Multi-class Framework Research Articles

Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification. This methodology dynamically adjusts class probabilities using misclassification costs, making it highly effective in imbalanced datasets. Our approach is further enhanced by integrating the simplicity, transparency, and effectiveness of Bayesian networks to create a robust predictive model. Using patient admission data, the model accurately identifies key risk and protective factors for COVID-19 outcomes. Our findings indicate that certain patient characteristics, such as high Charlson Index and pre-existing conditions, significantly influence the risk of ICU admission and mortality. Moreover, we introduce an explanatory model that elucidates the interrelationships among these factors, demonstrating the influence of therapeutic limits on the overall risk assessment of COVID-19 patients. Overall, our research provides a significant contribution to the field of Machine Learning by offering a novel solution for multiclass classification in the context of imbalanced datasets. This model not only enhances predictive accuracy but also supports critical decision-making processes in healthcare, potentially improving patient outcomes and optimizing clinical resource allocation.

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In recent decades, melanoma, a lethal form of skin cancer, has become more common internationally. Due to the recovery strategy used in the medical field, dermoscopic image-based automatic skin lesion detection remains a challenging and complicated task. This difficulty in diagnosing lesions can be attributed to various factors, such as the lesions revealing diverse characteristics, including uncertain borders, inadequate color assessment, shape variations, positional dependence, and complex structures. To prevent the growing public health burden from spreading to other body organs and potentially save many lives, early detection and appropriate treatment are essential among medical professionals and researchers. An individual may develop melanoma if there is an abnormal change in the appearance of the skin. To achieve more effective cancer detection, dermatology expertise should be integrated with computer vision strategies. Consequently, it is crucial to create a variety of detection strategies to aid medical experts in diagnosing early-stage cancer. This paper provides a thorough, methodical analysis of machine learning techniques for early skin cancer detection, examining published studies. It offers an overview of an artificial intelligence-assisted evaluation approach for diagnosing skin cancer. To address existing challenges in skin cancer detection, this paper proposes a model integrating improved techniques across preprocessing, feature extraction, and classification. The proposed model utilizes adaptive histogram equalization to enhance image clarity, combined with feature extraction methods such as the ABCD rule, GLCM, and HOG, alongside deep learning-based autoencoders. For classification, it employs an ensemble approach that integrates CNNs and SVMs, leveraging transfer learning from pre-trained models and implementing a multi-class framework. This model aims to enhance detection accuracy, reduce costs, and improve usability. The review aims to provide researchers with the latest advancements in machine learning for cancer diagnostics, offering a comprehensive understanding of current and emerging techniques. Keywords: Skin Cancer, Melanoma Detection, Artificial Intelligence, Machine Learning, Deep Learning ،Neural Network Melanin.

CNVoyant a machine learning framework for accurate and explainable copy number variant classification

The precise classification of copy number variants (CNVs) presents a significant challenge in genomic medicine, primarily due to the complex nature of CNVs and their diverse impact on rare genetic diseases (RGDs). This complexity is compounded by the limitations of existing methods in accurately distinguishing between benign, uncertain, and pathogenic CNVs. Addressing this gap, we introduce CNVoyant, a machine learning-based multi-class framework designed to enhance the clinical significance classification of CNVs. Trained on a comprehensive dataset of 52,176 ClinVar entries across pathogenic, uncertain, and benign classifications, CNVoyant incorporates a broad spectrum of genomic features, including genome position, disease-gene annotations, dosage sensitivity, and conservation scores. Models to predict the clinical significance of copy number gains and losses were trained independently. Final models were selected after testing 29 machine learning architectures and 10,000 hyperparameter combinations each for deletions and duplications via fivefold cross-validation. We validate the performance of CNVoyant by leveraging a comprehensive set of 21,574 CNVs from the DECIPHER database, a highly regarded resource known for its extensive catalog of chromosomal imbalances linked to clinical outcomes. Compared to alternative approaches, CNVoyant shows marked improvements in precision-recall and ROC AUC metrics for binary pathogenic classifications while going one step further, offering multi-classification of clinical significance and corresponding SHAP explainability plots. Additionally, when provided germline CNV calls from real-world RGD cases with diagnostic CNV(s), CNVoyant correctly classified all diagnostic CNVs as having pathogenic significance with high confidence. This large-scale validation demonstrates CNVoyant’s superior accuracy and underscores its potential to aid genomic researchers and clinical geneticists in interpreting the clinical implications of real CNVs.

