Transportation Engineering最新文献_第8页

AI project portfolio ranking in port and maritime industries: a novel framework for assessing deployment complexity 港口和航运业中的人工智能项目组合排名：评估部署复杂性的新框架

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-07-17 DOI: 10.1016/j.treng.2025.100370

Mehran Farzadmehr, Valentin Carlan, Thierry Vanelslander

{"title":"AI project portfolio ranking in port and maritime industries: a novel framework for assessing deployment complexity","authors":"Mehran Farzadmehr, Valentin Carlan, Thierry Vanelslander","doi":"10.1016/j.treng.2025.100370","DOIUrl":"10.1016/j.treng.2025.100370","url":null,"abstract":"<div><div>Although AI is a trending topic and many industries are eager to adopt this technology, its successful implementation at the organizational level remains low. The majority of organizations are struggling with the assessment of AI initiatives to make informed investment decisions. The port and maritime industries are no exception in this regard. This research offers a concrete methodology to compare AI project portfolios based on their perceived deployment complexity. It first identifies the characteristics of AI initiatives that contribute to the overall deployment complexity of AI projects. Then, it leverages a list of characteristics as a typology and develops a structured framework for calculating the relative complexity of AI project portfolios and ranking them for further decision-making. A comprehensive literature review is first carried out on various topics, including technology adoption theories, to develop the AI typology. Afterward, the study calculates the deployment complexity of four AI project portfolios based on the data collected through interviews concerning the designed typology framework. The AI project portfolios are finally ranked based on their deployment complexity in the typology dimensions under different conditions. Using this novel assessment framework, port stakeholders can prioritize their AI project portfolios and start from AI initiatives under the least complex AI portfolio to build experience in AI deployments, minimizing the potential impact of failures.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"21 ","pages":"Article 100370"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring train driving automation and signaling interaction 探索列车驾驶自动化与信号交互

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-06-19 DOI: 10.1016/j.treng.2025.100361

Patrick Urassa

{"title":"Exploring train driving automation and signaling interaction","authors":"Patrick Urassa","doi":"10.1016/j.treng.2025.100361","DOIUrl":"10.1016/j.treng.2025.100361","url":null,"abstract":"<div><div>The push for better capacity utilization, punctuality, passenger comfort, and energy efficiency has accelerated the adoption of more automated trains. Concurrently, advancements in signaling systems to enhance safety and the European Union's efforts to establish a unified signaling system are becoming more evident. These technologies, although distinct, have the potential to complement each other, with automation handling the operational aspects of train driving while signaling systems oversee signaling functionalities. This study aims to investigate the interaction between train automation and signaling systems. This exploration was motivated by the observation that signaling systems may have been developed based on driver-based approaches; however, more advanced automated systems now require minimal or no human involvement. The study seeks to answer the research questions: How has research on the interaction between train-driving technologies and signaling systems developed over time? And what gaps exist in their interaction? To achieve this, a scoping study was conducted, and 75 peer-reviewed articles were included to explore the interaction of automation and signaling systems. The findings of this study indicate a versatile body of literature and a significant rise in research activity starting around 2015. The study reveals the need for specific operational adaptations to achieve higher levels of automation in advanced signaling environments. Furthermore, the identified research opportunities provide a foundation for future exploration and development in railway technologies overall. The study is done in connection to the Europe’s Rail FutuRe research project (FP6).</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"21 ","pages":"Article 100361"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuel consumption and CO₂ emissions prediction in road transport using a hybrid deep learning approach 基于混合深度学习方法的道路运输燃料消耗和二氧化碳排放预测

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-07-04 DOI: 10.1016/j.treng.2025.100364

Sami Shaffiee Haghshenas, Sina Shaffiee Haghshenas, Vittorio Astarita, Giuseppe Guido

