclin B1 and CDK1 level
    12.5, 25, 50, 100 and 200 μMPI3K/Akt/mTOR
    signaling pathway
    Cyclin B1, CDK1↓; Bax↑;
    Caspase-9↓; Cleaved caspase-3↓; Cytochrome c↑; AIF↑; Bcl-2↓; Phosphorylation level of PI3K, Akt, mTOR↓
    [60]
    Impaired metastatic properties transfer of CSCsAT-ILoVo-CSCs; HT29-CSCsCell migration
    and invasion;
    miR-200c expression;
    Cell apoptosis
    200 μMPI3K/Akt/mTOR signaling pathwaySuppressing miR-200c activity; Disrupting EV uptake by non-CSCs[61]
    Colorectal cancerAT-IHCT116 cell;
    SW480 cell;
    male BALB/c nude mice (HCT116-implanted)
    Cell viability;
    Cell apoptosis;
    Glucose uptake;
    Lactate Production;
    STAT3 expression;
    Immunohistological analysis
    25, 50, 100, 150, 200 μM (for cell);
    50 mg/kg (for rats)
    JAK2/STAT3 signalingCaspase-3↑; PARP-1↓;
    Bax↑; Bcl-2↓; Rate-limiting glycolytic
    enzyme HK2↓; STAT3 phosphorylation↓
    [62]
    Human lung cancerAT-INSCLC cells (A549 and H1299);
    female nude mice (A549-Luc cells- implanted)
    Cell viability;
    Cell cycle;
    Phosphorylation and protein expression of
    ERK1/2, Stat3,
    PDK1, transcription factor SP1;
    mRNA levels of PDK1 gene
    12.5, 25, 50, 100, 150 μM (for cell);
    25 and 75 mg/kg (for rats)
    /ERK1/2↑; Stat3↓; SP1↓;
    PDK1↓
    [63]
    '/' denotes no useful information found in the study.
     | Show Table
    DownLoad: CSV

    AT-III affects human colorectal cancer. AT-II affects human gastric carcinoma and mammary tumorigenesis. AT-I affects human colon adenocarcinoma, human ovarian cancer, metastatic properties transfer of Cancer stem cells (CSCs), colorectal cancer, and human lung cancer, and enhances the sensitivity of triple-negative breast cancer cells to paclitaxel. Current techniques have made it possible to study the effects of atractylenolide on tumors at the signaling pathway level (Table 2). For instance, AT-III significantly inhibited the growth of HCT-116 cells and induced apoptosis by regulating the Bax/Bcl-2 apoptotic signaling pathway. In the HCT116 xenograft mice model, AT-III could inhibit tumor growth and regulate the expression of related proteins or genes. It indicated that AT-III could potentially treat human colorectal cancer[55]. AT-II significantly inhibited the proliferation and motility of HGC-27 and AGS cells and induced apoptosis by regulating the Akt/ERK signaling pathway. It suggested that AT-II can potentially treat gastric cancer[56]. However, in this study, the anti-tumor effects of AT-II in vivo were not examined. AT-II regulated intracellular-related enzyme expression in MCF 10A cells through the JNK/ERK-Nrf2-ARE signaling pathway. AT-II reduced inflammation and oxidative stress in rat mammary tissue through the Nrf2-ARE signaling pathway. AT-II inhibited tumor growth in the N-Nitroso-N-methyl urea (NMU)-induced mammary tumor mice model, indicating that AT-II can potentially prevent breast cancer[57]. AT-I induced apoptosis in HT-29 cells by activating anti-survival Bcl-2 family proteins and participating in a mitochondria-dependent pathway[58]. It indicated that AT-I is a potential drug effective against HT-29 cells. However, the study was only conducted in vitro; additional in vivo experimental data are needed. AT-I can enhance the sensitivity of triple-negative breast cancer (TNBC) cells to paclitaxel. MDA-MB-231 and HS578T cell co-culture systems were constructed, respectively. AT-I was found to impede TNBC cell migration. It also enhanced the sensitivity of TNBC cells to paclitaxel by inhibiting the conversion of fibroblasts into cancer-associated fibroblasts (CAFs) by breast cancer cells. In the MDA-MB-231 xenograft mice model, AT-I was found to enhance the effect of paclitaxel on tumors and inhibit the metastasis of tumors to the lung and liver[59]. AT-I inhibited the growth of A2780 cells through PI3K/Akt/mTOR signaling pathway, promoting apoptosis and blocking the cell cycle at G2/M phase change, suggesting a potential therapeutic agent for ovarian cancer[60]. However, related studies require in vivo validation trials. CSCs are an important factor in tumorigenesis. CSCs isolated from colorectal cancer (CRC) cells can metastasize to non-CSCs via miR-200c encapsulated in extracellular vesicles (EVs).

    In contrast, AT-I could inhibit the activity and transfer of miR-200c. Meanwhile, interfere with the uptake of EVs by non-CSCs. This finding contributes to developing new microRNA-based natural compounds against cancer[61]. AT-I has the function of treating colorectal cancer. HCT116 and SW480 cells were selected for in vitro experiments, and AT-I was found to regulate STAT3 phosphorylation negatively. The HCT116 xenograft mice model was constructed, and AT-I was found to inhibit the growth of HCT116. AT-I induced apoptosis in CRC cells, inhibited glycolysis, and blocked the JAK2/STAT3 signaling pathway, thus exerting anti-tumor activity[62]. The in vitro experiments were performed with A549 and H1299 cell lines. The in vivo experiments were performed to construct the A549-Luc xenograft mice model. The results showed that AT-I inhibited lung cancer cell growth by activating ERK1/2. AT-I inhibited SP1 protein expression and phosphorylation of Stat3, decreasing PDK1 gene expression. The study showed that AT-I could inhibit lung cancer cell growth and targeting PDK1 is a new direction for lung cancer treatment[63]. The research on the anti-tumor of atractylenolide is relatively complete, and there are various signaling pathways related to its anti-tumor activity. Based on the above information, the anti-tumor mechanism of atractylenolide in the past five years was schemed (Fig. 5).

    Figure 5.  Schematic diagram for the anti-tumor mechanism of atractylenolides.

    In recent years, few studies have been conducted on the neuroprotective activity of esters or sesquiterpenoids from AMR. The neuroprotective effects of AT-III have been studied systematically. Biatractylolide has also been considered to have a better neuroprotective effect. New compounds continue to be identified, and their potential neuroprotective effects should be further explored. The related biological activities, animal models, monitoring indicators, and results are summarized in Table 3. Neuroprotective effects include the prevention and treatment of various diseases, such as Parkinson's, Alzheimer's, anti-depressant anxiety, cerebral ischemic injury, neuroinflammation, and hippocampal neuronal damage. In vivo and in vitro will shed light on the potential effect of sesquiterpenoids from AMR and other medicinal plants.

    Table 3.  Neuroprotective effects of esters and sesquiterpenoids.
    ActivitiesSubstancesModelIndexDoseSignal pathwayResultsRef.
    Establish a PD modelAT-II; AT-I;
    Biepiasterolid;
    Isoatractylenolide I;
    AT-III; 3β-acetoxyl atractylenolide I;
    (4E,6E,12E)- tetradeca-4,6,12-triene-8,10-diyne-13,14-triol;
    (3S,4E,6E,12E)-1-acetoxy-tetradeca-4,6,12-triene-8,10-diyne-3,14-diol
    SH-SY5Y cell (MPP+-induced)Cell viability10, 1, 0.1 μM/All compounds have inhibitory activity on MPP+-
    induced SH-SY5Y cell
    [64]
    /4R,5R,8S,9S-diepoxylatractylenolide II;
    8S,9S-epoxyla-tractylenolide II
    BV-2 microglia cells (LPS-induced)Cell viability;
    NO synthase
    inhibitor;
    IL-6 levels
    6.25, 12.5, 25, 50, 100 μMNF-κB signaling
    pathway
    NO inhibition with IC50 values
    of 15.8, and 17.8 μМ, respectively;
    IL-6 ↓
    [65]
    Protecting Alzheimer’s diseaseBiatractylolidePC12 cell (Aβ25-35-induced);
    Healthy male Wistar rats (Aβ25-35-induced)
    Cell viability;
    Morris water maze model;
    TNF-α, IL-6, and IL-1β
    20, 40, 80 μM (for cells);
    0.1, 0.3, 0.9 mg/kg (for rats)
    NF-κB signaling
    pathway
    Reduce apoptosis; Prevent cognitive decline; Reduce the activation of NF-κB signal pathway[66]
    /BiatractylolidePC12 and SH-SY5Y cell (glutamate-induced)Cell viability;
    Cell apoptosis;
    LDA;
    Protein expression
    10, 15, 20 μMPI3K-Akt-GSK3β-Dependent
    Pathways
    GSK3β protein expression ↓;
    p-Akt protein expression ↑
    [67]
    Parkinson's DiseaseAT-IBV-2 cells (LPS-induced);
    Male C57BL6/J mice (MPTP-intoxicated)
    mRNA and protein levels;
    Immunocytochemistry; Immunohistochemistry;
    25, 50, 100 μM (for cells);
    3, 10, 30 mg/kg/mL (for rats)
    /NF-κB ↓; HO-1 ↑; MnSOD ↑; TH-immunoreactive neurons ↑; Microglial activation ↓[68]
    Anti depressant like effectAT-IMale ICR mice (CUMS induced depressive like behaviors)Hippocampal neurotransmitter levels;
    Hippocampal pro inflammatory cytokine levels;
    NLRP3 inflammasome in the hippocampi
    5, 10, 20 mg/kg/Serotonin ↓;
    Norepinephrine ↓; NLRP3 inflammasome ↓; (IL)-1β ↓
    [69]
    Alzheimer's diseaseBiatractylenolide II/AChE inhibitory activities;
    Molecular docking
    //Biatractylenolide II can interact with PAS and CAS of AChE[70]
    Cerebral ischemic injury and
    neuroinflammation
    AT-IIIMale C57BL/6J mice (MCAO- induced);
    Primary microglia (OGDR
    stimulation)
    Brain infarct size;
    Cerebral blood flow;
    Brain edema;
    Neurological deficits;
    Protein expressions of proinflammatory;
    Anti-inflammatory
    cytokines
    0.01, 0.1, 1, 10, 100 μM (for cells);
    0.1–10 mg/kg
    (for rats)
    JAK2/STAT3/Drp1-dependent mitochondrial fissionBrain infarct size ↓;
    Restored CBF;
    ameliorated brain edema; Improved neurological deficits;
    IL-1β ↓; TNF-α ↓; IL-6 ↓;
    Drp1 phosphorylation ↓
    [71]
    Reduces depressive- and anxiogenic-like behaviorsAT-IIIMale SD rats (LPS-induced and CUMS rat model)Forced swimming test;
    Open field test;
    Sucrose preference test;
    Novelty-suppressed feeding test;
    Proinflammatory cytokines levels
    3, 10, 30 mg/kg/30 mg/kg AT-III produced an anxiolytic-like effect; Prevented depressive- and anxiety-like behaviors; Proinflammatory cytokines levels ↓[72]
    Alleviates
    injury in rat
    hippocampal neurons
    AT-IIIMale SD rats (isoflurane-induced)Apoptosis and autophagy in the hippocampal neurons;
    Inflammatory factors;
    Levels of p-PI3K,
    p-Akt, p-mTOR
    1.2, 2.4, 4.8 mg/kgPI3K/Akt/mTOR signaling pathwayTNF-α ↓; IL-1β ↓; IL-6 ↓; p-PI3K ↑; p-Akt ↑; p-mTOR ↑[73]
    ''/' denotes no useful information found in the study.
     | Show Table
    DownLoad: CSV

