Similar Models Research Articles

Positive Emotions, Hope, and Life Satisfaction in Chinese Older Adults: An Application of Broaden-and-Build Model.

According to the broaden-and-build theory, positive emotions can broaden our awareness and build psychological resources, which leads to better psychological outcomes. Previous studies demonstrated that hope partially mediates the association between positive emotions and life satisfaction in college students. Yet, there has no similar model been tested among older adults. The objective of the current study was to evaluate the mediating role of hope agency and pathways in the association between positive emotions and life satisfaction. Three hundred and forty-one Chinese community-dwelling older adults (mean = 70.83, SD = 9.16) were asked to complete a survey questionnaire comprising measures assessing positive emotions, hope, life satisfaction, and other sociodemographic variables. Results showed that hope agency, but not hope pathways, partially mediated the relationship between positive emotions and life satisfaction. We discussed the importance of these initial findings concerning positive emotions and hope as predictors of life satisfaction in the older adult population.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Pericyte Control of Gene Expression in the Blood-Brain Barrier Endothelium: Implications for Alzheimer's Disease.

A strong body of evidence suggests that cerebrovascular pathologies augment the onset and progression of Alzheimer's disease (AD). One distinctive aspect of this cerebrovascular dysfunction is the degeneration of brain pericytes-often overlooked supporting cells of blood-brain barrier endothelium. The current study investigates the influence of pericytes on gene and protein expressions in the blood-brain barrier endothelium, which is expected to facilitate the identification of pathophysiological pathways that are triggered by pericyte loss and lead to blood-brain barrier dysfunction in AD. Bioinformatics analysis was conducted on the RNA-Seq expression counts matrix (GSE144474), which compared solo-cultured human blood-brain barrier endothelial cells against endothelial cells co-cultured with human brain pericytes in a non-contact model. We constructed a similar cell culture model to verify protein expression using western blots. The insulin resistance and ferroptosis pathways were found to be enriched. Western blots of the insulin receptor and heme oxygenase expressions were consistent with those observed in RNA-Seq data. Additionally, we observed more than 5-fold upregulation of several genes associated with neuroprotection, including insulin-like growth factor 2 and brain-derived neurotrophic factor. Results suggest that pericyte influence on blood-brain barrier endothelial gene expression confers protection from insulin resistance, iron accumulation, oxidative stress, and amyloid deposition. Since these are conditions associated with AD pathophysiology, they imply mechanisms by which pericyte degeneration could contribute to disease progression.

Complete characterization of NADH dehydrogenase subunit 1 gene in human hydatid cysts

يمتلك طفيلي الاكياس العدرية سلالاتين متشابهتين (G1 و G3) ونظرا للدور البارز الذي تلعبه عائلة جينات نازعات الهيدروجين في التميز بين هاتين السلالاتين، لذا هدفت الدراسة الحالية الى اجراء التوصيف الجزيئي الكامل لاحد ابرز انواع هذه العائلة وهو الجين NAD1 (من شفرة البدء الى الي شفرة التوقف) للاكياس العدرية المعزولة جراحيا من اكباد المرضى (ولمحافظتي البصرة وميسان) في مستشفى الصدر التعليمي في محافظة البصرة، العراق. عزلت الرؤيسات الاولية من الاكياس تحت ظروف معقمة واستخدمت في استخلاص الحامض النووي منقوص الاوكسجين. اذ ضخم التتابع الكامل للجين في عينات الدراسة وانجز التتابع (السكونس) لهما باستخدام بادئات متخصصه. اظهرت نتائج تحليل السكونس المتكرر Consensus sequence امتلاك كل من عينات البصرة و ميسان 1038 زوجا من القواعد وتقع شفرة البدء GTG في الموقع 87-89 وشفرة التوقف TAA في الموقع 978-980. كما اظهرت استدلالات الاحماض الامينية الى وجود 297 حامض اميني لبروتينات عينات البصرة وميسان. فضلا عن ذلك بينت تحاليل المعلومات الحياتية احتواء هذه البروتينات على الحامض الاميني Isoleucine في الموقع 188 (I188) وهو موجود فقط في سلالة الاغنام G1. وان النموذج التركيبي لبروتينات عينات البصرة وميسان اظهر تشابها كبيرا و يقع I188 في الحلزون 10 (α10). بينت الدراسة ولاول مرة على مستوى العراق التوصيف الكامل للجين NAD1 والعائد الى سلالة الاغنام G1 المسببه لمرض الاكياس العدرية البشري.