{"title":"Fuel consumption and CO₂ emissions prediction in road transport using a hybrid deep learning approach","authors":"Sami Shaffiee Haghshenas, Sina Shaffiee Haghshenas, Vittorio Astarita, Giuseppe Guido","doi":"10.1016/j.treng.2025.100364","DOIUrl":"10.1016/j.treng.2025.100364","url":null,"abstract":"<div><div>In recent times, due to the substantial increase in population and the number of vehicles, road transportation has emerged as a crucial factor contributing to energy consumption and the release of CO2 emissions. In road transportation, the analysis of driver behavior is one of the most significant approaches to evaluating vehicle fuel consumption (FC) and CO2 emissions. Driver behavior analysis provides valuable insights into the influence of driving habits and characteristics on FC and CO2 emissions, including factors such as driving style, average vehicle speed, and acceleration pedal usage. This analysis provides a comprehensive understanding of how these factors impact real-world fuel efficiency. For this purpose, this study utilizes two non-linear methods, an artificial neural network (ANN) and a hybrid deep learning method called long short-term memory with multi-verse optimizer (LSTM-MVO), to predict FC and CO2 emissions. In this study, 370 data points were recorded, and relevant driver behaviors’ parameters were measured from mobile technology and the OBD interface for FC and CO2 emissions, respectively. The input data for modeling and determining an optimized function for predicting the amount of fuel consumed and CO2 emissions include Fuel_flow_rate/hour (FFRH), Engine_RPM (ERMP), Speed (S), Acceleration (A), and Grade (G). Also, the amount of FC and CO2 emissions are also considered outputs data. The results clearly showed that the LSTM-MVO approach can provide higher performance capacity in predicting fuel consumption and CO2 emissions compared to ANN. Finally, the results of this study emphasize its potential as a promising approach to address specific issues related to driver behavior and environmental pollution.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"21 ","pages":"Article 100364"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Validation, interpretation and use of railway track acceleration data 铁路轨道加速度数据的验证、解释和使用

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-05-28 DOI: 10.1016/j.treng.2025.100353

William Powrie , David Milne , Geoff Watson , Ben Lee , Louis Le Pen

引用次数: 0

Predicting the temporal distribution of origin-destination traffic demand using machine learning 利用机器学习预测始发目的地交通需求的时间分布

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-07-27 DOI: 10.1016/j.treng.2025.100376

Keyvan Pourhassan , Mojgan Pourhassan , Sekhar Somenahalli

{"title":"Predicting the temporal distribution of origin-destination traffic demand using machine learning","authors":"Keyvan Pourhassan , Mojgan Pourhassan , Sekhar Somenahalli","doi":"10.1016/j.treng.2025.100376","DOIUrl":"10.1016/j.treng.2025.100376","url":null,"abstract":"<div><div>Temporal distribution of travel demand provides valuable insights into the planning and operation of transport systems. As a key input to dynamic traffic assignment (DTA) models, estimation of time-dependent origin-destination (TDOD) traffic demand matrices across the modelled network gained attention in the 1980s, with significant advancements in methods and techniques since then. However, the strong reliance on observed traffic counts has long been recognised as a limitation of these approaches. In the travel demand modelling (TDM) domain, the time-dependency of travel demand has been associated with travellers' characteristics, typically implemented through theory-based choice modelling (CM) methods informed by stated or revealed preference datasets. CM's reliance on preference datasets introduces its own limitations, e.g. the high cost of conducting reliable surveys, which constrain its broader applicability for predicting the temporal distribution of travel demand. This paper demonstrates the successful application of machine learning to predict the temporal distribution of origin-destination (OD) traffic demand using TDM data, including sociodemographic and land use information of the origin and destination zones as well as OD level network statistics, e.g. travel time. By incorporating the TDM data and available count-based TDOD estimates, we construct a combined dataset, which is partitioned into training, validation and test sets to train and evaluate the machine learning models. Results show that the trained models accurately predict the temporal distribution of origin-destination traffic demand. Our approach effectively addresses the limitations associated with count-based estimation and theory-driven choice modelling approaches.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"21 ","pages":"Article 100376"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preface for the special issue on geomaterials and transportation structures under dynamic and environmental loads 动力与环境荷载作用下岩土材料与运输结构特刊前言

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-06-18 DOI: 10.1016/j.treng.2025.100357

Tatsuya Ishikawa , Geert Degrande , Erol Tutumluer

引用次数: 0

An MPC-driven VSL control of motorway traffic by employing a continuum second-order traffic model 采用连续二阶交通模型的mpc驱动VSL控制高速公路交通

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-07-24 DOI: 10.1016/j.treng.2025.100365

Waheed Imran , Alessio Tesone , Balázs Varga , Gennaro Nicola Bifulco , Luigi Pariota

{"title":"An MPC-driven VSL control of motorway traffic by employing a continuum second-order traffic model","authors":"Waheed Imran , Alessio Tesone , Balázs Varga , Gennaro Nicola Bifulco , Luigi Pariota","doi":"10.1016/j.treng.2025.100365","DOIUrl":"10.1016/j.treng.2025.100365","url":null,"abstract":"<div><div>Variable speed limit (VSL) system driven by model predictive control (MPC) is implemented as a proactive traffic control measure for effective and safer traffic flow operations. Conventionally, the application of the VSL control hinges upon the discrete second-order macroscopic models. Conversely, the MPC paradigm hardly ever employs continuum second-order models for traffic control. In this paper, an improved continuum second-order macroscopic model is used as a predictive model in VSL control driven on MPC. The VSL control is based on actual motorway traffic in Dublin, and is implemented in a microscopic traffic simulation. The findings of the study are two-fold: first, the predictive capability of the continuum second-order model is evaluated; second, the effectiveness of the MPC-driven VSL control strategy is assessed. Within this MPC paradigm, the motorway experience a significant reduction in traffic congestion. The efficacy of the proposed VSL control is demonstrated with a key performance index, flatness index (FI). FI of 0.77 and 0.72 is observed in the congested segments of the motorway, indicating moderate to strong improvement of traffic flow with the VSL control. In other words, 58% to 76% improvement is observed in the congested segments of the motorway. The proposed framework is vital for effective traffic control as it demonstrates the capability to alleviate congestion.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"21 ","pages":"Article 100365"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing and testing shape memory alloy actuators for smart aircraft wings 智能飞机机翼形状记忆合金作动器设计与试验

Transportation Engineering Pub Date : 2025-09-01 Epub Date: 2025-07-22 DOI: 10.1016/j.treng.2025.100374

Afsar Husain, Latifa Al Farsi, Muhra Alshamsi, Sanan H. Khan

{"title":"Designing and testing shape memory alloy actuators for smart aircraft wings","authors":"Afsar Husain, Latifa Al Farsi, Muhra Alshamsi, Sanan H. Khan","doi":"10.1016/j.treng.2025.100374","DOIUrl":"10.1016/j.treng.2025.100374","url":null,"abstract":"<div><div>The pursuit of enhanced aircraft performance through shape-adapting smart wings ne- cessitates lightweight, reliable, and efficient actuation systems. While Shape Memory Alloys (SMAs) offer a high power-to-weight ratio, a primary challenge remains in the validation of robust bidirectional control mechanisms within realistic airframe structures. This study presents a complete methodology for the design, additive manufacturing, and experimental testing of an aircraft flap actuated by an antagonistic SMA spring system. The core novelty lies in the holistic experimental validation of this dual-sided mechanism, which is fully integrated within a 3D-printed NACA 4412 airfoil, demonstrating a practical pathway from concept to functional prototype. A corresponding Finite Element Analysis was developed and validated to verify structural integrity under measured operational loads. Experimental testing demonstrated highly controllable and repeatable bidirectional flap deflections, achieving a maximum of 30<sup>◦</sup> of upward actuation at 12 V with response times under 20 s. The structural analysis confirmed the prototype’s robustness, with the highest stress concentrations correctly identified at the hinge. This work provides a validated, low-cost framework and critical performance data, offering a foundational step towards the practical implementation of advanced smart wing technologies. The findings underscore the viability of the antagonistic SMA design while also highlighting key operational challenges, such as the need for active cooling to improve reset times, which are critical for future development. This research serves as a comprehensive case study, bridging the gap between theoretical SMA capabilities and tangible aerospace application.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"21 ","pages":"Article 100374"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybridization of machine learning/GPR Full-Waveform Inversion for pavement tack coat characterization: Numerical validation 混合机器学习/GPR全波形反演用于路面粘性涂层表征：数值验证

Transportation Engineering Pub Date : 2025-06-01 Epub Date: 2025-03-10 DOI: 10.1016/j.treng.2025.100313

Grégory Andreoli , Amine Ihamouten , Xavier Dérobert

{"title":"Hybridization of machine learning/GPR Full-Waveform Inversion for pavement tack coat characterization: Numerical validation","authors":"Grégory Andreoli , Amine Ihamouten , Xavier Dérobert","doi":"10.1016/j.treng.2025.100313","DOIUrl":"10.1016/j.treng.2025.100313","url":null,"abstract":"<div><div>The characterization of tack coat layers in pavements using conventional techniques, such as forward processing applied to ground-penetrating radar (GPR), is challenging due to the low temporal resolution of raw signals, among other factors. In this research, we propose a double inverse method based on machine learning algorithms (SVM/SVR) combined with Full-Waveform Inversion (FWI), enabling robust detection and characterization of such thin layers. Machine learning (ML) methods applied to GPR signals have initially demonstrated the potential to detect structural anomalies from time signals (A–scans) through inverse global/local approaches. However, despite their performance, these decision-making algorithms have proven insufficiently robust when faced with various physical phenomena, whose effects are not highlighted in the raw GPR signals used in the training database. Consequently, we propose adding <em>a priori</em> material characteristics using FWI in order to boost the machine learning model. This hybridization between ML and FWI data inversion has been successfully validated on very wide numerical databases issued from an FDTD forward model. This double inverse method has shown an improved decision-making process by integrating <em>a priori</em> physical information.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"20 ","pages":"Article 100313"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamics responses of railway bridges influenced by flooded ballasted tracks subjected to high-speed trains 高速列车作用下淹没有碴轨道对铁路桥梁动力响应的影响

Transportation Engineering Pub Date : 2025-06-01 Epub Date: 2025-04-20 DOI: 10.1016/j.treng.2025.100334

Naveen Kumar Kedia , Ratabhat Wangtawesap , Chayut Ngamkhanong

{"title":"Dynamics responses of railway bridges influenced by flooded ballasted tracks subjected to high-speed trains","authors":"Naveen Kumar Kedia , Ratabhat Wangtawesap , Chayut Ngamkhanong","doi":"10.1016/j.treng.2025.100334","DOIUrl":"10.1016/j.treng.2025.100334","url":null,"abstract":"<div><div>This study focuses on the dynamics of the train-track-bridge interaction system, specifically addressing the challenges posed by saturated ballasted tracks due to insufficient drainage during extreme flooding events. A full-scale sleeper-ballast experiment is first conducted using instrumented impact hammer excitation and a minimization algorithm, which characterizes the dynamic behaviour of sleeper-ballast interaction at varying water levels (0–35 cm). The ballast stiffness and damping are condensed into a single parameter using the Guyan condensation scheme, revealing increased damping and reduced stiffness with rising water levels. The condensed parameters are then used in the two-dimensional Train-Track-Bridge-Dynamic-Interaction-Systems (TTBDIS), which are validated with published literature to carry out the parametric studies. The dynamic response of the interaction system is influenced by a complex interplay of factors, including the inverse relationship between track stiffness and damping, an aspect that has not been previously investigated. Findings indicate that lower water levels are critical for dynamic amplification on longer-span bridges, while higher water levels are critical for shorter spans. Critical speeds emerge when the bridge’s fundamental frequency aligns with higher harmonics of dominant and driving frequencies, causing dynamic responses that exceed safety limits. The general dominance weight analysis further revealed that the bridge’s mass is the most significant factor, followed by track damping, span length, track stiffness, and train speed. The findings are crucial to understanding the behaviour of railway bridges during extreme flooding, helping railway authorities mitigate their adverse impacts, making them more climate resilient, and improving design and maintenance regimes.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"20 ","pages":"Article 100334"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0