    Zhang et al. identified eight compounds from AMR, two newly identified, including 3β-acetoxyl atractylenolide I and (3S,4E,6E,12E)-1-acetoxy-tetradecane-4,6,12-triene-8,10-diyne-3,14-diol. 1-Methyl-4-phenylpyridinium (MPP+) could be used to construct a model of Parkinson's disease. A model of MPP+-induced damage in SH-SY5Y cells was constructed. All eight compounds showed inhibitory effects on MPP+-induced damage[64]. Si et al. newly identified eight additional sesquiterpenoids from AMR. A model of LPS-induced BV-2 cell injury was constructed. 4R, 5R, 8S, 9S-diepoxylatractylenolide II and 8S, 9S-epoxylatractylenolide II had significant anti-neuroinflammatory effects. Besides, the anti-inflammatory effect of 4R, 5R, 8S, 9S-diepoxylatractylenolide II might be related to the NF-κB signaling pathway[65]. Biatractylolide has a preventive effect against Alzheimer's disease. In vitro experiments were conducted by constructing an Aβ25-35-induced PC12 cell injury model. In vivo experiments were conducted by constructing an Aβ25-35-induced mice injury model to examine rats' spatial learning and memory abilities. Biatractylolide reduced hippocampal apoptosis, alleviated Aβ25-35-induced neurological injury, and reduced the activation of the NF-κB signaling pathway. Thus, it can potentially treat Aβ-related lesions in the central nervous system[66]. It has also been shown that biatractylolide has neuroprotective effects via the PI3K-Akt-GSK3β-dependent pathway to alleviate glutamate-induced damage in PC12 and SH-SY5Y cells[67]. The attenuating inflammatory effects of AT-I were examined by constructing in vivo and in vitro Parkinson's disease models. Furthermore, AT-I alleviated LPS-induced BV-2 cell injury by reducing the nuclear translocation of NF-κB. AT-I restored 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced behavioral impairment in C57BL6/J mice, protecting dopaminergic neurons[68]. AT-I also has anti-depressant effects. Chronic unpredictable mild stress (CUMS) induced depressive behavior in institute of cancer research (ICR) mice, and AT-I achieved anti-depressant function by inhibiting the activation of NLRP3 inflammatory vesicles, thereby reducing IL-1β content levels[69]. Biatractylenolide II is a newly identified sesquiterpene compound with the potential for treating Alzheimer's disease. The AChE inhibitory activity of biatractylenolide II was measured, and molecular simulations were also performed. It was found to interact with the peripheral anion site and active catalytic site of AChE[70]. AT-III has a broader neuroprotective function. The middle cerebral artery (MCAO) mouse model and oxygen-glucose deprivation-reoxygenation (OGDR) microglia model were constructed. AT-III was found to ameliorate brain edema and neurological deficits in MCAO mice. In addition, AT-III suppressed neuroinflammation and reduced ischemia-related complications through JAK2/ STAT3-dependent mitochondrial fission in microglia[71]. In order to investigate the anti-depressant and anti-anxiolytic effects of AT-III, the LPS-induced depression model and CUMS model were constructed. Combined with the sucrose preference test (SPT), novelty-suppressed feeding test (NSFT), and forced swimming test (FST) to demonstrate that AT-III has anti-depressant and anti-anxiolytic functions by inhibiting hippocampal neuronal inflammation[72]. In addition, AT-III also has the effect of attenuating hippocampal neuronal injury in rats. An isoflurane-induced SD rats injury model was constructed. AT-III alleviated apoptosis, autophagy, and inflammation in hippocampal neurons suggesting that AT-III can play a role in anesthesia-induced neurological injury[73]. However, AT-III attenuates anesthetic-induced neurotoxicity is not known.

    Immunomodulatory and anti-inflammatory activities are studied in vivo and in vitro. The construction of an inflammatory cell model in vitro generally uses RAW 264.7 macrophages. Different cells, such as BV2 microglia, MG6 cells, and IEC-6 cells, can also be used. Active compounds' immune and anti-inflammatory activity is generally examined using LPS-induced cell and mouse models. For enteritis, injury induction is performed using TNBS and DSS. Several studies have shown that AT-III has immunomodulatory and anti-inflammatory activities. Other sesquiterpene compounds also exhibit certain activities. The related biological activities, animal models, monitoring indicators, and results are summarized in Table 4. For example, five new sesquiterpene compounds, atractylmacrols A-E, were isolated from AMR. The anti-inflammatory effect of the compounds was examined with LPS-induced RAW264.7 macrophage damage, and atractylmacrols A-E were found to inhibit NO production[74]. Three compounds, 2-[(2E)-3,7-dimethyl-2,6-octadienyl]-6-methyl-2, 5-cyclohexadiene-1, 4-dione (1); 1-acetoxy-tetradeca-6E,12E-diene-8, 10-diyne-3-ol (2); 1,3-diacetoxy-tetradeca-6E, 12E-diene-8,10-diyne (3) were isolated from AMR. All three compounds could inhibit the transcriptional activity and nuclear translocation of NF-κB. The most active compound was compound 1, which reduced pro-inflammatory cytokines and inhibited MAPK phosphorylation[75]. Twenty-two compounds were identified from AMR. LPS-induced RAW 264.7 macrophages and BV2 cell injury models were constructed, respectively. Among them, three compounds, AT-I, AT-II, and 8-epiasterolid showed significant damage protection in both cell models and inhibited LPS-induced cell injury by inactivating the NF-κB signaling pathway[76]. To construct a TNBS-induced mouse colitis model, AT-III regulated oxidative stress through FPR1 and Nrf2 signaling pathways, alleviated the upregulation of FPR1 and Nrf2 proteins, and reduced the abundance of Lactobacilli in injured mice[77]. AT-III also has anti-inflammatory effects in peripheral organs. A model of LPS-injured MG6 cells was constructed. AT-III alleviated LPS injury by significantly reducing the mRNA expression of TLR4 and inhibiting the p38 MAPK and JNK pathways[78]. It indicated that AT-III has the potential as a therapeutic agent for encephalitis. The neuroprotective and anti-inflammatory effects of AT-III were investigated in a model of LPS-induced BV2 cell injury and a spinal cord injury (SCI) mouse model. AT-III alleviated the injury in SCI rats, promoted the conversion of M1 to M2, and attenuated the activation of microglia/macrophages, probably through NF-κB, JNK MAPK, p38 MAPK, and Akt signaling pathways[79]. AT-III has a protective effect against UC. DSS-induced mouse model and LPS-induced IEC-6 cell injury model were constructed. AT-III alleviated DSS and LPS-induced mitochondrial dysfunction by activating the AMPK/SIRT1/PGC-1α signaling pathway[80].

    Table 4.  Immunomodulatory and anti-inflammatory activities of esters and sesquiterpenoids.
    ActivitiesSubstanceModelIndexDoseSignal pathwayResultRef.
    Against LPS-induced NO productionAtractylmacrols A-ERAW264.7 macrophages (LPS-induced)Isolation;
    Structural identification;
    Inhibition activity of
    NO production
    25 μM/Have effects on LPS-induced NO production[74]
    Anti-inflammatory2-[(2E)-3,7-dimethyl-2,6-octadienyl]-6-methyl-2,5-cyclohexadiene-1,
    4-dione;
    1-acetoxy-tetradeca-6E,12E-diene-8, 10-diyne-3-ol;
    1,3-diacetoxy-tetradeca-6E, 12E-diene-8,
    10-diyne
    RAW 264.7
    macrophages (LPS-induced)
    Level of NO and PGE2;
    Level of iNOS, COX-2;
    Levels of pro-inflammatory cytokines;
    Phosphorylation of MAPK(p38, JNK, and ERK1/2)
    2 and 10 μMNF-κB signaling pathwayIL-1β ↓; IL-6 ↓; TNF-α ↓;
    p38 ↓; JNK ↓; ERK1/2 ↓
    [75]
    Anti-inflammatoryAT-I; AT-II;
    8-epiasterolid
    RAW264.7 macrophages;
    BV2 microglial cells (LPS-
    induced)
    Structure identification;
    NO, PGE2 production;
    Protein expression of iNOS, COX-2, and cytokines
    40 and 80 μMNF-κB signaling pathway.NO ↓; PGE2 ↓; iNOS ↓;
    COX-2 ↓; IL-1β ↓; IL-6 ↓; TNF-α ↓
    [76]
    Intestinal inflammationAT-IIIMale C57BL/6 mice (TNBS-induced)Levels of myeloperoxidase;
    Inflammatory factors;
    Levels of the prooxidant markers, reactive oxygen species, and malondialdehyde;
    Antioxidant-related enzymes;
    Intestinal flora
    5, 10, 20 mg/kgFPR1 and Nrf2 pathwaysDisease activity index score ↓; Myeloperoxidase ↓; Inflammatory factors interleukin-1β ↓; Tumor necrosis factor-α ↓; Antioxidant enzymes catalase ↓; Superoxide dismutase ↓; Glutathione peroxidase ↓; FPR1 and Nrf2 ↑; Lactobacilli ↓[77]
    Anti-inflammatoryAT-IIIMG6 cells (LPS-
    induced)
    mRNA and protein levels of TLR4,
    TNF-α, IL-1β, IL-6, iNOS, COX-2;
    Phosphorylation of p38 MAPK and JNK
    100 μMp38 MAPK and JNK signaling pathwaysTNF-α ↓; IL-1β ↓; IL-6 ↓;
    iNOS ↓; COX-2 ↓
    [78]
    Ameliorates spinal cord injuryAT-IIIBV2 microglial (LPS-
    induced);
    Female SD rats (Infinite Horizon impactor)
    Spinal cord lesion area;
    Myelin integrity;
    Surviving neurons;
    Locomotor function;
    Microglia/macrophages;
    Inflammatory factors
    1, 10, 100 μM (for cell);
    5 mg/kg (for rats)
    NF-κB,
    JNK MAPK, p38 MAPK, and Akt pathways
    Active microglia/macrophages;
    Inflammatory mediators ↓
    [79]
    Ulcerative colitisAT-IIIIEC-6 (LPS-induced);
    C57BL/6J male mice (DSS-induced)
    MDA,GSH content;
    SOD activity;
    Intestinal permeability;
    Mitochondrial membrane potential;
    Complex I and complex IV activity
    40 and 80 μM (for cell);
    5 and 10 mg/kg (for rats)
    AMPK/
    SIRT1/PGC-1α signaling pathway
    Disease activity index ↓;
    p-AMPK ↑; SIRT1 ↑;
    PGC-1α ↑;
    Acetylated PGC-1α ↑
    [80]
    '/' denotes no useful information found in the study.
     | Show Table
    DownLoad: CSV

    The biosynthetic pathways for bioactive compounds of A. macrocephala are shown in Fig. 6. The biosynthetic pathways of all terpenes include the mevalonate (MVA) pathway in the cytosol and the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway in the plastid[81]. The cytosolic MVA pathway is started with the primary metabolite acetyl-CoA and supplies isopentenyl (IPP), and dimethylallyl diphosphate (DMAPP) catalyzed by six enzymatic steps, including acetoacetyl-CoA thiolase (AACT), hydroxymethylglutaryl-CoA synthase (HMGS), hydroxymethylglutaryl-CoA reductase (HMGR), mevalonate kinase (MVK), phosphomevalonate kinase (PMK) and mevalonate 5-phosphate decarboxylase (MVD)[82]. IPP and DMAPP can be reversibly isomerized by isopentenyl diphosphate isomerase (IDI)[83]. In the MEP pathway, D-glyceraldehyde-3-phosphate (GAP) and pyruvate are transformed into IPP and DMAPP over seven enzymatic steps, including 1-deoxy-d-xylulose 5-phosphate synthase (DXS), 1-deoxy-d-xylulose 5-phosphate reductoisomerase (DXR), 2C-methyl-d-erythritol 4-phosphate cytidyltransferase (MECT), 4-(cytidine 5′-diphospho)-2C-methyl-d-erythritol kinase (CMK), 2C-methyl-d-erythritol-2,4-cyclodiphosphate synthase (MECP), 4-hydroxy-3-methylbut-2-enyl diphosphate synthase (HDS) and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR) were involved in the whole process[84]. The common precursor of sesquiterpenes is farnesyl diphosphate (FPP) synthesized from IPP and DMAPP under the catalysis of farnesyl diphosphate synthase (FPPS)[85]. Various sesquiterpene synthases, such as β-farnesene synthase (β-FS), germacrene A synthase (GAS), β-caryophyllene synthase (QHS), convert the universal precursor FPP into more than 300 different sesquiterpene skeletons in different species[8689]. Unfortunately, in A. macrocephala, only the functions of AmFPPS in the sesquiterpenoid biosynthetic pathway have been validated in vitro[90]. Identifying sesquiterpene biosynthesis in A. macrocephala is difficult due to the lack of: isotope-labeled biosynthetic pathways, constructed genetic transformation system, and high-quality genome.

    Figure 6.  Biosynthetic pathways for bioactive compounds of A. macrocephala.

    With the gradual application of transcriptome sequencing technology in the study of some non-model plants, the study of A. macrocephala has entered the stage of advanced genetics and genomics. Yang et al. determined the sesquiterpene content in the volatile oil of AMR by gas chromatography and mass spectrometry (GC-MS) in A. macrocephala. Mixed samples of leaves, stems, rhizomes, and flowers of A. macrocephala were sequenced by Illumina high throughput sequencing technology[91]. Similarly, compounds' relative content in five A. macrocephala tissue was quantitatively detected by ultra-performance liquid chromatography-tandem mass spectrometry. Sesquiterpenoids accumulations in rhizomes and roots were reported[90]. Seventy-three terpenoid skeleton synthetases and 14 transcription factors highly expressed in rhizomes were identified by transcriptome analysis. At the same time, the function of AmFPPS related to the terpenoid synthesis pathway in A. macrocephala was verified in vitro[90]. In addition to the study of the different tissue parts of A. macrocephala, the different origin of A. macrocephala is also worthy of attention. The AMR from different producing areas was sequenced by transcriptome. Seasonal effects in A. macrocephala were also studied. Interestingly, compared with one-year growth AMR, the decrease of terpenes and polyketone metabolites in three-year growth AMR was correlated with the decreased expression of terpene synthesis genes[92]. Infestation of Sclerotium rolfsii sacc (S. rolfsii) is one of the main threats encountered in producing A. macrocephala[93]. To explore the expression changes of A. macrocephala-related genes after chrysanthemum indicum polysaccharide (CIP) induction, especially those related to defense, the samples before and after treatment were sequenced. The expression levels of defense-related genes, such as polyphenol oxidase (PPO) and phenylalanine ammonia-lyase (PAL) genes, were upregulated in A. macrocephala after CIP treatment[94].

    Traditional Chinese Medicine (TCM), specifically herbal medicine, possesses intricate chemical compositions due to both primary and secondary metabolites that exhibit a broad spectrum of properties, such as acidity-base, polarity, molecular mass, and content. The diverse nature of these components poses significant challenges when conducting quality investigations of TCM[95]. Recent advancements in analytical technologies have contributed significantly to the profiling and characterizing of various natural compounds present in TCM and its compound formulae. Novel separation and identification techniques have gained prominence in this regard. The aerial part of A. macrocephala (APA) has been studied for its anti-inflammatory and antioxidant properties. The active constituents have been analyzed using high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS). The results indicated that APA extracts and all sub-fractions contain a rich source of phenolics and flavonoids. The APA extracts and sub-fractions (particularly ACE 10-containing constituents) exhibited significant anti-inflammatory and antioxidant activity[96]. In another study, a four-dimensional separation approach was employed using offline two-dimensional liquid chromatography ion mobility time-of-flight mass spectrometry (2D-LC/IM-TOF-MS) in combination with database-driven computational peak annotation. A total of 251 components were identified or tentatively characterized from A. macrocephala, including 115 sesquiterpenoids, 90 polyacetylenes, 11 flavonoids, nine benzoquinones, 12 coumarins, and 14 other compounds. This methodology significantly improved in identifying minor plant components compared to conventional LC/MS approaches[97]. Activity-guided separation was employed to identify antioxidant response element (ARE)-inducing constituents from the rhizomes of dried A. macrocephala. The combination of centrifugal partition chromatography (CPC) and an ARE luciferase reporter assay performed the separation. The study's results indicate that CPC is a potent tool for bioactivity-guided purification from natural products[98]. In addition, 1H NMR-based metabolic profiling and genetic assessment help identify members of the Atractylodes genus[99]. Moreover, there were many volatile chemical compositions in A. macrocephala. The fatty acyl composition of seeds from A. macrocephala was determined by GC-MS of fatty acid methyl esters and 3-pyridylcarbinol esters[100]. Fifteen compounds were identified in the essential oil extracted from the wild rhizome of Qimen A. macrocephala. The major components identified through gas chromatography-mass spectrometry (GC-MS) analysis were atractylone (39.22%) and β-eudesmol (27.70%). Moreover, gas purge microsolvent extraction (GP-MSE) combined with GC-MS can effectively characterize three species belonging to the Atractylodes family (A. macrocephala, A. japonica, and A. lancea)[101].