Multidimensional clustering analysis of mathematical knowledge difficulty based on Gspan

In addition to providing learners with a large amount of teaching resources, online teaching platforms can also provide learning resources and channels such as video courseware, Q&A tutoring groups, and forums. However, currently, there are still shortcomings in depth and dimensionality in mining student learning behavior data on the platform. In view of this situation, based on the learning interaction behavior, this study established the difficulty similarity model of knowledge points, and used spectral clustering to classify their difficulty. In addition, the study intended to use the maximum frequent subgraph under the Gspan framework to characterize learners’ implicit learning patterns. The outcomes expressed that the algorithm put forward in the study achieved the highest accuracy index of 98.8%, which was 1.4%, 4.0%, and 8.6% higher than Apriori-based graph mining algorithms, K-means, and frequent subgraph discovery algorithms. In terms of F1 index, the convergence value of the algorithm proposed in the study was 95.5%, which was about 2.5% higher than the last three algorithms. In addition, learners of all three cognitive levels had the highest maximum number of frequent subgraphs with sizes above 100 when the minSup value was 60%. And when the number of clusters was 3, the clustering accuracy of the three learners was the highest. In similarity calculation, the calculation method used in the study was at the minimum in terms of root mean square error and absolute error average index, which were 0.048% and 0.01% respectively. This indicated that the model proposed by the research had better classification effect on the difficulty of knowledge points for learners of different cognitive levels, and had certain application potential.

Group-aware graph neural networks for sequential recommendation

Sequential recommendations play a crucial role in recommender systems. Existing methods commonly focus on extracting sequential patterns within individual item sequences to make personalized recommendations. However, this paper argues that such a closure strategy has limitations. Firstly, the inherent uncertainty and sparsity within these sequences pose considerable challenges in accurately capturing sequential patterns. Secondly, ignoring group behaviors beyond the sequence hinders the modeling of user similarities, which are essential for recommendations. To address these limitations, this paper proposes a novel collaborative sequential recommendation approach, called Group-aware Graph Neural Networks (GaGNN). GaGNN seeks to incorporate group behaviors within sequential recommendations, thereby enhancing the modeling of user preferences through the assimilation of collective wisdom. GaGNN constructs two transition graphs, each offering a distinct behavioral perspective. The group transition graph is designed to capture the collaborative effects and learn collaborative information, whereas the individual transition graph aims to articulate individual behaviors, thereby extracting sequential information. Additionally, a multi-source information fusion module is designed to bridge semantic gaps between the two types of information and yield a holistic understanding of user preferences. Through rigorous experimental comparisons and ablation studies, the superiority of GaGNN and the beneficial integration of collaborative information are emphatically validated.

Introspective Deep Metric Learning.

This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods focus on learning a discriminative embedding to describe the semantic features of images, which ignore the existence of uncertainty in each image resulting from noise or semantic ambiguity. Training without awareness of these uncertainties causes the model to overfit the annotated labels during training and produce overconfident judgments during inference. Motivated by this, we argue that a good similarity model should consider the semantic discrepancies with awareness of the uncertainty to better deal with ambiguous images for more robust training. To achieve this, we propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. We further propose an introspective similarity metric to make similarity judgments between images considering both their semantic differences and ambiguities. The gradient analysis of the proposed metric shows that it enables the model to learn at an adaptive and slower pace to deal with the uncertainty during training. Our framework attains state-of-the-art performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets for image retrieval. We further evaluate our framework for image classification on the ImageNet-1 K, CIFAR-10, and CIFAR-100 datasets, which shows that equipping existing data mixing methods with the proposed introspective metric consistently achieves better results (e.g., +0.44% for CutMix on ImageNet-1 K).

An Experimental Platform of Heating Network Similarity Model for Test of Integrated Energy Systems

An Experimental Platform of Heating Network Similarity Model for Test of Integrated Energy Systems

Elegans-AI: How the connectome of a living organism could model artificial neural networks

This paper introduces Elegans-AI models, a class of neural networks that leverage the connectome topology of the Caenorhabditis elegans to design deep and reservoir architectures. Utilizing deep learning models inspired by the connectome, this paper leverages the evolutionary selection process to consolidate the functional arrangement of biological neurons within their networks. The initial goal involves the conversion of natural connectomes into artificial representations. The second objective centers on embedding the complex circuitry topology of artificial connectomes into both deep learning and deep reservoir networks, highlighting their neural-dynamic short-term and long-term memory and learning capabilities. Lastly, our third objective aims to establish structural explainability by examining the heterophilic/homophilic properties within the connectome and their impact on learning capabilities. In our study, the Elegans-AI models demonstrate superior performance compared to similar models that utilize either randomly rewired artificial connectomes or simulated bio-plausible ones. Notably, these Elegans-AI models achieve a top-1 accuracy of 99.99% on both Cifar10 and Cifar100, and 99.84% on MNIST Unsup. They do this with significantly fewer learning parameters, particularly when reservoir configurations of the connectome are used. Our findings indicate a clear connection between bio-plausible network patterns, the small-world characteristic, and learning outcomes, emphasizing the significant role of evolutionary optimization in shaping the topology of artificial neural networks for improved learning performance.