Cost-sensitive stacked long short-term memory with an evolutionary framework for minority class detection

‘Minority attack detection’ is a matter of great concern while designing a secure network and safeguarding it against cyber criminals who attempt to breach its defenses, intrude into its systems, and gain unauthorized access to sensitive information. An adequate intrusion detection system is demanded to counter such threats involving class imbalance and high dimensionality in network traffic. In this context, an artificial intelligence-based multiclass framework MCGAL is presented, which leverages the evolutionary technique of genetic algorithm to achieve the finest feature selection and stacked long short-term memory (LSTM) for traffic classification. The proposed approach utilizes cost-sensitive LSTM by assigning greater significance to minority or rarely occurring classes, thereby addressing the challenge of misclassification in the presence of imbalanced data in a real web attack subset of the CSE-CIC-IDS2018 dataset. A comprehensive performance analysis is performed to evaluate our proposed scheme concerning various class metrics using weighted and macro averages, which reveals a classification accuracy of 100 %. An achieved weighted average of 1.00 is then compared with baseline classifiers like decision trees, AdaBoost, and support vector machines utilizing both linear and radial kernels. The results of MCGAL are also assessed with recently reported techniques on the same dataset demonstrating remarkable improvements in all metrics such as precision, recall, and F-score of minority class detection. A pair-wise comparison and Kruskal-Wallis test at a 95 % significance level further illustrate the statistical significance of the proposed scheme for minority class detection.

A supervised multiclass framework for mineral classification of Iberian beads.

Research on personal adornments depends on the reliable characterisation of materials to trace provenance and model complex social networks. However, many analytical techniques require the transfer of materials from the museum to the laboratory, involving high insurance costs and limiting the number of items that can be analysed, making the process of empirical data collection a complicated, expensive and time-consuming routine. In this study, we compiled the largest geochemical dataset of Iberian personal adornments (n = 1243 samples) by coupling X-ray fluorescence compositional data with their respective X-ray diffraction mineral labels. This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. As a proof of concept, we developed a multiclass model and evaluated its performance on two assemblages from different Portuguese sites with current mineralogical characterisation: Cova das Lapas (n = 15 samples) and Gruta da Marmota (n = 10 samples). Our results showed that decisión-tres based classifiers outperformed other classification logics given the discriminative importance of some chemical elements in determining the mineral phase, which fits particularly well with the decision-making process of this type of model. The comparison of results between the different validation sets and the proof-of-concept has highlighted the risk of using synthetic data to handle imbalance and the main limitation of the framework: its restrictive class system. We conclude that the presented approach can successfully assist in the mineral classification workflow when specific analyses are not available, saving time and allowing a transparent and straightforward assessment of model predictions. Furthermore, we propose a workflow for the interpretation of predictions using the model outputs as compound responses enabling an uncertainty reduction approach currently used by our team. The Python-based framework is packaged in a public repository and includes all the necessary resources for its reusability without the need for any installation.

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.

The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed-trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.

A novel multi-featured decision system for multi-classification tasks

Abstract Feature engineering is a difficult task, and for real signal data, it is difficult to find a certain feature that can easily distinguish all classes. Multiple features can provide more information, which means the fusion of multi-feature learning strategies has potential significant advantages. Based on this premise, this paper proposes a multi-class framework based on the multi-featured decision to distinguish all the different classes, and takes automatic dependent surveillance-broadcast (ADS-B) signal data as an example, first extracts the phase features and wavelet decomposition features of the signal data, then selects the features with high discrimination between classes, then proposes a one-dimensional residual neural network based on 16 convolutional layers to learn the unique features of different features and classes separately, and finally proposes a novel multi-featured decision method based on voting method and a priori probability. Results show that the proposed one-dimensional residual neural network has better performance metrics on the test set compared to some machine learning-based and neural network-based algorithms, with classification accuracies of 86.1%, 84.6% and 83.6% on wavelet decomposition features, raw features and phase features, respectively, on ADS-B preamble signals. The proposed feature decision framework based on the voting method and a priori probability has a recall, precision and F1 value of 80.24%, 89.89% and 84.79% on ADS-B preamble signals, respectively.

A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.

To accurately diagnose multiple lung diseases from chest X-rays, the critical aspect is to identify lung diseases with high sensitivity and specificity. This study proposed a novel multi-class classification framework that minimises either false positives or false negatives that is useful in computer aided diagnosis or computer aided detection respectively. To minimise false positives or false negatives, we generated respective stacked ensemble from pre-trained models and fully connected layers using selection metric and systematic method. The diversity of base classifiers was based on diverse set of false positives or false negatives generated. The proposed multi-class framework was evaluated on two chest X-ray datasets, and the performance was compared with the existing models and base classifiers. Moreover, we used LIME (Local Interpretable Model-agnostic Explanations) to locate the regions focused by the multi-class classification framework.

Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features

Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.

An investigation about the relationship between dysarthria level of speech and the neurological state of Parkinson’s patients

Parkinson’s disease (PD) is the most common neurological disorder that typically affects elderly people. In the earlier stage of disease, it has been seen that 90% of the patients develop voice disorders namely hypokinetic dysarthria. As time passes, the severity of PD increases, and patients have difficulty performing different speech tasks. During the progression of the disease, due to less control of articulatory organs such as the tongue, jaw, and lips, the quality of speech signals deteriorates. Periodic medical evaluations are very important for PD patients; however, having access to a medical appointment with a neurologist is a privilege in most countries. Considering that the speech recording process is inexpensive and very easy to do, we want to explore in this paper the suitability of mapping information of the dysarthria level into the neurological state of patients and vice versa. Three levels of severity are considered in a multiclass framework using time–frequency (TF) features and random-forest along with an Error-Correcting Output Code (ECOC) approach. The multiclass classification task based on dysarthria level is performed using the TF features with words and diadochokinetic (DDK) speech tasks. The developed model shows an unweighted average recall (UAR) of 68.49% with the DDK task /pakata/ based on m-FDA level, and 48.8% with the word /petaka/ based on the UPDRS level using the Random Forest classifier. With the aim, to evaluate the neurological states using the dysarthria level, the developed models are used to predict the MDS-UPDRS-III level of patients. The highest matching accuracy of 32% with the word /petaka/ is achieved. Similarly, the multiclass classification framework based on MDS-UPDRS-III is applied to predict the dysarthria level of patients. In this case, the highest matching accuracy of 18% was obtained with the DDK tasks /pataka/.

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.

Modeling spatiotemporal patterns of gait anomaly with a CNN-LSTM deep neural network

In this work, we propose an end-to-end deep learning model that uses the skeleton data recorded by Kinect to capture spatiotemporal patterns for gait anomaly recognition. Via considering the entire skeleton, the proposed model captures the relationship between different body joints in locomotion. Unlike the common two-class or one-class approaches in skeleton-based methods, the proposed model considers a multi-class framework. Such a multi-class technique can be easily adapted for a more frequent and less expensive gait assessment outside of motion capture facilities. The proposed deep learning model is trained and evaluated on the publicly available Walking gait dataset and achieves an average accuracy of 90.57% in identifying nine different walking patterns. Through transfer learning, we also evaluate our model on two other publicly available datasets, acquiring an average accuracy of 83.64% on a dataset of three classes and 90.83% on a dataset with six classes of normal/pathological gait patterns. The results of this work indicate the potential of markerless modalities such as Kinect for designing less costly and more convenient health infrastructures for assisted living. Additionally, an automatic and non-invasive gait assessment can further augment the clinical diagnosis for an extensive list of ailments that cause different types of gait disorders.

Practical Applications of Analyzing the Evidence:Are Multiclass Mutual Funds the Right Choice?

<h3>Practical Applications Summary</h3> In <b>Analyzing the Evidence: <i>Are Multiclass Mutual Funds the Right Choice?</i></b> in the Spring 2020 edition of <i>The Journal of Index Investing</i>, <b>Jonathan Handy</b> of <b>Western Kentucky University</b>, <b>Jonathan Ricketts</b> of <b>Furman University</b>, and <b>Thomas Smythe</b> of <b>Florida Gulf Coast University</b> review the empirical and academic literature on multiclass mutual funds and draw conclusions about the suitability of such funds for investors. The authors trace the history of these investment products and examine the considerable body of evidence that multiclass funds have governance issues and incur substantial, if not excessive, expenses. Although the multiclass framework may broaden investor options, it also inherently leads to agency problems. Fund advisors often face conflicts of interest between themselves and their clients. They may be motivated to recommend a class of mutual fund shares that is inappropriate for a client but may be the most profitable for the advisor to sell. Moreover, investors are confused by the categorization schemes of multiclass funds and their associated fees. Advisors and fund managers themselves are often not fully conversant with the characteristics of the multiclass funds they are touting. The systemic weaknesses of the multiclass-fund framework should prompt reconsideration or even eschewal of these products by investors and advisors alike. <b>TOPICS:</b>Financial crises and financial market history, mutual fund performance

Stochastic Monotonicity of Markovian Multiclass Queueing Networks

Multiclass queueing networks (McQNs) extend the classical concept of the Jackson network by allowing jobs of different classes to visit the same server. Although such a generalization seems rather natural, from a structural perspective, there is a significant gap between the two concepts. Nice analytical features of Jackson networks, such as stability conditions, product–form equilibrium distributions, and stochastic monotonicity, do not immediately carry over to the multiclass framework. The aim of this paper is to shed some light on this structural gap, focusing on monotonicity properties. To this end, we introduce and study a class of Markov processes, which we call Q-processes, modeling the time evolution of the network configuration of any open, work-conservative McQN having exponential service times and Poisson input. We define a new monotonicity notion tailored for this class of processes. Our main result is that we show monotonicity for a large class of McQN models, covering virtually all instances of practical interest. This leads to interesting properties that are commonly encountered for “traditional” queueing processes, such as (i) monotonicity with respect to external arrival rates and (ii) star-convexity of the stability region (with respect to the external arrival rates); such properties are well known for Jackson networks but had not been established at this level of generality. This research was partly motivated by the recent development of a simulation-based method that allows one to numerically determine the stability region of a McQN parameterized in terms of the arrival-rates vector.

Numerical solution of a multi-class model for batch settling in water resource recovery facilities

• The numerical solution for several batch settling unit processes in WRRFs is computed. • Multi-class concentration-driven model based on settling velocity distributions used. • Non-linear convection–diffusion equations appear due to the multi-class extension. • IMEX-RK schemes combined with high order shock-capturing technology are applied. In Torfs et al. (2017) a new unified framework to model settling tanks in water resource recovery facilities was proposed providing a set of partial differential equations (PDEs) modelling different settling unit processes in wastewater treatment such as primary and secondary settling tanks (PSTs and SSTs). The extension to a multi-class framework to deal with the distributed properties of the settling particles leads to a system of non-linear hyperbolic-parabolic PDEs whose solutions may contain very sharp transitions. This necessitates the use of a consistent and robust numerical method to obtain well-resolved and reliable approximations to the PDE solutions. The use of implicit–explicit Runge–Kutta (IMEX-RK) schemes, along with the weighted essentially non-oscillatory (WENO) shock-capturing technology for the discretization of the set of equations, is advocated in this work. The versatility of the proposed unified framework is demonstrated through a set of numerical examples for batch settling occurring in both PSTs and SSTs, along with the efficiency and reliability of the numerical scheme.