    So far, the research on A. macrocephala has focused on pharmacological aspects, with less scientific attention to biogeography, PAO-ZHI processing, biosynthesis pathways for bioactive compounds, and technology application. The different origins lead to specific differences in appearance, volatile oil content, volatile oil composition, and relative percentage content of A. macrocephala. However, A. macrocephala resources lack a systematic monitoring system regarding origin traceability and quality control, and there is no standardized process for origin differentiation. Besides, the PAO-ZHI processing of A. macrocephala is designed to reduce toxicity and increase effectiveness. The active components will have different changes before and after processing. But current research has not been able to decipher the mechanism by which the processing produces its effects. Adaptation of in vivo and in vitro can facilitate understanding the biological activity. The choice of the models and doses is particularly important. The recent studies that identified AMR bioactivities provided new evidence but are somewhat scattered. For example, in different studies, the same biological activity corresponds to different signaling pathways, but the relationship between the signaling pathways has not been determined. Therefore, a more systematic study of the various activities of AMR is one of the directions for future pharmacological activity research of A. macrocephala. In addition, whether there are synergistic effects among the active components in AMR also deserves further study, but they are also more exhaustive. As for the biosynthesis of bioactive compounds in A. macrocephala, the lack of isotopic markers, mature genetic transformation systems, and high-quality genomic prediction of biosynthetic pathways challenge the progress in sesquiterpene characterization. In recent years, the transcriptomes of different types of A. macrocephala have provided a theoretical basis and research foundation for further exploration of functional genes and molecular regulatory mechanisms but still lack systematicity. Ulteriorly, applying new technologies will gradually unlock the mystery of A. macrocephala.

    This work was supported by the Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties (2021C02074), National Young Qihuang Scholars Training Program, National 'Ten-thousand Talents Program' for Leading Talents of Science and Technology Innovation in China, National Natural Science Foundation of China (81522049), Zhejiang Provincial Program for the Cultivation of High level Innovative Health Talents, Zhejiang Provincial Ten Thousands Program for Leading Talents of Science and Technology Innovation (2018R52050), Research Projects of Zhejiang Chinese Medical University (2021JKZDZC06, 2022JKZKTS18). We appreciate the great help/technical support/experimental support from the Public Platform of Pharmaceutical/Medical Research Center, Academy of Chinese Medical Science, Zhejiang Chinese Medical University.

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Ahmed S, Wang S. 2024. Systematic review of the impacts of electric vehicles on evolving transportation systems. Digital Transportation and Safety 3(4): 220−232 doi: 10.48130/dts-0024-0020
    Ahmed S, Wang S. 2024. Systematic review of the impacts of electric vehicles on evolving transportation systems. Digital Transportation and Safety 3(4): 220−232 doi: 10.48130/dts-0024-0020

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Systematic review of the impacts of electric vehicles on evolving transportation systems

Digital Transportation and Safety  3 2024, 3(4): 220−232  |  Cite this article

Abstract: Electric vehicles (EVs) promise significant advancements, including high energy efficiency and the facilitation of grid-stabilizing technologies such as vehicle-to-grid. However, their increased adoption introduces challenges such as elevated congestion, compromised safety, and grid instability. These challenges stem from differences in acceleration and deceleration patterns between EVs and internal combustion engine vehicles (ICEVs), mismatches between charging station demand and grid supply, and potential cyberattacks on the communications of EVs with charging stations and local grids. To address these issues, novel mathematical and machine-learning models have been developed. These models incorporate both simulated and real-world traffic flow data, charging station distribution and utilization data, and in-vehicle energy management and driver assistance data. The outcomes include optimally planned routes for EVs to destinations and charging stations, stabilized power distribution systems during peak hours, enhanced security in EV-station-grid communication, more energy-efficient storage systems, and reduced range anxiety for EV drivers. This paper systematically reviews the emerging impacts of EVs on evolving transportation systems, highlighting the latest developments in these areas and identifying potential directions for future research. By reviewing these specific challenges and solutions, this paper aims to contribute to the development of more efficient and sustainable electrified transportation systems.

    • Electric vehicles (EVs) are gaining market share over internal combustion engine vehicles (ICEVs) due to their higher energy efficiency, superior energy conversion, regenerative braking technology, and the ability to support grid-stabilizing technologies like Vehicle-to-Grid (V2G)[13]. The transition from ICEVs to EVs influences driver behavior and route choices[4,5], thereby impacting transportation systems. The differences in acceleration and deceleration patterns between EVs and ICEVs raise concerns regarding congestion and safety[6]. Furthermore, integrating EVs into smart grids via V2G technology[1] presents additional challenges, such as its impact on urban mobility. This paper examines recent advancements in these areas, with a particular focus on mathematical and machine learning models, and identifies critical gaps and future research directions.

      The increasing presence of EVs on roads not only directly impacts traffic patterns but also interacts with smart grids, introducing potential cyber vulnerabilities due to extensive communication technology use. Research indicates that EVs, whether alone or in conjunction with ICEVs during morning commutes, can cause traffic congestion[2]. One study found that a 15% and 30% increase in EV usage led to an 8.7% and 12.1% annual rise in waiting periods[7], respectively, highlighting the congestion impact of EVs on traditional traffic systems. However, other studies have proposed potential solutions. For instance, separating traffic flows and implementing optimal tolling could reduce the additional congestion caused by the growing market penetration rate (MPR) of EVs[2]. Additionally, a traffic control model incorporating traffic lights and a flow model based on total time spent has been implemented to identify congested areas, ensuring smoother traffic flow, minimizing energy consumption, and reducing emissions[8]. The adoption of micro-mobility options for shorter trips has also been suggested. A case study in Seattle (USA) estimated that replacing a significant portion of short car trips with micro-mobility options could reduce traffic congestion by up to 18%[9].

      Beyond normal commuting conditions, areas near EV charging stations are prone to traffic congestion due to factors such as the availability of parking slots, charging plugs, and charging rates[7]. Hence, the positioning, sizing, and coordination of charging stations play crucial roles in mitigating the induced congestion, which can spill over to adjacent road networks. This issue is considered as a multi-dimensional optimization problem[10] and was addressed using queueing theory to determine the optimal number of charging outlets needed to minimize wait times and optimize station utilization. Compared to concurrent models, the proposed scheme demonstrated a notable 40% increase in customer satisfaction and a 45% improvement in charging station utilization. A study using queuing theory with an M/M/C model for fast charging stations (FCS) indicated potential improvements, showing a 40% increase in EV user satisfaction and a 45% boost in FCS utilization through optimized allocation and sizing[10]. Additionally, integrating EVs into smart grids, especially those incorporating renewable energy sources (RES), can alter charging patterns, thereby influencing overall traffic flow by potentially changing when and where EVs are on the move. Since EVs spend most of their time being idle, they can fulfill surges in energy demand during peak hours through V2G technology, potentially reducing congestion near charging stations by encouraging charging during off-peak hours through lower electricity rates and through earning by plugging in the vehicles[1113]. However, the increasing integration of EVs into smart grids also raises cybersecurity concerns. These concerns are substantiated by potential attacks on the growing connections between EVs, charging infrastructures, and smart grids[1416].

      Traffic flow models are crucial for understanding the effects of increasing EV market penetration and for finding solutions to address these effects. Recent models have focused on the unique driving behaviors of EVs compared to traditional ICEVs. For example, the micro-traffic model proposed by Xu et al.[17] considered the distinct acceleration and braking patterns of EVs in both free-flow and stop-and-go traffic. Compared to behavioral models, it provided additional insights into vehicle energy consumption in complex and congested scenarios. To direct EVs to their destinations or appropriate charging stations and thereby manage traffic congestion, route planning can serve as an effective solution. For instance, Sebai et al.[18] proposed a scheme for planning EV routes that accounts for dynamic traffic phenomena, road topologies, and charging station locations. This scheme provided predictive flow identification based on previous trajectory data to plot energy-efficient maps. The algorithm was tested in real-world scenarios and demonstrated efficiency in planning optimal routes for EVs.

      In addition, Yang et al.[19] proposed a microscopic model to simulate the impacts of charging station location on traffic flow and charging load, subsequently developing a joint planning model that integrates real-world traffic network data with power distribution planning to balance traffic assignment and reduce congestion. Other studies have examined the impact of EVs on pedestrian safety, finding that they have a higher risk of collisions due to their quiet operation - 31.5% higher in one study[20] and up to 30% higher in noisy environments and 10% higher in quieter ones in another[21]. This suggests that adding alert sounds to EVs may improve pedestrian awareness[22].

      While traditional analytical and optimization methods have improved traffic efficiency, recent studies use machine learning (ML) to better plan EV routing and charging. For example, Jin et al.[23] used a Deep-Q Network (DQN) in a deep reinforcement learning framework to optimize route planning in dynamic environments. Another study combined Gaussian processes with optimization techniques to predict where to place charging infrastructure[24]. ML-based schemes for managing charging demand can also help reduce peak hour congestion by encouraging off-peak charging[25,26]. Additionally, accurately estimating an EV's state-of-charge (SoC) is important for predicting its range; Praveena & Manoj[27] developed a neural network model to improve SoC prediction accuracy.

      Limited reviews on the impacts of EVs have primarily focused on addressing challenges such as range anxiety, grid stabilization, and driver safety through innovative technologies like smart sizing and allocation of charging infrastructure, predictive SoC, and V2G[2830]. The present study complements existing literature in the following ways:

      • This paper systematically reviews the impacts of EVs on various aspects of the evolving transportation system, including travel behavior, traffic congestion, routing, and charging planning, cybersecurity, among others, with an overarching representation shown in Fig. 1.

      Figure 1. 

      The increasing adoption of EVs can pose challenges on the existing transportation network. These stem from discrepancies in acceleration/deceleration patterns of EVs and ICEVs, disparities between charging station demands and grid supplies, or even from the cybersecurity of EVs' internal communication and external communication with charging stations and local grids. The figure on the left shows possible decision-making points of EVs while being part of a smart grid network. The figure on the top right illustrates a car-following scenario involving EVs based on mathematical modeling. The figure on the bottom right demonstrates a hypothetical case of ML application in determining the optimal route to a charging station.