Across time, space, and genres: measuring probabilistic grammar distances between varieties of Mandarin

Abstract This paper aims to quantify distances between varieties of Mandarin (diachronic, regional, and situational) as a function of the similarity in the choice between syntactic variants in the Mandarin theme-recipient alternation (yŭ/gěi dative alternation). We use a novel corpus-based method, Variation-Based Distance and Similarity Modeling, which draws inspiration from work in comparative sociolinguistics and quantitative dialectometry. Analysis reveals that, while there is a relatively stable probabilistic grammar across the investigated varieties, historical varieties do exhibit a relatively higher degree of heterogeneity than synchronic varieties. Despite the overall high similarity of the latter, we identify substantial probabilistic differences between fictional writings of Modern Mainland Mandarin and all other synchronic varieties. Our findings thus provide evidence in support of the hypothesis that the transition from Early Mandarin to Modern Mandarin over the past two centuries has witnessed salient grammatical shifts and also empirically demonstrate the interaction between genre variability and regional variability in Modern Mandarin.

Online addiction analysis and identification of students by applying gd-LSTM algorithm to educational behaviour data

Abstract Internet has become the primary source of extracurricular entertainment for college students in today’s information age of Internet entertainment. However, excessive Internet addiction (IA) can negatively impact a student’s daily life and academic performance. This study used Stochastic models to gather data on campus education behaviour, extract the temporal characteristics of university students’ behaviour, and build a Stochastic dropout long short-term memory (LSTM) network by fusing Dropout and LSTM algorithms in order to identify and analyse the degree of IA among university students. The model is then used to locate and forecast the multidimensional vectors gathered, and finally to locate and evaluate the extent of university students’ Internet addiction. According to the experiment’s findings, there were 4.23% Internet-dependent students among the overall (5,861 university students), and 95.66% of those students were male. The study examined the model using four dimensions, and the experimental findings revealed that the predictive model suggested in the study had much superior predictive performance than other models, scoring 0.73, 0.72, 0.74, and 0.74 on each dimension, respectively. The prediction model outperformed other algorithms overall and in the evaluation of the four dimensions, performing more evenly than other algorithms in the performance comparison test with other similar models. This demonstrated the superiority of the research model.

Digital Sustainability for Human Resource Management Canvas Meta-Synthesis Approach

In the era of Digital Transformation (DT) and Sustainable Development (SD), the pivotal role of Human Resources (HR) in addressing business challenges has become increasingly evident. These challenges directly impact different HR departments and processes, prompting a need for a strategic overhaul. HR management should continuously adapt its service delivery model to align with the business model and create a balance between internal and external organizational expectations and employee needs, considering the two fundamental challenges of digital transformation and sustainable development, in an agile manner. The critical role of sustainable human resource management in fostering overall business sustainability is evident. However, in many cases, HR management has failed to recognize and demonstrate its cost structure, revenue flow, and social and environmental benefits transparently for the business. This research focuses on presenting the sustainable digital human resource management canvas and its connection to the business canvas to achieve sustainability in the digital era. Employing a qualitative research methodology, this study conducts a meta-synthesis of two pivotal concepts-sustainable human resource management and digital human resource management- leveraging data from reputable databases including Science Direct, Scopus, and Web of Science. Following meticulous screening and examination, a total of 17 articles from Q1-ranked journals were selected as the final articles for a more detailed and in-depth review. The findings from the meta-synthesis results consist of 5 dimensions and 23 components, ultimately presenting the sustainable digital human resource management canvas by modeling it after the business canvas. The distinct feature of this canvas, compared to other similar and conventional models, is the inclusion of individual sustainability in the value proposition and the creation of a digital value component, including artificial intelligence, the Internet of Things, and big data.

Can the giant planets of the Solar System form via pebble accretion in a smooth protoplanetary disc?

