Article
Economics
Xiaolei Wang, Jun Wang, Lei Guo, Wei Liu, Xiaoning Zhang
Summary: A new modeling approach for ridesharing user equilibrium (RUE) was proposed, which transforms the problem into a convex programming problem by redefining feasible driver trajectories and ridesharing market equilibrium conditions. The algorithm effectively avoids path enumeration and can be implemented on large networks, with theoretical analysis and numerical demonstrations on the impact of problem size on computational efficiency.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Computer Science, Interdisciplinary Applications
Sam O'Neill, Ovidiu Bagdasar, Stuart Berry, Nicolae Popovici, Ramachandran Raja
Summary: This paper presents a method of considering multiple objectives simultaneously in selfish routing of network flow. By manipulating free parameters such as speed limits, the behavior of road users is coerced to reconcile conflicts between multiple objectives. The results show that small parameter adjustments can lead to solutions that Pareto dominate other solutions.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Engineering, Multidisciplinary
Tomas Potuzak, Frantisek Kolovsky
Summary: This paper describes a technique for predicting traffic flows in individual roads of a road traffic network and presents the parallelization process of the technique, along with the convergence, usability, and speed tests conducted on real road traffic networks.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Energy & Fuels
Quan Yuan, Yujian Ye, Yi Tang, Yuanchang Liu, Goran Strbac
Summary: This paper proposes a novel deep learning based surrogate modeling method for effective modeling and optimization of EV flows and charging demand. Case studies demonstrate that the proposed method outperforms existing methods in both solution accuracy and computational performance. Coordinated spatial optimization of EV flows and charging demand benefits the operation of both TN and PDN.
Article
Engineering, Electrical & Electronic
Yan Wang, Qun Chen
Summary: This study analyzes the parking allocation problem in a multidestination multiple parking lot system, considering factors that affect driver's parking choice, and establishes a mathematical programming model. The proposed model is validated through a numerical example and discusses the impact of dynamic parking fees on driver's choice and parking lot utilization balance. The model is of significance to parking facility location optimization and dynamic parking pricing strategy.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Thermodynamics
Ze Zhou, Zhitao Liu, Hongye Su, Liyan Zhang
Summary: Dynamic wireless charging technology can alleviate range anxiety and promote the widespread use of electric vehicles. It is important to consider it as a significant charging method in future urban scenarios. This study proposes a planning strategy for the coexistence of static and dynamic charging facilities in order to maximize the comprehensive performance of the power-traffic system.
Article
Economics
Ruqing Huang, Lee D. Han, Zhongxiang Huang
Summary: This paper presents an unconventional equilibrium flow model to analyze travelers' route choice behavior, using path residual capacity as the quantity signal. The proposed model and solution algorithm are shown to be feasible and effective, and can capture route choice behaviors that have not been modeled in previous studies.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Biology
Shyam Marjit, Trinav Bhattacharyya, Bitanu Chatterjee, Ram Sarkar
Summary: This paper proposes a meta-heuristic approach called SAGA, which combines simulated annealing and genetic algorithm to identify informative genes from high-dimensional datasets. SAGA outperforms other algorithms by utilizing a clustering-based population generation and a score-based filter approach.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Kaipu Wang, Xinyu Li, Liang Gao, Peigen Li, Surendra M. Gupta
Summary: In this paper, a parallel partial disassembly line balancing model is established, and a new genetic simulated annealing algorithm is proposed to optimize the model, which can improve the disassembly efficiency and economic benefits. The proposed algorithm shows superior performance in practical applications.
APPLIED SOFT COMPUTING
(2021)
Article
Economics
Terry L. Friesz, Ke Han, Amir Bagherzadeh
Summary: This paper presents sufficient conditions for convergence of projection and fixed-point algorithms used to compute dynamic user equilibrium with elastic travel demand, without the need for strongly monotone increasing path delay operators. Instead, weakly monotone increasing path delay operators and strongly monotone decreasing inverse demand functions are assumed. The Lipschitz continuity of path delay is a mild regularity condition, allowing for convergence even with nonmonotone delay operators under certain conditions.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Engineering, Chemical
Yan Zhang, Ya-Jun Wang, Yong Zhang, Tong Yu
Summary: In this paper, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network is proposed. The algorithm utilizes adaptive genetic algorithm and simulated annealing to optimize the BP neural network for improving the maximum power point tracking of photovoltaic cells.
Article
Economics
Tongfei Li, Min Xu, Huijun Sun, Jie Xiong, Xueping Dou
Summary: In this study, a generalized stochastic user equilibrium model is developed to analyze travelers' mode and route choice behavior in urban traffic systems with ridesharing programs. The proposed model considers travelers' heterogeneity in terms of car ownership and value of time, and their limited perceived information based on the stochastic user equilibrium principle. The decision-making problem of ridesharing compensation is also addressed, aiming to minimize total travel cost and vehicular air pollution emissions. A bi-objective optimization model and two single-objective optimization models are proposed, and a genetic algorithm is used to generate Pareto-optimal solutions. Numerical experiments demonstrate the effectiveness of the proposed model and algorithm in mitigating traffic congestion and pollution emissions.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Computer Science, Information Systems
Md Shahidul Islam, Md Rafiqul Islam
Summary: In this paper, the authors investigated the RNA structure prediction problem with pseudoknots. They used genetic algorithm, simulated annealing, and their combination to predict RNA secondary structure. Through comparisons on five datasets, the combination of genetic algorithm and simulated annealing demonstrated the best performance.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Civil
Shin-Hyung Cho, Seung-Young Kho
Summary: The study aims to develop a route choice model incorporating travellers' heterogeneity based on a stochastic route choice set. The model is evaluated using empirical data from RFID device and establishes a K-reliable shortest path model considering heterogeneous behaviour. Different behaviours concerning the chosen routes are analysed by comparing the magnitude of different coefficients, revealing the travellers' heterogeneity in route choice behaviour.
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
Article
Computer Science, Information Systems
Jiehan Yu, Zhendong Xie, Zhiguo Dong, Haina Song, Jiayi Su, Honggang Wang, Jinchao Xiao, Xiaosong Liu, Jingfeng Yang
Summary: This paper proposes a design method for a bus controller based on data collection and edge computing requirements of autonomous driving buses, and develops an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. Experimental results demonstrate the feasibility of the proposed method.
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)