      • It provides a thorough summary and analysis of the latest research advancements in traffic flow models, EV-grid integration, transportation safety and security, and experimental data collection, considering the growing presence of EVs on the roads.

      • By reviewing the current state of the field and identifying promising future research directions, this study aims to inspire new insights into mitigating the potential adverse impacts of widespread EV adoption and enhancing the efficiency and reliability of future transportation systems.

    • As the MPR of EVs increases, their impacts on traffic flow, charging infrastructure, grid stability, and associated cybersecurity concerns are becoming more pronounced[7,10,1416,31]. Figure 2 provides an overview of the impacts of EVs in comparison with ICEVs, while the subsequent subsections elaborate on these impacts, highlighting future research directions to enhance the efficacy of existing technologies where necessary. Table 1 familiarizes the readers with the list of important acronyms that will be used throughout the subsequent discussions, and Table 2 summarizes the relevant studies that discuss the impacts of EVs on emerging traffic flow.

      Figure 2. 

      Illustration of the comparative impacts that EVs and ICEVs have on the transportation system. (a) shows two hypothetical values for the accelerations of EVs and ICEVs. The higher acceleration value of EVs enables them to quickly catch up with the vehicle in front during stop-and-go traffic, facilitating traffic smoothing. In contrast, ICEVs, with their comparatively slower acceleration, take longer to catch up (d), leading to ripple effects in traffic wave propagation. (b) illustrates how EVs, through their integration into smart grids and optimal allocation of charging stations, may impact the equilibrium distribution of vehicles on the road (compared to (e)). The leaf connected to an electric plug symbol indicates that this approach is environmentally friendly by accommodating renewable energy sources and reducing carbon emissions. While EVs offer certain benefits to the transportation system, they may be more vulnerable to cyber threats, such as CAN bus attacks and false data injection attacks, compared to ICEVs (c) and (f), which requires additional safety precautions.

      Table 1.  List of acronyms.

      Acronym Full form
      QoE Quality-of-Experience
      SoC State-of-Charge
      SoH State-of-Health
      ACC Adaptive Cruise Control
      ADAS Advanced Driver Assistance System
      CAN Controller Area Network
      DQN Deep-Q-Network
      DRL Deep Reinforcement Learning
      EV Electric Vehicle
      FCS Fast Charging Station
      GCN Graph Convolutional Network
      GPS Global Positioning System
      ICEV Internal Combustion Engine Vehicle
      MDP Markov Decision Process
      ML Machine Learning
      MPR Market Penetration Rate
      PDS Power Distribution System
      RES Renewable Energy Source
      RL Reinforcement Learning
      V2G Vehicle-to-Grid
    • Microscopic and macroscopic traffic flows exhibit distinct characteristics. Microscopic flow examines individual vehicle behavior, whereas macroscopic flow considers overall traffic dynamics. EVs significantly impact traffic flows at both the microscopic and macroscopic levels due to their unique acceleration and deceleration patterns compared to ICEVs. EVs typically accelerate faster from a stop, affecting stop-and-go traffic dynamics. Although ICEVs are slower initially, they tend to accelerate quickly to match EV speeds, potentially disrupting the flow in mixed traffic scenarios[6]. Fernandes et al.[32] investigated the environmental and traffic performance implications of integrating shared, electric, and automated vehicles into the transportation system. The study developed a simplified model to estimate CO2 and NOX emissions at both individual and system levels. It concluded that, in the context of an increasing MPR of EVs, these vehicles are notably more efficient at lower speeds compared to higher speeds. Additionally, EVs have been shown to be energy-efficient due to regenerative braking and optimized part-load operation in congested urban conditions, achieving up to 13% energy savings compared to ICEVs[33]. The study also indicates that EVs experience greater gains from congestion reduction compared to ICEVs, further demonstrating their potential for improved efficiency in evolving traffic scenarios.

      In addition, Wang et al.[2] used a microscopic energy consumption model to assess the impacts of increased EV MPR on traffic congestion, considering the variable exit flow rate of a morning commute model. They concluded that congestion is inevitable in mixed or all-EV scenarios. However, a staggered arrival time for EVs and ICEVs can mitigate this, modeled by parameters like the extra congestion period (ECP) and total extra congestion delay (TECD). Both ECP and TECD are eliminated at MPR values of approximately 0.718 and 0.836, respectively. Moreover, an optimal toll paradigm, ensuring both EVs and ICEVs spend the same trip time, can eliminate congestion[2]. In mixed traffic scenarios, Zhang et al.[31] employed an improved cellular automaton model, considering the unique acceleration and deceleration patterns of EVs and ICEVs. This model handled mixed traffic better than previous models, showing that increased EV penetration reduces congestion and improves safety near critical density. However, at high EV penetration rates, congestion fluctuates, and traffic safety decreases compared to homogeneous traffic.

    • This subsection discusses how the interplay between power systems and EVs can impact the entire transportation network. An overview of the main ideas is illustrated in Fig. 3. The integration of EVs and smart grids significantly affects traffic dynamics by altering charging patterns and road availability[34]. Increased EV adoption places additional demand on local grids, leading to instability, particularly during peak hours like early evening. This can cause congestion near charging stations, spilling over to adjacent roads and intersections, affecting overall traffic flow[35]. Some additional studies have assessed the impact of large-scale EV integration on grid stability and traffic congestion[3538]. For example, Tang & Wang[35] concluded that increased EV charging demand leads to higher congestion levels and nodal voltage deviation, particularly during evening peaks, which are 160% higher than morning peaks. Congestion near charging stations may persist even with V2G due to uneven station distribution or off-peak charging demand.

      Figure 3. 

      Illustration of the interactions between power systems and transportation networks, with a primary focus on EVs. Acting as mobile energy storage devices, EV integration into power grids can accommodate the uncertainty of renewable energy sources (RES) such as solar and wind. This stored energy can be fed back to the power grid during peak energy demands through V2G technology, thereby facilitating grid stability. Additionally, charging stations can contribute to grid stability by regulating the charging rates of EVs in a demand-responsive manner. Furthermore, integrating traffic network data with power distribution network data can optimize FCS allocation and route planning, as well as dynamically adjust charging schedules to reduce congestion.

      However, the proper integration of EVs with smart grids can help mitigate these issues. Acting as mobile energy storage systems, EVs can store renewable energy and supply it to the grid during peak hours, thereby alleviating extra demand[12]. V2G technology, supported by predictive control mechanisms, further curtails grid instability[11]. Tang & Wang[35] suggested nodal time-of-use and traffic congestion pricing to dynamically shift EV loads, altering charging and driving behaviors. Similarly, the dynamic pricing methodology proposed by Zhou et al.[34] can alter the charging trends of EV drivers through hourly forecasts of traffic flow and RES generated energies while suggesting optimal routes to various charging stations. This paradigm is also claimed to be efficient in reducing traffic congestion to some degree during both peak and off-peak hours. Complementing these studies, Zhang et al.[31] demonstrated that increasing the MPR of EVs and adopting V2G technology significantly improves grid and EV reliability, especially when MPR ranges from 20% to 60%. In addition, Chen et al.[36] treated the charging network as a cyber-physical system while coupling it to the transportation network and smart grid. Then, an algorithm was proposed to schedule EV charging in a way that would balance the load across unbalanced power grids by transferring energy between them. Additionally, widespread V2G adoption is expected to alter EV charging behavior, promoting off-peak charging to reduce utility costs and discharging during peak hours, potentially earning revenue and alleviating peak-hour congestion[11].

    • EVs, like other vehicles, use controller area network (CAN) bus structures for internal communication[39]. Their integration into charging infrastructure and smart grids necessitates frequent data exchanges, posing cybersecurity threats at various data points within these communications[4042]. This section discusses the vulnerabilities inherent in these systems, the potential impacts of cyberattacks on traffic flow, and explores viable solutions to mitigate these risks.

    • The internal communication among electronic components within an EV, similar to other vehicles, is facilitated by a CAN bus. However, the CAN bus protocol lacks message authentication, making it vulnerable to malicious actors. Attackers accessing the CAN bus can alter data to manipulate or disable EV functionalities, potentially causing accidents or sudden changes in driving behavior, significantly impacting traffic flow, especially with high EV density[39]. Additionally, EVs integrated into smart grids exchange data with charging stations and local grids using protocols like ISO 15118 for station-to-vehicle communication[43] and SCADA (supervisory control and data acquisition) for station-to-grid monitoring[44]. These communications expose EVs to information disclosure and tampering during charging. Cyberattacks, such as false data injection, can manipulate vital information like battery health or charging status[40,41,45]. This can result in grid instability, unexpected charging delays, or stops, causing congestion near the charging stations as other EVs wait in line.

    • The vulnerabilities discussed above underscore the need for robust security measures to protect EVs and ensure smooth traffic flow. Some exemplary approaches include:

    • The ML model developed by Avatefipour et al.[39] is capable of detecting anomalies in the CAN bus, potentially identifying and preventing cyberattacks. The authors also developed a bat algorithm to optimize the ML model's efficiency and performance, ultimately ensuring better security.

    • The risk assessment framework proposed by Shirvani et al.[42] can address cybersecurity concerns at charging stations through utilizing personalized criteria and the STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service, the elevation of privilege) threat model to evaluate vulnerabilities.

    • The STRIDE threat model[40] can also be used to assess security weaknesses in smart grid components and communication protocols, ensuring standardization across the EV ecosystem.

    • Blockchain technology[46,47] can secure EV charging data exchanges between charging stations and smart grids, making tampering difficult through timestamped and hashed data lists.

      Table 2.  Impacts of EVs, EV-grid integration, and their cybersecurity on transportation systems.

      Study Impact/concern presented Solution proposed (in case of negative impacts)
      Zare et al.[6] The differences in acceleration/deceleration patterns between EVs and ICEVs in stop-and-go traffic lead to disruptions[6]. Proposed the EVM car-following model to better assess the EV-ACC behavior in those traffic scenarios, creating future research opportunities to mitigate the disruption[6].
      Wang et al.[2],
      Zhang et al.[31]
      Whether in a mixed EV or all-EV scenario, congestion during morning commuting persists[2]. Staggering the arrival times of EVs, or implementing an optimal tolling paradigm, can help alleviate or eliminate congestion[2].
      Fluctuations in congestion and degradation of traffic safety occurs only at higher EV MPRs[31]. Increased EV penetration reduces congestion and improves safety at critical density levels[31].
      Zhou et al.[8] The integration of EVs and smart grids significantly affects traffic dynamics by altering charging patterns and road availability[8]. Dynamic pricing based on hourly forecasts of traffic flow and RES generated energy can reduce traffic congestion during both peak and off-peak hours[34].
      Mishra et al.[11],
      Rizvi et al.[12],
      Tang & Wang[35],
      Chen et al.[36]
      Increased EV charging demand leads to higher congestion levels and nodal voltage deviations[35]. Nodal time-of-use pricing and traffic congestion pricing can be used to dynamically shift EV loads[35].
      Congestion near charging stations may persist even with V2G due to uneven station distribution or off-peak charging demand[35]. EVs can act as mobile energy storage for RES and supply energy to the grid during peak hours, thereby alleviating excess demand[12].
      EV charging can facilitate load balancing by transferring energy among power grids[36].
      Large-scale V2G adoption can promote off-peak charging and peak-hour discharging, potentially alleviating congestion by altering the availability of cars on the road[11].
      Avatefipour et al.[39], Acharya et al.[40],
      Dey & Khanra[41],
      Gunduz & Das[45]
      CAN bus attacks[39] can cause accidents; false data injection attacks can manipulate vital information such as battery health or charging status[40,41,45]. ML model for CAN bus anomaly detection[39].
      Algorithms combining system dynamics knowledge with measurements[41].
    • The dynamic attack detection algoriths developed by Dey & Khanra[41] overcome the limitations in existing static attack detection algorithms by combining system dynamics knowledge and measurements.

    • As the MPR of EVs continues to rise[2], and given their unique driving patterns within mixed traffic environments[6], there is an urgent necessity for the development of new traffic flow models that address the issues and concerns posed by the increasing presence of EVs on the roads. To this need, researchers are actively developing novel models and adapting existing models to accommodate the new traffic dynamics introduced by EVs[6,10,1719,48]. The following subsections discuss the models in detail, and before diving into the associated future research directions, Table 3 summarizes the main ideas of these models.

      Table 3.  Traffic flow models to address emerging questions and concerns related to EVs.

      Study Model Main ideas
      Xu et al.[17] EV Behavioral Model To consider the unique acceleration and deceleration patterns of EVs to better understand their behavior in congested traffic.
      Sebai et al.[18] EV Route Planning with Real-Time Traffic Prediction To use real-time data, such as incidents and congestion, along with road features like slopes, to optimize routes for EVs.
      Li et al.[48] By-Level Dynamic Charging Scheduling Model To utilize real-time traffic data and day-ahead power system scheduling to optimize EV charging schedules, thereby efficiently managing charging loads and reducing wait times.
      Guler[10],
      Yang et al.[19]
      Optimal FCS Allocation and Sizing Model[10] To use a queueing algorithm to determine the optimal FCS size that maximizes EV user satisfaction and station utilization while reducing user wait times[10].
      Traffic-Aware Joint Planning Model for FCS[19] To improve on the study by Guler[10] by integrating traffic network data into a combined planning model for PDS and FCS to efficiently balance traffic flow and reduce congestion[19].
      Liu et al.[20],
      Karaaslan et al.[21]
      Pedestrian Traffic Safety Models[20,21] Liu et al.[20] used a logistic regression model to analyze factors such as pedestrian traffic and road type, finding that EVs, due to their quiet operation, are 31.5% more likely to collide with pedestrians or cyclists.
      Karaaslan et al.[21], using a simulation model, concluded that EVs are at a 30% higher risk of colliding with pedestrians in noisy environments and a 10% higher risk in quieter environments, compared to ICEVs.
      Zare et al.[6] Electric Vehicle Model To better assess the EV-ACC behavior in stop-and-go traffic and ultimately to mitigate the disruptions using these vehicles.
      Ozkan et al.[51] Green Wave Control Model To anticipate road traffic, regulate vehicle speed within a predetermined range, and ultimately reduce energy usage while extending the range of EVs.
      He et al.[52] Real-Time Traffic Prediction Model To consider the impacts of lane changing on the evolution of traffic states to optimally control the speeds of EV eco-driving to maximize energy efficiency.
      Li et al.[53] Communication-Efficient Distributed Pricing Model[53] To account for uncertainties in RES while simultaneously distributing power and pricing, and managing traffic flow assignments[53]
      Čičić &
      Canudas-De-Wit[54]
      EV Virtual Power Line Model[54] To dynamically adjust charging prices and rates at charging stations based on the concept of virtual power lines for EVs[54].
      Li et al.[55] Reliability Evaluation Model To accurately describe the spatiotemporal characteristics of PDS that incorporate microgrids to facilitate the integration of EVs into vehicle-sharing networks.
    • Despite significant contributions from researchers in accurately modeling EV-induced traffic dynamics and providing measures to mitigate the concerning impacts of the increasing MPR of EVs, substantial opportunities for improvement and adaptation remain. The following are some promising traffic flow models, along with their major strengths and limitations.

    • This study introduces a detailed traffic flow model for EVs, taking into account their unique acceleration and deceleration patterns. This approach is important for better understanding how EVs behave in traffic, especially in congested situations where their distinct characteristics are most noticeable. Although the model shows good performance in heavy traffic, it behaves similarly to simpler models in free-flowing traffic. This similarity suggests that a simpler model might be just as effective in these conditions, which could be worth investigating.

    • This study improves existing traffic flow models by using real-time data (like incidents and congestion) and road features (such as slopes) to optimize routes. Although it considers real-time data and factors affecting individual EVs, it does not account for how EVs interact with overall traffic. Including other factors like driver behavior, different vehicle types, and weather conditions could provide a more complete picture of EV traffic dynamics.

    • This study suggests a combined approach to manage both traffic flow and power grid stability. By using real-time traffic data to optimize EV charging schedules, it aims to efficiently manage charging loads and reduce wait times. The focus is on day-ahead power system scheduling, but integrating real-time power grid dynamics could make the model more effective. Since the study uses a hypothetical system, validating it with real-world data would make the findings stronger.

    • This study aims to optimize the placement of FCS to maximize EV user satisfaction and station utilization. It uses a queuing algorithm to determine the best FCS size, reducing user wait times. However, the model does not include traffic flow data, limiting its ability to assess how FCS placement and queuing affect overall traffic patterns.

    • This study improves on previous work by integrating traffic network data into a combined planning model for power distribution systems (PDS), and FCS. The method helps balance traffic flow and reduce congestion. It also tests the results using real data from two systems, making the model more relevant to real-world situations. However, it doesn't account for the effects of new technologies like V2G on EV charging patterns, which could limit its effectiveness in infrastructure that uses such technology.

    • These studies examine how adopting EVs affects pedestrian safety using different models. For example, one study used a logistic regression model to analyze factors like pedestrian traffic and road type[20], finding that EVs are 31.5% more likely to collide with pedestrians or cyclists, possibly because they are quieter. Another study used simulations to show that EVs have a higher risk of pedestrian collisions compared to ICEVs[21] - 30% higher in noisy environments and 10% higher in quieter ones. However, the crash data in the first study is from 2011 to 2018, which may not reflect recent trends in EV adoption. Additionally, neither study considers different types of EVs, which might have different noise levels and safety features.

    • In the microscopic traffic simulation model presented in a previous study[17], particular focus was placed on the unique acceleration and deceleration patterns exhibited by EVs. This is a useful approach as it enhances the understanding of EV behavior within traffic networks, especially in congested scenarios where these distinctive characteristics have significant implications[17]. With the increasing presence of EVs on roadways, the potential impact of advanced driver assistance systems (ADAS) such as ACC on traffic flow dynamics becomes more pronounced. This underscores the necessity for models that not only account for EV-specific traits but also anticipate future ADAS adoption trends. Zare et al.[6] developed an EVM that strides toward addressing these needs by capturing the unique behavioral patterns of EVs compared to ICEVs. Beyond car-following behaviors and ADAS influences, maximizing the efficiency of EVs is critical for optimizing traffic flow as their penetration rates grow. Technologies such as regenerative braking are integral to EV efficiency enhancements. For instance, Ziadia et al.[49] focused on strategies that optimize energy recovery while considering driver comfort. Unlike conventional approaches that solely aim to maximize energy capture, this study integrated naturalistic regeneration performance aligned with driver behavior preferences. By employing machine learning techniques to predict braking patterns and optimize deceleration profiles, the approach enhances efficiency while maintaining user acceptance. This underscores the significance of incorporating driver-centric strategies alongside traffic flow modeling to effectively enhance the overall efficiency and integration of EVs in complex traffic scenarios.

      Other innovative measures to enhance the efficiency of EVs in mixed traffic scenarios include energy-efficient route choices. For example, Deshpande et al.[50] proposed a model that employs real-time data from traffic lights to guide surrounding traffic along the most energy-efficient trajectories. This approach can reduce energy consumption and mitigate range anxiety in EVs. Similarly, Ozkan et al.[51] developed a green wave control strategy, which anticipates road traffic and regulates vehicle speed within a predetermined speed frame. This technique can lead to significant energy savings and ensure an extended range for EVs. In addition, lane-changing behavior was incorporated into a standard traffic flow model to enhance traffic prediction, thereby enabling more efficient eco-driving controls for EVs[52].

      In addition, as the MPR of EVs continues to increase, there is a growing need for integrating RES into smart grids to manage rising energy demand and to alleviate congestion near charging stations. To address this, Li et al.[53] proposed a data-driven optimization model that simultaneously distributes power and pricing while managing traffic flow assignments. This model also accounts for uncertainties in renewable energy generation through a robust optimization framework. Additional innovative technologies for regulating the grid and shifting power transmission include virtually controlled power plants and power lines. For instance, Čičić & Canudas-De-Wit[54] utilized a similar technique and proposed an EV virtual power lines concept to dynamically adjust charging prices and rates at charging stations. This approach can modify charging patterns and reduce peak demand periods, thereby alleviating congestion near charging stations. Finally, leveraging the potential of EVs in vehicle-sharing networks, Li et al.[55] integrated an improved charging load model with the Gauss-Markov mobility model to accurately describe the spatiotemporal characteristics of PDS incorporating microgrids. This integration can enhance the efficiency of charging load transfer throughout the network, further reducing peak demand periods.

    • While the studies mentioned above have made important contributions to modeling and managing emerging traffic flow in the context of EVs, there remain ample opportunities for further improvement and future research. For instance, the EV behavioral model[17] demonstrates the effectiveness of microscopic traffic flow models by considering unique EV acceleration and deceleration patterns. However, the added complexity of these models may not be necessary under free-flow conditions. Future research could explore simpler models that achieve similar performance under free-flow traffic while maintaining a detailed approach for congested scenarios. It is crucial that the models developed are not oversimplified. For example, the one proposed by Zare et al.[6] only covers ACC; future studies should consider incorporating more ADAS. Additionally, this model was tested only on a simulated string of EVs equipped with ACC. Further research should investigate the model's performance in more realistic scenarios, considering interactions with non-ACC vehicles, lane changes, and merging conditions[56].

      On the other hand, studies such as the one carried out by Li et al.[48] highlight the potential of combining traffic flow and power grid models. However, this approach relies on day-ahead power system scheduling, which may not sufficiently capture the evolving dynamics of the real grid. Future research could focus on integrating real-time power grid data for more flexible and responsive charging scheduling optimization. Considering the potential of integrating traffic flow data with grid and charging infrastructure data, Yang et al.[19] developed an effective approach to incorporate traffic flow information into an integrated model for PDS and FCS planning. This approach ensures smoother traffic flow while planning for the necessary infrastructure. This study paves the way for exploring innovative technologies such as V2G systems to create a more balanced EV-PDS-FCS ecosystem.

      Future research could also explore how the proposed route planning algorithm by Sebai et al.[18] can be further expanded to incorporate real-time charging station availability information for more efficient route optimization. Additionally, the route planning process could consider individual driver preferences, such as preferred driving styles or charging priorities, to add an extra layer of sophistication. While Guler[10] proposed an approach for optimal FCS allocation and sizing, it could be enhanced by using the methodologies developed by Yang et al.[19] and incorporating microscopic information such as individual driving patterns.

      In addition, Ziadia et al.[49] proposed a novel regenerative braking strategy to improve the overall efficiency of EVs. However, it is also important to explore the integration of driver-centric strategies like regenerative braking into existing traffic flow models for greater efficiency improvements. A similar approach was proposed by Deshpande et al.[50] to enhance EV efficiency in mixed traffic scenarios. However, this study did not consider battery models and state-of-health (SoH) estimation in the eco-driving algorithm to maximize battery efficiency. Similarly, the energy savings scheme of Ozkan et al.[51] could be further extended by incorporating a battery management system, and its robustness could be examined across more diverse scenarios.

      Finally, there are numerous opportunities for grid-level optimization to facilitate the integration of EVs and RES into smart grids while minimizing adverse impacts. Although Li et al.[53] incorporated a distributed pricing strategy to enhance demand-supply management and optimize traffic flow assignments, their approach could benefit from considering the optimal placement of charging stations to minimize congestion and maximize utilization. Li et al.[55] also made significant efforts to understand the impact of EV sharing on distribution networks with microgrids. However, it would be beneficial to explore the potential of implementing dynamic pricing strategies for shared EVs to optimize charging behavior and reduce peak load on the grids. In comparison to these studies, the EV virtual power lines concept with its dynamic charging rates and pricing options, as presented by Čičić & Canudas-De-Wit[54], shows considerable promise. A potential improvement would be to incorporate traffic management strategies to optimize EV routing and charging based on dynamic grid conditions. Further enhancements could be achieved by examining how dynamic pricing and incentives affect EV driver behavior and charging patterns, thus facilitating the optimization of grid utilization.

    • With the rise in EV adoption within traffic networks, there is a concurrent increase in the generation of real-time traffic flow data[2,23], presenting a unique opportunity to optimize the increasingly diverse traffic environment. ML models provide a robust framework to leverage this data either independently or in conjunction with mathematical models[2325,57]. In this section, we explore the successful applications of ML in addressing several critical challenges in evolving transportation systems involving EVs: optimizing route planning for EVs, managing demand at charging stations, efficiently managing energy use alleviating range anxiety, and integrating automation for smoother traffic flow. Finally, Table 4 provides a summary of the key implementations of the ML models discussed in the following three subsections.

      Table 4.  Machine learning applications for optimizing EV integration into transportation networks.

      Study ML model used Purpose of the model used
      Basso et al.[58,59] Bayesian Model[58] To predict energy consumption variations across different route choices, enabling the most efficient selection of routes that offer lower energy consumption and increased reliability[58].
      Safe Reinforcement Learning (SRL)[59] To solve the dynamic stochastic EV routing problem through offline learning of stochastic customer requests and energy consumption, allowing for predictive and safe online route planning that minimizes energy usage and prevents battery depletion[59].
      Lin et al.[60] Deep Reinforcement Learning (DRL) To solve the EV routing problem with time windows for commercial EV fleets.
      Jin et al.[23] Deep Q-Network (DQN) To effectively handle large-scale complex traffic network data and to facilitate the application of Markov decision processes in route planning problems.
      Li et al.[63] Graph Convolutional Network (GCN) To predict charging demand and optimize charger placement and resource allocation strategies while taking into account the market the market penetration of EVs.
      Zhang et al.[64] Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Artificial Neural Network (ANN) To facilitate charging infrastructure planning and scheduling through urban charging load forecasting, considering trip patterns and network characteristics.
      Mohammad et al.[61], Golsefidi et al.[24], Abdalrahman & Zhuang[66],
      Chen et al.[67]
      Convolutional long short-term memory (ConvLSTM), Bidirectional ConvLSTM, and Gaussian Process Regression (GPR) To capture spatio-temporal features in energy demand data from charging stations across cities and to predictively expand EV charging infrastructure.
      Praveena et al.[27] Artificial Neural Network (ANN) To accurately predict an EV's SoC[27].
      Yang et al.[69] To estimate SoH using data directly extracted from EV batteries[69].
      Chaoui et al.[68] Deep Reinforcement Learning (DRL) To optimize battery health in EVs by strategically managing the SoC of multiple energy storage devices to extend battery lifespan.
    • Complementary to traditional methods, ML can take into account factors such as real-time traffic conditions, availability of charging stations, and driver preferences when planning routes. For instance, Basso et al.[58] introduced an ML-based approach to address the EV routing problem. Their method employs a Bayesian model to predict energy consumption variations across different route choices, enabling more efficient selection of routes with lower energy consumption and increased reliability. In a subsequent study, they applied safe reinforcement learning (RL) to solve the dynamic stochastic EV routing problem (DS-EVRP)[59]. This approach incorporates offline learning of stochastic customer requests and energy consumption through Monte Carlo simulations, allowing for predictive and safe online route planning that minimizes energy usage and prevents battery depletion. The effectiveness of this approach is demonstrated through realistic traffic simulations, highlighting its potential to enhance the efficiency and reliability of EV operations. Prior to Basso et al.[59], a DRL framework was developed by
      Lin et al.[60] to solve the EV routing problem with time windows (EVRPTW) in commercial EV fleets. Their framework incorporates an attention model, utilizing a pointer network and graph embedding layer to formulate a stochastic policy. Training is conducted using policy gradient with rollout baseline, resulting in significant improvements in solving large-scale EVRPTW instances compared to existing methods. Given the complexity of traffic networks and the volume of data they generate, a route planning approach was proposed by Jin et al.[23] using DQN, a DRL algorithm, to dynamically manage MDP in such environments. This approach aims to optimize route planning while effectively handling the vast amounts of data originating from traffic networks.

    • Building on route planning, this section focuses on how ML can enhance the optimization of charging infrastructure by predicting EV demand based on traffic flow patterns and enabling flexible charging schedules. For example, Mohammad et al.[61] evaluated the Quality-of-Experience (QoE) for public EV charging stations, which effectively assesses user satisfaction and optimizes station utilization. Such metrics can serve as inputs to ML models trained on recent real-world datasets to forecast long-term charging station loads[62]. Li et al.[63] proposed a market-based approach for optimal EV charger planning using a multi-relation graph convolutional network (GCN) to predict charging demand and optimize charger placement and resource allocation strategies. Zhang et al.[64] developed an ML model for urban charging load forecasting, incorporating trip patterns and network characteristics to optimize charging infrastructure planning and scheduling. Orzechowski et al.[65] introduced a method for medium-term EV charging demand forecasting, integrating weather conditions and forecasting demand for multiple stations and the entire network. Mohammad et al.[61] proposed convolutional long short-term memory (ConvLSTM) and bidirectional ConvLSTM models to capture spatio-temporal features in energy demand data from charging stations across cities. Mejdi et al.[25] presented an online grid-level model predictive control system for predicting EV charging demand and mitigating grid impacts. Palaniyappan & Vinopraba[26] explored ML models for short-term electricity consumption forecasting and dynamic pricing to manage peak demand. Other studies underscore the potential to integrate spatiotemporal data like traffic flow patterns into ML models for improved demand forecasting and optimized charging strategies[24,66,67]. Finally, to address the escalating charging demand due to the increasing MPR of EVs, Golsefidi et al.[24] integrated Gaussian processes with optimization techniques to predictively expand EV charging infrastructure.

    • ML also plays a vital role in estimating the remaining stored energy and managing EV battery usage during planned routes, thereby mitigating range anxiety during travel. Alongside accurately estimating the state-of-charge (SoC), maintaining satisfactory battery SoH is essential for ensuring long-term EV performance and addressing range anxiety concerns. For instance, Praveena & Manoj[27] proposed a hybrid SoC estimation model for EVs that integrates machine learning with mathematical modeling. This neural network-based SoC estimation model enhances accuracy in predicting an EV's SoC, a critical factor for estimating its driving range. Meanwhile, Chaoi et al.[68] introduced a DRL approach for optimizing battery health in EVs by strategically managing the SoC of multiple energy storage devices to extend battery lifespan. Additionally, Yang et al.[69] developed a neural network-based method for SoH estimation using data directly extracted from EV batteries. Furthermore, Yang et al.[70] devised an ML-based SoH estimation model tailored for real-world EVs, which considers changes in ohmic internal resistance as a key indicator of SoH degradation, thereby offering reliable SoH assessment and driving range prediction.

      In addition to accurately identifying the SoC and SoH of EV batteries using ML models, research has investigated their connection with range anxiety among EV users, which significantly impacts EV-induced congestion. For instance, Wang et al.[71] demonstrated that in-vehicle information systems that display the remaining EV range adjusted by SoH can substantially alleviate drivers' range anxiety compared to systems lacking this information. Akasapu & Singh[72] further explored the role of in-vehicle information in mitigating range anxiety by proposing a method that utilizes current SoC to suggest optimal driving speeds, thereby maximizing travel distance. Beyond examining the effects of SoC and SoH on range anxiety, studies have explored alternative approaches to address this concern. For example, Chakraborty et al.[73] introduced the peer-to-peer car charging (P2C2) concept, enabling charging while in motion to reduce reliance on fixed charging stations. Song & Hu[74] focused on understanding driver behavior by employing an ensemble learning model to identify at what battery level EV drivers typically recharge their vehicles. The model integrates factors such as traffic conditions, charging station availability, and spatiotemporal information of charging events. Furthermore, Zhang et al.[75] suggested that faster charging could potentially alleviate anxiety by increasing SoC, although this effect is influenced by variables such as temperature and charging station availability. Hence, investigating the underlying relationships between anxiety and charging decisions remains a critical area for further research.

    • As EVs become more prevalent in our transportation network, concerns regarding their effects on traffic flow are increasingly being discussed. An effective way to examine the realistic impacts of EVs is by leveraging real-world data, which can be fed into appropriate mathematical and machine learning models to understand how these models will perform in EV-integrated traffic. To this end, multiple studies have been conducted to develop EV data collection methods, supporting further research utilizing data from real-world experiments.

      Different approaches have been developed for EV data collection. For instance, Ziryawulawo et al.[76] proposed a method that utilized an in-vehicle device to collect data from the CAN or local interconnect network (LIN) bus and wirelessly store that data in an EV driving database through general packet radio service (GPRS) and the internet. Analysis of this database provided insights into driving patterns, driving cycles, and control strategies of EVs in real-world traffic scenarios. A more recent study[77] leveraged the global navigation satellite system (GNSS), GPRS, and other auxiliary sensors to collect in-vehicle diagnostic data. This system, with its real-world data on EV driving patterns, including acceleration, braking, and speed profiles, is useful for refining traffic flow models, leading to more accurate traffic simulations and predictions. Additionally, the system can identify congestion points or areas of high EV concentration, providing insights into optimizing traffic signal timings and suggesting alternative routes.

      Range anxiety and charging infrastructure remain critical barriers to widespread EV adoption. To address these challenges, significant efforts have been made to understand and optimize EV usage patterns. For example, Ping et al.[78] proposed a real-time microscopic EV driving data collection method. This method considered microscopic driving phenomena such as instantaneous speed and acceleration, and EV states such as battery SoC. The resulting model showed significant improvements in assessing energy consumption during deceleration, with slight uplifts during acceleration and cruising. Zhuang et al.[79] complemented these efforts by collecting data from a systematic driving setup and developed a methodology for constructing representative urban driving cycles, providing a foundation for accurate energy consumption modeling. Additionally, Svendsen et al.[80] collected energy consumption data from 201 actual trips of an EV. This data was then mapped against the actual speed profiles of the EV to gain insights into how driving behavior impacts battery drainage. To further improve EV efficiency and range, Zhang & Yao[81] focused on collecting voltage information from individual lithium-ion battery cells, gathered through a sensing layer within the battery pack. The data was then transmitted via the CAN bus to the central control unit to facilitate monitoring the health and balance of individual battery cells.

      Despite the rapid increase in EV adoption, there is a lack of training datasets for developing ML models. Zhao et al.[82] introduced physical rationality in data augmentation to expand driving trip datasets, thereby facilitating data-driven approaches. The study demonstrated that synthesizing trip patterns with rational physical context leads to promising improvements in energy consumption predictions. To address the charging infrastructure challenge, previous studies[83,84] have integrated driving data with charging profiles. Specifically, Lee & Wu[83] conducted a three-year study across eight European countries, monitoring the charging and driving patterns of EVs on a monthly basis. This data is useful for determining the appropriate locations and quantities of charging stations for future deployment. Yang & Zhang[84] employed a stochastic modeling approach to generate synthetic EV driving and charging profiles using real-world global positioning system (GPS) data. This approach captures the uncertainty in EV behavior and facilitates analyses of aggregated power demand and charging station optimization.

      To accurately assess the impacts of large-scale EV adoption, Ma et al.[85] analyzed real-world data from over 40 private EVs in Beijing (China). The study examined factors such as charging habits, trip distances, and energy consumption to facilitate the strategic placement of charging stations and assist in managing the impact of broader EV adoption on electricity grids. Another study conducted in Shanghai (China), a city with one of the highest numbers of EVs, provided further insights into the real-world driving behavior of EVs[86]. The experimental data revealed charging patterns with a peak around 9:00 PM and a preference for urban charging spots. These insights are valuable for optimizing the placement of charging infrastructure.

      In addition to the optimal allocation of charging stations, optimal route planning plays a significant role in mitigating EV-induced congestion. Brady & O'Mahony[87] presented a model integrated with real-world data for energy-efficient route planning for EVs. By predicting energy consumption on various roads and prioritizing low-energy paths, the model offers a potential solution for optimizing traffic flow. The integration of real-world data enhances the model's accuracy, making it a promising tool for future EV route optimization and congestion mitigation.

      Apart from optimal routing, range anxiety is another major concern for large-scale EV adoption[88,89]. De Cauwer et al.[90] utilized real-world driving data from electric taxis to develop a more precise energy consumption prediction model using ML. The results significantly improve prediction accuracy over traditional approaches, paving the way for optimized battery sizing, energy-efficient route planning, and improved charging infrastructure operation. Motivated by both charging infrastructure limitations and range anxiety, Pevec et al.[91] used real-world traffic data to develop a more precise link-level energy consumption model for EVs, advancing EV adoption through applications such as eco-routing systems.

    • Based on a review of the literature concerning the emerging impacts of EVs on evolving traffic flow, as well as the existing mathematical and ML models aimed at assessing and mitigating these impacts, it is evident that the widespread adoption of EVs will pose challenges for transportation systems with emerging cyber-physical characteristics. However, with methodological and technological advancements, it is possible to mitigate EV-induced congestion, stabilize grid systems during peak hours, and address associated cybersecurity concerns.

      There is significant potential for dynamic electricity usage pricing and V2G technology to encourage off-peak charging, thereby mitigating both congestion near charging stations and grid instability during peak hours. Further improvements in traffic conditions can be achieved through the integration of real-time EV data into traffic management systems and the development of advanced demand response strategies for EVs. Additionally, ML applications have the potential to address cybersecurity concerns related to EVs' internal communication and their external communication with charging stations and local grids, thereby preventing undesired charging delays and grid instabilities. Overall, there are extensive opportunities for both mathematical and ML models to address various EV-related concerns, including route planning, demand management, resource allocation, and the personalization of driver assistance systems.

      • The authors confirm contribution to the paper as follows: study conception and design: Ahmed S, Wang S; data collection: Ahmed S; analysis and interpretation of results: Ahmed S, Wang S; draft manuscript preparation: Ahmed S, Wang S. Both authors reviewed the results and approved the final version of the manuscript.

      • Any data necessary for further understanding of the paper will be provided upon reasonable request by the reader.

      • The authors declare that they have no conflict of interest.

      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (3)  Table (4) References (91)
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    Ahmed S, Wang S. 2024. Systematic review of the impacts of electric vehicles on evolving transportation systems. Digital Transportation and Safety 3(4): 220−232 doi: 10.48130/dts-0024-0020
    Ahmed S, Wang S. 2024. Systematic review of the impacts of electric vehicles on evolving transportation systems. Digital Transportation and Safety 3(4): 220−232 doi: 10.48130/dts-0024-0020

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