Context. Prevailing N-body planet formation models typically start with lunar-mass embryos and show a general trend of rapid migration of massive planetary cores to the inner Solar System in the absence of a migration trap. This setup cannot capture the evolution from a planetesimal to embryo, which is crucial to the final architecture of the system. Aims. We aim to model planet formation with planet migration starting with planetesimals of ~10−6−10−4 M⊕ and reproduce the giant planets of the Solar System. Methods. We simulated a population of 1000-5000 planetesimals in a smooth protoplanetary disc, which was evolved under the effects of their mutual gravity, pebble accretion, gas accretion, and planet migration, employing the parallelized N-body code SyMBAp. Results. We find that the dynamical interactions among growing planetesimals are vigorous and can halt pebble accretion for excited bodies. While a set of results without planet migration produces one to two gas giants and one to two ice giants beyond 6 au, massive planetary cores readily move to the inner Solar System once planet migration is in effect. Conclusions. Dynamical heating is important in a planetesimal disc and the reduced pebble encounter time should be considered in similar models. Planet migration remains a challenge to form cold giant planets in a smooth protoplanetary disc, which suggests an alternative mechanism is required to stop them at wide orbits.

Globotriaosylsphingosine improves risk stratification of kidney progression in Fabry disease patients

BackgroundKidney damage is common in patients with Fabry disease (FD), but more accurate information about the risk of progression to kidney failure is needed for clinical decision-making. In particular, FD patients with mild renal involvement often lack timely intervention and treatment. We aimed to utilize a model to predict the risk of renal progression in FD patients. MethodsBetween November 2011 and November 2019, ERT-naive patients with FD were recruited from three medical centers in China. To assess the risk of a 50% decline in the estimated glomerular filtration rate (eGFR) or end-stage kidney disease (ESKD), Cox proportional hazards models were utilized. The performance of these models was assessed using discrimination, calibration, and reclassification. ResultsA total of 117 individuals were enrolled. The mean follow-up time was 4.8 years, during which 35 patients (29.9 %) progressed to the composite renal outcomes. Male sex, baseline proteinuria, eGFR and globotriaosylsphingosine (Lyso-Gb3) were found to be independent risk factors for kidney progression by the Cox model, based on which a combined model containing those clinical variables and Lyso-Gb3 and clinical models including only clinical indicators were constructed. The two prediction models had relatively good performance, with similar model fit measured by R2 (59.8 % vs. 61.1 %) and AIC (51.54 vs. 50.08) and a slight increase in the C statistic (0.949 vs. 0.951). Calibration curves indicated closer alignment between predicted and actual renal outcomes in the combined model. Furthermore, subgroup analysis revealed that Lyso-Gb3 significantly improved the predictive performance of the combined model for kidney prognosis in low-risk patients with a baseline eGFR over 60 ml/min/1.73 m2 or proteinuria levels less than 1 g/d when compared to the clinical model. ConclusionsLyso-Gb3 improves the prediction of kidney outcomes in FD patients with a low risk of progression, suggesting that these patients may benefit from early intervention to assist in clinical management. These findings need to be externally validated.

Testing validity of 1D models for impurity fraction scaling for divertor detachment with EDGE2D-EIRENE

Predictions of the Huber–Chankin (HC) scaling for the upstream impurity fraction were verified in a series of EDGE2D-EIRENE (‘code’) runs for highly radiating plasmas with nitrogen injection. The main quantity extracted from the code was poloidally averaged, from X-point to X-point, separatrix impurity fraction cZ in the main scrape-off layer (SOL). Variation of the main working gas (H, D and T) revealed a qualitative agreement between the model and code results owing to the very large isotope difference in the predicted cZ values caused primarily by the inverse isotope mass dependence of the H-mode power threshold assumed in the HC model and implemented in the code. At the same time, the variation of the toroidal field and safety factor in deuterium cases yielded no correlation between the model predictions and code results. The code showed much higher local impurity fractions (fZ ) in the divertor compared to the main SOL, as well as large case-to-case variations in the divertor to the main SOL ratio of impurity fractions. The analysis of code results has wide-ranging consequences not only for the HC model, but also for other similar 1D models which use simple geometry ignoring strong neutral recycling in the divertor/ Different topology makes plasma parameters in the divertor and main SOL very different, resulting in different impurity charge state composition. Missing mechanisms in 1D codes (e.g. friction and thermo-forces exerted on impurity ions by main working gas ions) lead to impurity density redistribution. Neglecting all above factors, 1D models assume a constant impurity fraction along field lines.