Irish credit unions: Differential regulation based on business model complexity

This study examines the business model complexity of Irish credit unions using a latent class approach to measure structural performance over the period 2002 to 2013. The latent class approach allows the endogenous identification of a multi-class framework for business models based on credit union specific characteristics. The analysis finds a three class system to be appropriate with the multi-class model dependent on three financial viability characteristics. This finding is consistent with the deliberations of the Irish Commission on Credit Unions (2012) which identified complexity and diversity in the business models of Irish credit unions and recommended that such complexity and diversity could not be accommodated within a one size fits all regulatory framework. The analysis also highlights that two of the classes are subject to diseconomies of scale. This may suggest credit unions would benefit from a reduction in scale or perhaps that there is an imbalance in the present change process. Finally, relative performance differences are identified for each class in terms of technical efficiency. This suggests that there is an opportunity for credit unions to improve their performance by using within-class best practice or alternatively by switching to another class.

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

Over the past years, the health impact of airborne particulate matter $$\mathrm{PM}_{10}$$ has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient $$\mathrm{PM}_{10}$$ concentrations. In this paper, we are interested in the statistical classification of the daily mean $$\mathrm{PM}_{10}$$ concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of $$\mathrm{PM}_{10}$$ concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the $$\mathrm{PM}_{10}$$ concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of $$\mathrm{PM}_{10}$$ concentration may be crucial to get an accurate prediction.

Two-class freeway traffic regulation to reduce congestion and emissions via nonlinear optimal control

The objective of this paper is the regulation of freeway traffic by means of optimal control techniques. A first innovative aspect of the proposed approach is the adopted objective function in which, besides the reduction of traffic congestion (which is typically considered in traffic control schemes), the minimization of traffic emissions is also included. Moreover, a multi-class framework is defined in which two classes of vehicles (cars and trucks) are explicitly modelled, and specific control actions for each vehicle class are sought. This results in the formulation of a multi-objective optimal control problem which is described in the paper and for which a specific solution algorithm is developed and used. The algorithm exploits a specific version of the feasible direction algorithm whose effectiveness is demonstrated in the paper by means of simulation results.

Boosted multi-class semi-supervised learning for human action recognition

Human action recognition is a challenging task due to significant intra-class variations, occlusion, and background clutter. Most of the existing work use the action models based on statistic learning algorithms for classification. To achieve good performance on recognition, a large amount of the labeled samples are therefore required to train the sophisticated action models. However, collecting labeled samples is labor-intensive. To tackle this problem, we propose a boosted multi-class semi-supervised learning algorithm in which the co-EM algorithm is adopted to leverage the information from unlabeled data. Three key issues are addressed in this paper. Firstly, we formulate the action recognition in a multi-class semi-supervised learning problem to deal with the insufficient labeled data and high computational expense. Secondly, boosted co-EM is employed for the semi-supervised model construction. To overcome the high dimensional feature space, weighted multiple discriminant analysis (WMDA) is used to project the features into low dimensional subspaces in which the Gaussian mixture models (GMM) are trained and boosting scheme is used to integrate the subspace models. Thirdly, we present the upper bound of the training error in multi-class framework, which is able to guide the novel classifier construction. In theory, the proposed solution is proved to minimize this upper error bound. Experimental results have shown good performance on public datasets.

Multiclass Bid-Rent Network Equilibrium Model

A two-way interaction between transport and land use is generally acknowledged, but conventional approaches in travel demand analyses and urban economics have not satisfactorily represented this mutual relationship. The bid-rent network equilibrium model adopted a bilevel program under game theory for addressing this reciprocal interaction. However, the transport component of the model assumed only one type of household. This structure resulted in difficulties in representing class-specific behavior and interclass interactions of transport. This problem can be mitigated by incorporating a multiclass framework, which establishes a multiclass bid-rent network equilibrium model. The single- and multiclass models are tested on an artificial network. The simulation intends to demonstrate the ability of the model to represent the relationship between transport and the location of activities. The result shows that the developed model successfully addresses the two-way interaction and a Nash equilibrium intended.