Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2161-2194, 2024, DOI:10.32604/cmc.2024.058888 - 18 November 2024

Abstract Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may… More >

Pengfei Duan^1,*, Luling Li¹, Jianhui Liu¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012431

Abstract The supply of hydrogen-blended natural gas to civil and industrial users can assist downstream firm to achieve carbon emission reduction, and ensure energy security as an alternative gas source. This application mode has been widely concerned by urban gas enterprises. This paper focuses on the leakage problem of hydrogen-blended pipelines in utility tunnel due to corrosion and other reasons. Using dimensional analysis method, a model experiment is designed to verify that the three-dimensional compressible fluid model coupled with transport equations can effectively simulate the concentration change of hydrogen-blended natural gas after leakage in the utility… More >

Yijun Gao^1,2, Yong Wang^1,*, Qing Na¹, Jiawei Zhang¹, Aixiang Wu¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2443-2458, 2024, DOI:10.32604/fdmp.2024.051385 - 28 October 2024

Abstract Filling methods in the mining industry can maximize the recovery of mineral resources and protect the underground and surface environments. In recent years, such methods have been widely used in metal mines where pipeline transportation typically plays a decisive role in the safety and stability of the entire filling system. Because the filling slurry contains a large percentage of solid coarse particles, the involved pipeline is typically eroded and often damaged during such a process. A possible solution is the so-called nesting repair technology. In the present study, nesting a 127 mm outer diameter pipeline… More >

Fan Yang^1,2,*, Honggang Mi^1,2, Jian Wu^1,2, Qi Yang^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2637-2656, 2024, DOI:10.32604/fdmp.2024.048574 - 28 October 2024

Abstract The gas production of deep coalbed methane wells in Linxing-Shenfu block decreases rapidly, the water output is high, the supporting effect is poor, the effective supporting fracture size is limited, and the migration mechanism of proppant in deep coal reservoir is not clear at present. To investigate the migration behavior of proppants in complex fractures during the volume reconstruction of deep coal and rock reservoirs, an optimization test on the conductivity of low-density proppants and simulations of proppant migration in complex fractures of deep coal reservoirs were conducted. The study systematically analyzed the impact of… More >

Jingfa Ma, Hu Liu^*, Lingxiao Chen

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 443-469, 2024, DOI:10.32604/cmc.2024.056209 - 15 October 2024

Abstract Demand-responsive transportation (DRT) is a flexible passenger service designed to enhance road efficiency, reduce peak-hour traffic, and boost passenger satisfaction. However, existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs. Consequently, there is a need to develop real-time DRT route optimization methods that integrate both initial and real-time requests. This paper presents a two-stage, multi-objective optimization model for DRT vehicle scheduling. The first stage involves an initial scheduling model aimed at minimizing vehicle configuration, and operational, and CO₂ emission costs while ensuring passenger satisfaction. The second stage develops a real-time scheduling… More >

Yongsheng Zhu^1,2,*, Chong Liu^3,4, Chunlei Chen⁵, Xiaoting Lyu^3,4, Zheng Chen^3,4, Bin Wang⁶, Fuqiang Hu^3,4, Hanxi Li^3,4, Jiao Dai^3,4, Baigen Cai¹, Wei Wang^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1305-1325, 2024, DOI:10.32604/cmes.2024.054820 - 27 September 2024

Abstract The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency. Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data. However, despite its privacy benefits, federated learning systems are vulnerable to poisoning attacks, where adversaries alter local model parameters on compromised clients and send malicious updates to the server, potentially compromising the global model’s accuracy. In this study, we introduce PMM (Perturbation coefficient Multiplied by Maximum value), a new poisoning attack method that perturbs model More >

Benxin Li^*, Xuanming Chang

Energy Engineering, Vol.121, No.10, pp. 3001-3018, 2024, DOI:10.32604/ee.2024.051332 - 11 September 2024

Abstract The increasingly large number of electric vehicles (EVs) has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks. To address this issue, an EV charging station load prediction method is proposed in coupled urban transportation and distribution networks. Firstly, a finer dynamic urban transportation network model is formulated considering both nodal and path resistance. Then, a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature. Thirdly, the Monte Carlo method… More > Graphic Abstract

Nguyen Huu Loc Khuu^1,2,3, Thuy Duy Truong^1,2,3, Quoc Dien Le^1,2,3, Tran Thanh Cong Vu^1,2,3, Hoa Binh Tran^1,2,3, Tuong Quan Vo^1,2,3,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 255-279, 2024, DOI:10.32604/iasc.2024.043854 - 21 May 2024

Abstract Optimizing Flow Path Design (FPD) is a popular research area in transportation system design, but its application to Overhead Transportation Systems (OTSs) has been limited. This study focuses on optimizing a double-spine flow path design for OTSs with 10 stations by minimizing the total travel distance for both loaded and empty flows. We employ transportation methods, specifically the North-West Corner and Stepping-Stone methods, to determine empty vehicle travel flows. Additionally, the Tabu Search (TS) algorithm is applied to branch the 10 stations into two main layout branches. The results obtained from our proposed method demonstrate More >

Mohammed Alatiyyah^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 875-907, 2024, DOI:10.32604/cmc.2024.047790 - 25 April 2024

Abstract In the contemporary era of technological advancement, smartphones have become an indispensable part of individuals’ daily lives, exerting a pervasive influence. This paper presents an innovative approach to passenger counting on buses through the analysis of Wi-Fi signals emanating from passengers’ mobile devices. The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels, thereby addressing a crucial aspect of public transportation. The proposed system comprises three crucial elements: Signal capture, data filtration, and the calculation and estimation of passenger numbers. The pivotal findings reveal that the system demonstrates commendable… More >

Yunchang Liu^1,*, Fei Wan¹, Chengwu Liang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4343-4361, 2024, DOI:10.32604/cmc.2024.047211 - 26 March 2024

Abstract Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph More >