The Role of the Intraspecific Variability of Hydraulic Traits for Modeling the Plant Water Use in Different European Forest Ecosystems

AbstractThe drought resilience of forest ecosystems is generally believed to depend on the dominant tree species' hydraulic traits. These traits define the maximum water transport capacity and the degree of vulnerability to hydraulic failure of a tree species. This work evaluates the effect of the intraspecific variability of hydraulic traits on the simulated tree water use in the Community Land Model (CLM, version 5.0). We selected two contrasting broadleaved tree species and performed a series of numerical experiments by modifying the parameters of the plant vulnerability curve and the maximum xylem hydraulic conductance accounting for the variability within each species. Our prescribed parameter sets represent vulnerable and resistant tree responses to the water deficit. At sites with an ample water supply, the resistant configuration simulates reduced water stress and increased transpiration compared to the vulnerable configuration. Meanwhile, the model results are counter‐intuitive at temporarily dry sites when water availability is the limiting factor. The numerical experiments demonstrate the emergent role of the maximum xylem conductance as a modulator of the plant water use strategy and the simulated transpiration within the model. Using the default value for maximum xylem conductance, the model tends to overestimate the early summer transpiration at drier sites, forcing the vegetation to experience unrealistic water stress later in the year. Our findings suggest that the parameterization of maximum xylem conductance is an important yet unresolved problem in the CLM and similar land surface models.

High-frequency channel attention and contrastive learning for image super-resolution

AbstractOver the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general, recovering high-frequency features is crucial for high-performance models. High-frequency features suffer more serious damages than low-frequency features during downscaling, making it hard to recover edges and textures. In this paper, we attempt to guide the network to focus more on high-frequency features in restoration from both channel and spatial perspectives. Specifically, we propose a high-frequency channel attention (HFCA) module and a frequency contrastive learning (FCL) loss to aid the process. For the channel-wise perspective, the HFCA module rescales channels by predicting statistical similarity metrics of the feature maps and their high-frequency components. For the spatial perspective, the FCL loss introduces contrastive learning to train a spatial mask that adaptively assigns high-frequency areas with large scaling factors. We incorporate the proposed HFCA module and FCL loss into an EDSR baseline model to construct the proposed lightweight high-frequency channel contrastive network (HFCCN). Extensive experimental results show that it can yield markedly improved or competitive performances compared to the state-of-the-art networks of similar model parameters.

Salutogenic health measures: Psychometric properties of the Danish versions of the Flourish Index and the Secure Flourish Index.

Measurement of human flourishing represents a salutogenic approach to epidemiological and behavioral research emphasizing factors contributing to "good lives" rather than pathology. The objective of this study was to translate and psychometrically test the 10-item Flourish Index (FI) and 12-item Secure Flourish Index (SFI) in a convenience sample of Danish adults. A total of 325 participants completed a cross-sectional survey, with 148 of those participants completing the survey a second time (retest). Confirmatory factor analysis in a structural equation modelling framework was used to establish structural validity by comparing four different pre-specified models of the indexes. Additionally, internal consistency, convergent and incremental validity, and retest reliability were examined. The FI models exhibited superior structural validity compared with similar models of the SFI, although all models had good fits. Internal consistencies with McDonald's omega were 0.89 and 0.87 for the FI and SFI, respectively. The five (FI) or six (SFI) domains were happiness & life satisfaction, mental & physical health, meaning & purpose, character & virtue, close social relationships, and financial & material stability (λ4 = 0.51-0.91). Convergent and incremental validity tests supported predefined hypotheses. Retest analyses with the FI and SFI showed a high degree of retest reliability. Based on the psychometric evidence reported in this study, the Danish Flourish Index and Secure Flourish Index seem suitable for use with healthy adult Danes. The authors hope that this psychometric evaluation of the FI and SFI will stimulate research on patterns, health and economic outcomes, and predictors of human flourishing in Denmark.

Analysis of Pressure Distribution on a Single-Family Building Caused by Standard and Heavy Winds Based on a Numerical Approach

The aim of this research is to analyze the pressure distribution caused by wind pressure on the structure of a single-family house. The research object is a model reflecting a real structure, which was damaged in 2018 because of heavy winds. The main idea is to create numerical models using various complex structural analysis software and compare the results. The obtained results will be compared with each other to analyze the impact of various factors, hereinafter referred to as boundary conditions, on the pressure values in characteristic places of the facility. The values closest to the normal distribution will be compared to the actual damage to the house structure. The essence of the research will be the identification of phenomena occurring during the action of heavy winds in global conditions (European and American), considering modifications and different ways of creating seemingly similar numerical models and the way they work. Everything will be compared with each other to find the most optimal design method in the given programs and to obtain wind pressure results that are closest to the real ones.

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems.