New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Drones drones	5.2	10.0	2017	21.1 Days	CHF 2600	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Future Transportation futuretransp	2.5	3.4	2021	14.6 Days	CHF 1200	Submit
Infrastructures infrastructures	3.6	5.7	2016	18.2 Days	CHF 1800	Submit
Logistics logistics	4.4	8.1	2017	17.1 Days	CHF 1500	Submit
Modelling modelling	1.8	2.4	2020	22.7 Days	CHF 1200	Submit
Technologies technologies	5.2	6.7	2013	17 Days	CHF 1800	Submit

13 pages, 9963 KB

Open AccessArticle

Numerical and Experimental Ground Vibration Test of Composite Flying Wing

by Maciej Milewski, Jakub Wróbel, Mateusz Kucharski, Krzysztof Kaliszuk, Bartłomiej Dziewoński, Jacek Napora, Tomasz Kisiel, Paweł Bury and Artur Kierzkowski

Appl. Sci. 2026, 16(13), 6572; https://doi.org/10.3390/app16136572 - 1 Jul 2026

Viewed by 168

Abstract

Ground vibration testing (GVT) plays a key role in the validation of numerical models and the assessment of aeroelastic stability in lightweight aircraft structures. This study presents an experimental and numerical investigation of a full-scale composite flying wing unmanned aerial vehicle (UAV) intended [...] Read more.

Ground vibration testing (GVT) plays a key role in the validation of numerical models and the assessment of aeroelastic stability in lightweight aircraft structures. This study presents an experimental and numerical investigation of a full-scale composite flying wing unmanned aerial vehicle (UAV) intended for vertical take-off and landing operations. Due to its low structural mass and highly integrated configuration, the aircraft exhibits increased sensitivity to modeling assumptions, boundary conditions, and measurement uncertainties. A finite element model was developed in Ansys, incorporating detailed laminate definitions and the internal sandwich structure. Experimental modal testing was performed under free-free boundary conditions using an electrodynamic shaker and a distributed measurement consisting of 94 response locations. Frequency Response Functions (FRFs), coherence analysis, and the Complex Mode Indication Function (CMIF) were employed to identify the dominant structural modes. Particular attention was given to the bending and torsional modes that govern aeroelastic behavior. Comparison of experimental and numerical results showed good agreement in mode shapes, while discrepancies in natural frequencies ranged from 10.4% to 20.1%. The results demonstrate that the model adequately captures the dynamic behavior of the aircraft and provides a reliable basis for future aeroelastic and flutter analyses of lightweight composite flying wing. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

33 pages, 7858 KB

Open AccessArticle

A System Dynamics Model to Support Transportation Procurement Based on the Logistical Costs of Potato Distribution in Mexico

by Andrea C. Vazquez-Hernández, Ruben H. Alvarez-Mirazo and Ernesto A. Lagarda-Leyva

Logistics 2026, 10(6), 126; https://doi.org/10.3390/logistics10060126 - 3 Jun 2026

Viewed by 587

Abstract

Background: This study evaluates the return on investment (ROI) in new transport equipment using a purpose-built graphical user interface (GUI), addressing whether acquiring additional vehicles for peak demand periods is economically viable compared to optimizing the existing fleet. The research focuses on [...] Read more.

Background: This study evaluates the return on investment (ROI) in new transport equipment using a purpose-built graphical user interface (GUI), addressing whether acquiring additional vehicles for peak demand periods is economically viable compared to optimizing the existing fleet. The research focuses on agricultural product transportation—specifically potatoes—across four key routes. Methods: A system dynamics (SD) methodology was applied, combining simulation and data analysis through a GUI that enabled the adjustment of key variables, including operating costs, yields, and transportation expenses. Results: The analysis revealed notable differences in costs and profitability across the studied routes. Variables such as diesel costs and fuel efficiency proved particularly influential on outcomes. The GUI demonstrated clear value as a visualization tool, enhancing comprehension of simulated scenarios and supporting strategic decision-making. Conclusions: Investing in new transport equipment can be profitable under specific operational and economic conditions, providing a solid foundation for expansion and optimization decisions. Beyond its immediate operational contribution, the study offers a replicable profitability analysis model applicable to future projects within the company. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

38 pages, 833 KB

Open AccessReview

Bridging the Fragmentation in Unmanned Aircraft System Traffic Management (UTM): A Systematic Survey on UTM

by Guanzhen Li, Xiao Han, Yuan Shi and Leye Wang

Drones 2026, 10(5), 377; https://doi.org/10.3390/drones10050377 - 14 May 2026

Viewed by 693

Abstract

The Unmanned Aircraft System Traffic Management (UTM) system is designed to autonomously coordinate dense Unmanned Aerial Vehicles (UAVs) within shared airspace, ensuring both the efficiency and safety of aerial traffic. With the rapid proliferation of UAV applications, autonomous UTM systems have become increasingly [...] Read more.

The Unmanned Aircraft System Traffic Management (UTM) system is designed to autonomously coordinate dense Unmanned Aerial Vehicles (UAVs) within shared airspace, ensuring both the efficiency and safety of aerial traffic. With the rapid proliferation of UAV applications, autonomous UTM systems have become increasingly essential, motivating various stakeholders to develop their distinct UTM solutions. However, due to the lack of common guidelines, these emerging solutions exhibit substantial incompatibilities, which hinder the transferability of existing techniques and the overall standardization of UTM. To address the fragmentation, this paper provides a systematic survey of existing UTM research and identifies commonalities across various UTM systems. Specifically, this paper summarizes core UTM service modules and groups them with similar objectives, thereby proposing a unified UTM framework with four layers: Fundamental Infrastructure, Pre-flight UTM, In-flight UTM, and UTM Application. Based on the framework, existing solutions for each module are reviewed in detail. Furthermore, this paper draws analogies between UTM systems and more mature transportation systems, like railways, to identify transferable solutions and derive UTM future trends. This survey aims to clarify the current state of UTM research and provide guidance for future studies in this field. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

31 pages, 6573 KB

Open AccessArticle

Simulation Model and Intelligent Optimization Methods for Freight Transportation Under the Digital Transformation of the Transport System of the Republic of Kazakhstan

by Aizhan Kamysbayeva, Alisher Khussanov, Botagoz Kaldybayeva, Oleksandr Prokhorov, Zhakhongir Khussanov, Aibarsha Dosmakanbetova, Baurzhan Korganbayev and Aikerim Issayeva

Logistics 2026, 10(5), 109; https://doi.org/10.3390/logistics10050109 - 8 May 2026

Cited by 1 | Viewed by 780

Abstract

Background: In the context of the digital transformation of transport systems and the increasing complexity of logistics flows, the role of intelligent route forming methods capable of accounting for the spatial structure of transport networks, time constraints and resource limitations is growing. [...] Read more.

Background: In the context of the digital transformation of transport systems and the increasing complexity of logistics flows, the role of intelligent route forming methods capable of accounting for the spatial structure of transport networks, time constraints and resource limitations is growing. This issue is particularly relevant for the Republic of Kazakhstan, which is characterized by a vast territory, a distributed network of transport nodes and significant transit potential. Methods: This article presents an integrated model for the intelligent optimization of freight transportation based on the combined use of the Google OR-Tools library and simulation modeling in the AnyLogic environment with the application of geographic information technologies. The main variants of vehicle routing problems are implemented, including VRPTW, CVRP, and MDVRP. Results: The developed model enables both identification of optimal routes and simulation of their execution in a dynamic environment, forming the basis for a digital twin of the transport system. Experimental studies demonstrate the impact of time constraints, capacity limitations, and spatial structure on routing solutions. Conclusions: The results confirm the effectiveness of the proposed approach for logistics flow distribution in a distributed transport system and its potential for decision support in the digital transport sector. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

22 pages, 4721 KB

Open AccessArticle

Towards Sustainable Inland Transport in the Brazilian Amazon: Estimating the Effective Power of Regional Boats

by Jassiel V. H. Fontes, Irving D. Hernández, Edgar Mendoza and Rodolfo Silva

Modelling 2026, 7(3), 84; https://doi.org/10.3390/modelling7030084 - 28 Apr 2026

Viewed by 484

Abstract

A significant number of regional boats are used in the Brazilian Amazon to perform a range of social activities. However, the estimation of their propulsion parameters still requires exploring technically supported methods if the efficiency and sustainability of inland navigation is to be [...] Read more.

A significant number of regional boats are used in the Brazilian Amazon to perform a range of social activities. However, the estimation of their propulsion parameters still requires exploring technically supported methods if the efficiency and sustainability of inland navigation is to be optimized. This study explores various approaches for estimating the total resistance and effective propulsive power required by regional boats. The research examines the real case of a rabeta, a regional boat commonly used in the Brazilian Amazon, by analyzing the applicability at full scale of two approaches: the conventional Mercier–Savitsky pre-planing method and a multiphase computational fluid dynamics (CFD) approach. Using experimental data, such as boat speed and the patterns of boat-generated waves, computational analysis and the comparison of results, respectively, were carried out. It was found that for the case considered, the CFD results underpredicted the conventional approach in less than 10% for the minimum and maximum drafts considered, suggesting that both approaches are useful for estimating the effective power of artisanal boats. However, the use of CFD has the potential to visualize a greater number of parameters, such as the generated waves during vessel motion, which can facilitate the optimization of the hydrodynamics of boats, thus contributing to the sustainability of inland navigation in the region. The procedure employed in this study can be further extended to estimate the propulsive parameters of other regional vessels in the Amazon and similar areas. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

16 pages, 5250 KB

Open AccessArticle

Benchmarking Multi-Platform APIs and Fuzzy-AHP for Enhanced HAZMAT Emergency Logistics: A Case Study of Bangkok’s Expressway Network

by Wipaporn Kitthiphovanonth, Chalermchai Chaikittiporn, Arroon Ketsakorn and Korn Puangnak

Logistics 2026, 10(5), 95; https://doi.org/10.3390/logistics10050095 - 24 Apr 2026

Viewed by 1687

Abstract

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process [...] Read more.

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traffic. By employing a normalized cost function (scale 0–1), we evaluated the performance of localized (Longdo Map) versus global (Google Maps and OpenStreetMap) platforms across day and night scenarios. Results: Experimental results, yielding normalized costs between 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. Conclusions: These findings offer a practical decision-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

24 pages, 4367 KB

Open AccessArticle

A Physics-Constrained Hybrid Deep Learning Model for State Prediction in Shipboard Power Systems

by Jiahao Wang, Xiaoqiang Dai, Mingyu Zhang, Kaikai You and Jinxing Liu

Modelling 2026, 7(2), 65; https://doi.org/10.3390/modelling7020065 - 26 Mar 2026

Cited by 1 | Viewed by 802

Abstract

Accurate and physically consistent state prediction is essential for shipboard power systems (SPS) operating under dynamic conditions. However, purely data-driven models often exhibit degraded robustness and physically inconsistent outputs when exposed to transient disturbances or limited data coverage. To address these limitations, this [...] Read more.

Accurate and physically consistent state prediction is essential for shipboard power systems (SPS) operating under dynamic conditions. However, purely data-driven models often exhibit degraded robustness and physically inconsistent outputs when exposed to transient disturbances or limited data coverage. To address these limitations, this paper proposes a physics-constrained hybrid prediction model that integrates a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture with wide residual connections (WRC) and a physics-constrained loss (PCL). The proposed modeling approach combines real operational measurement data with high-resolution simulation data to enhance data diversity and improve generalization capability. The CNN–BiLSTM structure captures nonlinear temporal dependencies, while the WRC preserves critical low-level transient electrical features during deep temporal modeling. In addition, multiple physical constraints, including power balance, voltage conversion relationships, and battery state-of-charge (SOC) dynamics, are incorporated into the training process to enforce physically consistent predictions. The model is validated using charging and discharging experiments on a laboratory-scale SPS under both steady-state and transient conditions. Comparative results demonstrate that the proposed approach achieves higher prediction accuracy, improved dynamic stability, and faster recovery following disturbances compared with conventional data-driven models. These results indicate that physics-constrained deep learning provides an effective and interpretable modeling framework for SPS state prediction, supporting digital twin-oriented monitoring and real-time prediction applications. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

20 pages, 1006 KB

Open AccessArticle

A Data-Driven Discrete-Event Simulation for Assessing Passenger Dynamics and Bottlenecks in Mexico City Metro Line 7

by Elias Heriberto Arias Nava, Brendan Patrick Sullivan and Luis A. Moncayo-Martinez

Modelling 2026, 7(2), 58; https://doi.org/10.3390/modelling7020058 - 17 Mar 2026

Viewed by 892

Abstract

Mexico City’s Metro Line 7 is a critical north–south artery within one of the world’s largest metro systems, yet it suffers from persistent operational inefficiencies, including chronic overcrowding and extended passenger travel times. This research employed a data-driven discrete-event simulation model built in [...] Read more.

Mexico City’s Metro Line 7 is a critical north–south artery within one of the world’s largest metro systems, yet it suffers from persistent operational inefficiencies, including chronic overcrowding and extended passenger travel times. This research employed a data-driven discrete-event simulation model built in SIMIO to analyze the passenger dynamics of Line 7. The model was grounded in a comprehensive dataset of approximately 280,000 daily passengers over one year. Key innovations included modeling station-specific passenger arrivals as non-stationary Poisson processes with time-varying rates calculated at 15-min intervals and incorporating empirically derived walking times within stations. The simulation framework replicated the system’s operational logic, including train movements, passenger boarding and alighting, and complex transfer behaviors at interchange stations, while accounting for the influence of the broader metro network on Line 7’s passenger flows. The simulation results, derived from 100 replications, quantified severe systemic inefficiencies. The average total travel time for a passenger using Line 7 was 81.17 min. However, the ideal in-motion travel time was calculated to be only 53 min, revealing that passengers spend a disproportionate amount of time waiting. This yielded a travel time efficiency of just 65.3%. The model identified specific bottlenecks at key transfer stations like Tacubaya and San Pedro de Los Pinos, where platform utilization reaches full capacity, directly causing the excessive queuing times that degrade the overall passenger experience. This study demonstrated that the primary issue is not the speed of trains but the systemic inability to manage passenger flow during peak demand, leading to critical capacity shortfalls at specific stations. The simulation provides a quantitative tool for diagnosing these inefficiencies and offers a robust platform for prototyping and evaluating strategic interventions, such as optimized timetables and resource allocation, before costly real-world implementation. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

12 pages, 765 KB

Open AccessArticle

A Bayesian-Optimized Mixture of Experts Framework for Short-Term Traffic Flow Prediction

by Jianqing Wu, Jiaao Ren, Hui Wang, Fei Xie, Shaohan Chen and Mengjie Jiang

Modelling 2026, 7(2), 55; https://doi.org/10.3390/modelling7020055 - 16 Mar 2026

Viewed by 765

Abstract

Accurate and reliable short-term traffic flow prediction is crucial for managing urban congestion but is challenged by the complex spatio-temporal dependencies inherent in traffic systems. Conventional single models, such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), often fail to capture [...] Read more.

Accurate and reliable short-term traffic flow prediction is crucial for managing urban congestion but is challenged by the complex spatio-temporal dependencies inherent in traffic systems. Conventional single models, such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), often fail to capture these nonlinear dynamics. To address this, we propose a novel Bayesian-Optimized Mixture of Experts (BO-MoE) framework. This hybrid architecture utilizes a Mixture of Experts (MoE) to dynamically integrate multiple specialized deep learning models, allowing it to adapt to diverse and complex traffic patterns. Bayesian Optimization (BO) is further integrated to automate hyperparameter tuning, significantly enhancing predictive accuracy and model efficiency. We evaluated BO-MoE on three real-world traffic datasets. Empirical results demonstrate that our model consistently outperforms strong baselines, including TCN. Specifically, on PEMS04, it reduces MAE, RMSE, and MAPE by 1.97%, 1.19%, and 3.23%, respectively, while on PEMS08, the corresponding reductions reach 3.83%, 1.26%, and 5.49%. On the NZ dataset, BO-MoE also achieves superior performance, with improvements comparable to those on PEMS benchmarks. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

23 pages, 2148 KB

Open AccessArticle

Enhancing Traffic Efficiency Through Deep Reinforcement Learning-Based Traffic Signal Control with Cooperative Connected and Autonomous Vehicles

by Le Dinh Nghiem, Sang Hoon Bae, Pham Minh Thao and Kyoung Kuk Yoon

Appl. Sci. 2026, 16(5), 2576; https://doi.org/10.3390/app16052576 - 7 Mar 2026

Viewed by 906

Abstract

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often [...] Read more.

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

18 pages, 1635 KB

Open AccessArticle

Agent-Based Simulation of Digital Interoperability Thresholds in Fragmented Air Cargo Systems: Evidence from a Developing Country

by Siska Amonalisa Silalahi, I Nyoman Pujawan and Moses Laksono Singgih

Logistics 2025, 9(4), 160; https://doi.org/10.3390/logistics9040160 - 13 Nov 2025

Cited by 1 | Viewed by 1889

Abstract

Background: This study investigates how varying levels of digital interoperability affect coordination and performance in Indonesia’s decentralized air cargo system, reflecting the inefficiencies typical of fragmented digital infrastructures in developing economies. Methods: An Agent-Based Model (ABM) was developed to simulate interactions among shippers, [...] Read more.

Background: This study investigates how varying levels of digital interoperability affect coordination and performance in Indonesia’s decentralized air cargo system, reflecting the inefficiencies typical of fragmented digital infrastructures in developing economies. Methods: An Agent-Based Model (ABM) was developed to simulate interactions among shippers, freight forwarders, airlines, ground handlers, and customs agents along the CGK–SIN/HKG export corridor. Six simulation scenarios combined varying levels of digital adoption, operational friction, and behavioral adaptivity to capture emergent coordination patterns and threshold dynamics. Results: The simulation identified a distinct interoperability threshold at approximately 60%, beyond which performance improvements became non-linear. Once this threshold was surpassed, clearance times decreased by more than 40%, and capacity utilization exceeded 85%, particularly when adaptive decision rules were implemented among agents. Conclusions: Digital transformation in fragmented logistics systems requires both technological connectivity and behavioral adaptivity. The proposed hybrid framework—integrating Autonomous Supply Chains (ASC), Graph-Based Digital Twins (GBDT), and interoperability thresholds—provides a simulation-based decision-support tool to determine when digitalization yields system-wide benefits. The study contributes theoretically by linking behavioral adaptivity and digital interoperability within a unified modeling approach, and practically by offering a quantitative benchmark for policymakers and practitioners seeking to develop efficient and resilient logistics ecosystems. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

20 pages, 7652 KB

Open AccessArticle

Hybrid Numerical Analysis Models and Experiment Research for Wheel–Rail Noise of Urban Rail Vehicle

by Shangshuai Jia, Xinli Zhao, Wenmin Zhang, Leiming Song, Chen Hu, Hao Lin and Xiaojun Hu

Modelling 2025, 6(4), 133; https://doi.org/10.3390/modelling6040133 - 22 Oct 2025

Viewed by 1068

Abstract

For urban rail vehicles operating at speeds ranging from 60 to 250 km/h, the dominant source of radiated noise is the wheel–rail interaction. Finite element modal analysis was conducted on the wheelset, rails, and track slab. A multibody dynamics model under straight-line condition [...] Read more.

For urban rail vehicles operating at speeds ranging from 60 to 250 km/h, the dominant source of radiated noise is the wheel–rail interaction. Finite element modal analysis was conducted on the wheelset, rails, and track slab. A multibody dynamics model under straight-line condition was established. It was a rigid–flexible coupling dynamics model, including the rigid vehicle body, flexible wheelsets, flexible rails, and flexible track slabs. Dynamic simulation calculations were carried out in this model to obtain the wheel–rail forces. The finite element and boundary element models of wheels and rails were established using simulation software to obtain the results of wheel–rail noise. The sound pressure levels on the surfaces of wheels and rails were calculated under the operating conditions of 120 km/h, 140 km/h, 160 km/h, and 200 km/h in the straight-line condition. The variation law of the frequency distribution of wheel–rail noise with the change in speed was obtained. The variation fitting function of wheel–rail noise SPL with speeds was obtained. Within the speed of 200 km/h, as the speed increased, the total value of wheel–rail SPL basically shows a linear growth. The simulation analysis results were compared with the experiment results. It indicated that the simulation results were reasonable. The simulation models are of great significance for the noise prediction in train design and manufacturing. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Graphical abstract

18 pages, 2584 KB

Open AccessArticle

Evaluating Factors Influencing Dynamic Modulus Prediction: GRA-MLR Compared with Sigmoidal Modelling for Asphalt Mixtures with Reclaimed Asphalt

by Majda Belhaj, Jan Valentin, Nicola Baldo and Jan B. Król

Infrastructures 2025, 10(10), 269; https://doi.org/10.3390/infrastructures10100269 - 9 Oct 2025

Cited by 2 | Viewed by 799

Abstract

The dynamic modulus of asphalt mixtures (|E*|) is a key mechanical parameter in the design of road pavements, yet direct laboratory testing is time- and resource-intensive. This study evaluates two predictive models for estimating |E*| using data from 62 asphalt mixtures containing reclaimed [...] Read more.

The dynamic modulus of asphalt mixtures (|E*|) is a key mechanical parameter in the design of road pavements, yet direct laboratory testing is time- and resource-intensive. This study evaluates two predictive models for estimating |E*| using data from 62 asphalt mixtures containing reclaimed asphalt: a grey relational analysis–multiple linear regression (GRA-MLR) hybrid model and a mechanistic sigmoidal model. The results showed that the GRA-MLR model effectively identifies influential variables but achieved moderate predictive accuracy (R² values varying from 0.4743 to 0.6547). In contrast, the sigmoidal model outperformed across all temperature conditions (R² > 0.96) and produced predictions deviating by less than ±20% from measured values. Temperature-dependent shifts in factor influence were observed, with stiffness and gradation dominating at low temperatures and reclaimed asphalt (RA) content becoming more significant at higher temperatures. While the GRA-MLR model is advantageous, offering rapid assessments and early-stage evaluations, the sigmoidal model offers the precision suited for detailed design. Integrating both models can balance computational efficiency and provide a balanced strategy, with strong predictive reliability to advance mechanistic–empirical pavement design. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

17 pages, 3222 KB

Open AccessArticle

The Influences of Bright–Dark Lighting Environments on Driving Safety in the Diverging Zone of Interchange in Highway Tunnels

by Zechao Zhang, Jiangbi Hu, Ronghua Wang and Changqiu Jiang

Appl. Sci. 2025, 15(18), 10067; https://doi.org/10.3390/app151810067 - 15 Sep 2025

Viewed by 1140

Abstract

Increasing the lighting luminance in the diverging zone of interchange in highway tunnels can generally enhance driving safety. However, it creates a bright–dark luminance contrast with the adjacent road. A pronounced contrast can induce new driving risks. This underlying mechanism remains unclear. Three [...] Read more.

Increasing the lighting luminance in the diverging zone of interchange in highway tunnels can generally enhance driving safety. However, it creates a bright–dark luminance contrast with the adjacent road. A pronounced contrast can induce new driving risks. This underlying mechanism remains unclear. Three key factors, i.e., the luminance of the dark environment, the bright–dark luminance ratio, and the position of the small target, are identified in this paper, which affect drivers’ visual recognition abilities. Based on fundamental tunnel lighting design rules, a series of naturalistic driving tests on the visual recognition distance for small targets with 132 conditions were designed. It combined three dark environment luminance levels (1.5~3.5 cd/m²), four bright–dark luminance ratios (2~5), and eleven small target positions (−50~+50 m). Twenty-four drivers were randomly selected and drove vehicles under the different scenarios. Their visual recognition distances for small targets were recorded and analyzed. The results show that visual recognition distances for small target visuals under different bright–dark lighting environments vary significantly, and the shortest distances occur exactly at the luminance boundary. Both decreasing the bright–dark luminance ratio and proportionally increasing the luminance levels of the bright and dark environments can markedly improve the visual recognition distance. A multi-parameter regression model was developed to correlate the visual recognition distance at the bright–dark luminance boundary with the luminance of the dark environment and the bright–dark luminance ratio. Based on drivers’ required safe sight distance, a method for setting lighting luminance in the diverging zone of interchange was proposed. The methodology and findings offer technical support for lighting design and safety management in the diverging zone of interchange in highway tunnels. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

17 pages, 2687 KB

Open AccessArticle

Examining the Application Possibilities and Economic Issues of an Alternative Drive Chain in Hungary: Scenario Analysis

by Adrienn Boldizsár, Ádám Török and Norina Szander

Logistics 2025, 9(2), 77; https://doi.org/10.3390/logistics9020077 - 19 Jun 2025

Cited by 1 | Viewed by 1457

Abstract

Background: A societal shift in attitudes is going to be required to reduce greenhouse gas emissions in the field of transportation, which is crucial to the level of mitigation that can be achieved. There is increasing pressure on policymakers to address climate [...] Read more.

Background: A societal shift in attitudes is going to be required to reduce greenhouse gas emissions in the field of transportation, which is crucial to the level of mitigation that can be achieved. There is increasing pressure on policymakers to address climate change and, in turn, to promote sustainable transport. The sector’s decarbonization is essential to meet climate change targets, and alternative powertrains, particularly battery electric trucks, can play a key role. However, international research shows that the solutions and strategic plan proposals are primarily developed in isolation according to the country’s specific conditions. Methods: This study aims to compare battery electric trucks and conventional internal combustion engine trucks in Hungary, focusing on the total cost of ownership over ten years. Results: This study examines the cost parameters for operating electric and conventional trucks, based on current economic conditions. In addition, alternative studies have been carried out to see what additional savings can be expected by changing the parameters under consideration. This research examines four scenarios that model changes in state subsidies, tolls, and excise duties alongside current cost parameters. Conclusions: The results suggest that public policy interventions play a key role in developing sustainable transport systems, particularly to preserve the competitiveness of small and medium-sized enterprises. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

► Show Figures

Figure 1

Topic Menu

Topic Editors

New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition

Topic Information

Keywords

Participating Journals

Published Papers (15 papers)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Topic Menu

Topic Editors

New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition

Topic Information

Keywords

Participating Journals

Related Topic

Published Papers (15 papers)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI