Hasil untuk "Highway engineering. Roads and pavements"

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.

Marek Pawlik

Abstract: The requirements of the CPK Railway Standards in selected technical areas were presented during the International Scientific and Technical Conference ‘High-Speed Railway Development’ in Poland. These standards comprise thirty-two volumes. Most users read them as a set of requirements relevant to their specific area of interest. However, it is worth considering them from a broader perspective. This is precisely what this article aims to explore. Keywords: High-speed rail (HSR); HSR legal regulations; Standardisation; HSR technical standards

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Milin Patel, Marzana Khatun, Rolf Jung et al.

One of the fundamental tasks of autonomous driving is safe trajectory planning, the task of deciding where the vehicle needs to drive, while avoiding obstacles, obeying safety rules, and respecting the fundamental limits of road. Real-world application of such a method involves consideration of surrounding environment conditions and movements such as Lane Change, collision avoidance, and lane merge. The focus of the paper is to develop and implement safe collision free highway Lane Change trajectory using high order polynomial for Highly Automated Driving Function (HADF). Planning is often considered as a higher-level process than control. Behavior Planning Module (BPM) is designed that plans the high-level driving actions like Lane Change maneuver to safely achieve the functionality of transverse guidance ensuring safety of the vehicle using motion planning in a scenario including environmental situation. Based on the recommendation received from the (BPM), the function will generate a desire corresponding trajectory. The proposed planning system is situation specific with polynomial based algorithm for same direction two lane highway scenario. To support the trajectory system polynomial curve can be used to reduces overall complexity and thereby allows rapid computation. The proposed Lane Change scenario is modeled, and results has been analyzed (verified and validate) through the MATLAB simulation environment. The method proposed in this paper has achieved a significant improvement in safety and stability of Lane Changing maneuver.

Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu et al.

We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Yubian Wang, Rui Li, Ruilin Chen et al.

Inna Yermakova, Maksym Nechyporenko

Introduction. The article deals with the analysis and coverage of statistical data related to the development and application of temporary loads and the relevant norms for the design of bridge structures. Problem Statement. Over the past 5-7 years, the highway bridges of Ukraine have undergone significant changes in the intensity and composition of traffic flows. At the same time, the load, which is established by the current regulations, does not take into account the tendency to increase the weight of cars. At the same time, a significant part of the building's life cycle takes place under conditions of overload, which leads to premature destruction and overspending on repairs. In this regard, it is not always possible to unambiguously assign the type of calculated load, which can cause significant errors in the assessment of the strength and serviceability of bridge structures. Materials and methods. Comparison of estimated vehicle loads of models H-10, H-13, H-18, NK-80, Н-30, NK-100, NG-60, A11, A15, AB-51, AB-74, AB-151 (from 1931 to 2023) with the LM1 model of Eurocode EN 1991-2 shows that there is some balance between the two. The estimated load according to Ukrainian standards is intermediate between the standards of Europe and the USA. However, the level of characteristic loads is almost twice as low as in the Eurocode. This is a significant shortcoming of state building regulations, as it reduces the level of reliability of calculations.

Ieva Jakubėnaitė, Audrius Vaitkus, Judita Škulteckė et al.

Permeable asphalt pavement is one of the sustainable solutions to remove water from road surfaces. The aim of the research is to analyse the performance of permeable asphalt mixtures depending on the different nominal maximum size and, as a result, to determine the minimum air voids content, which ensures that the asphalt pavement is permeable. To analyse the permeability of asphalt mixtures, ten porous asphalt mixtures with different air voids content and nominal maximum size were designed and tested in terms of air voids content, horizontal and vertical water permeability, water sensitivity, water sensitivity after ultraviolet radiation and mass loss. The results showed that the PA 16 mixture, designed according to the technical requirements TRA ASPHALT 08, was the most porous and permeable mixture, while the modified PA 8 mixture (PA 8_M2) had the lowest air voids content and permeability. Based on the importance of vertical water permeability (0.5%), mass loss (0.3%), water sensitivity (ITSR) (0.2%), the Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods showed that PA 11 (0.729 and 0.745) and PA 16 (0.684 and 0.631) had the highest overall weights.

Teerapong Panboonyuen

This paper introduces a novel framework for detecting and segmenting critical road assets on Thai highways using an advanced Refined Generalized Focal Loss (REG) formulation. Integrated into state-of-the-art vision-based detection and segmentation models, the proposed method effectively addresses class imbalance and the challenges of localizing small, underrepresented road elements, including pavilions, pedestrian bridges, information signs, single-arm poles, bus stops, warning signs, and concrete guardrails. To improve both detection and segmentation accuracy, a multi-task learning strategy is adopted, optimizing REG across multiple tasks. REG is further enhanced by incorporating a spatial-contextual adjustment term, which accounts for the spatial distribution of road assets, and a probabilistic refinement that captures prediction uncertainty in complex environments, such as varying lighting conditions and cluttered backgrounds. Our rigorous mathematical formulation demonstrates that REG minimizes localization and classification errors by applying adaptive weighting to hard-to-detect instances while down-weighting easier examples. Experimental results show a substantial performance improvement, achieving a mAP50 of 80.34 and an F1-score of 77.87, significantly outperforming conventional methods. This research underscores the capability of advanced loss function refinements to enhance the robustness and accuracy of road asset detection and segmentation, thereby contributing to improved road safety and infrastructure management. For an in-depth discussion of the mathematical background and related methods, please refer to previous work available at \url{https://github.com/kaopanboonyuen/REG}.

Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi et al.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.

Zhen Leng, Zepeng Fan, Pengfei Liu et al.

Concrete pavement is accompanied by two major functional properties, namely noise emission and friction, which are closely related to pavement surface texture. While several technologies have been developed to mitigate tire-pavement noise and improve driving friction by surface texturization, limited information is available to compare the advantages and disadvantages of different surface textures. In this study, a state-of-the-art and state-of-the-practice review is conducted to investigate the noise reduction and friction improvement technologies for concrete pavement surfaces. The commonly used tests for characterizing the surface texture, skid resistance, and noise emission of concrete pavement were first summarized. Then, the texturing methods for both fresh and hardened concrete pavement surfaces were discussed, and the friction, noise emission and durability performances of various surface textures were compared. It is found that the next generation concrete surface (NGCS) texture generally provides the best noise emission performance and excellent friction properties. The exposed aggregate concrete (EAC) and optimized diamond grinding textures are also promising alternatives. Lastly, the technical parameters for the application of both diamond grinding and diamond grinding & grooving textures were recommended based on the authors' research and practical experience in Germany and the US. This study offers a convenient reference to the pavement researchers and engineers who seek to quickly understand relevant knowledge and choose the most appropriate surface textures for concrete pavements.

Dmytro Bespalov, Lev Ivanets, Volodymyr Sistuk

Introduction. Interchange designs that provide specific benefits to road users will always be of interest. It is extremely interesting a type of interchange in the same level named in literary sources as a «hamburger roundabout» or «throughabout». Usually, two separate lanes run through the extended roundabout, crossing each other inside the interchange. Such intersections can be regulated by both road signs and traffic lights, depending on their size and the intensity of incoming traffic flows. Problem Statement. The analysis of the designs of the intersection at one level by the type of throughabout is insufficiently presented both in the normative documentation for the design of highways and in modern scientific periodicals. However, this type of intersection refers to alternative junctions used in road construction practice in European countries. Thus, an urgent task is to evaluate the operational efficiency indicators and surrogate traffic safety indicators of the throughabout, which can later serve as a basis for making decisions about the need to construct this type of intersection. Purpose – to identify the main problems of the interchange by the type of throughabout based on the analysis of traffic efficiency and safety indicators, carried out by the method of traffic microsimulation.

Joanna Żyłkowska

Abstract: Railway lines are a permanent element of wildlife habitats and usually do not have a large impact on animals functioning in their natural environment. Sometimes, however, when an animal finds itself on the tracks while a train passes, they pose a deadly danger. The article presents the causes of wild animal – train collisions resulting from how the senses and psyche of animals function and how they perceive railway lines. Keywords: Railway lines; Environmental impact; Wildlife; Wild animal – train collisions

Minmao Liao, Huaili Peng

A modified deflection theory is developed for preliminary design of self-anchored suspension bridges. The proposed theory modifies the questionable approach of the existing theory considering the initial fabrication camber and overcomes the limitation that the hangers are assumed inextensible, which results in a stiffer bridge system and thus underestimation of the main cable and girder deflections. In addition, in order to avoid the inconvenience of solving a system of nonlinear equations iteratively for the preliminary design, the tower flexural stiffness is neglected rationally to obtain a system of linear equations only. With the aid of all force equilibrium and deformation compatibility conditions for the entire bridge system, the modified deflection theory is formulated. Its solution procedure is presented, which leads to a complicated sixth-order variable-coefficient ordinary differential equation, and a practical approximate solution to the equation is sought. To verify the proposed theory, a bridge example is investigated, and the results are compared to those from the previous deflection theory and complex finite element analysis. The comparisons demonstrate the effectiveness of the modified deflection theory.

Jarosław Kiepura, Dariusz Seliga

Abstract: The key factor in the development of the Świnoujście seaport is gaining a strong position in handling Czech marine transit, which has been dominated for years by German seaports, with Hamburg at the forefront. Therefore, the announced construction of a modern reloading infrastructure, with a container hub capable of receiving and handling large cargo ships, is an obvious asset here - necessary to take up such competition, but far from suffi cient. It is necessary to launch comprehensive measures that would include investments associated with both seaport infrastructure and necessary communication routes that lead to the said port, ensuring its effi cient communication with the land facilities - the main suppliers and recipients of cargo. An important element - although not suffi cient - of these activities is the construction of the S3 expressway, which is nearing completion, but for eff ective competition, it is necessary to create a dedicated railway route with the highest possible parameters adopted for trans- European routes. It should be noted that without said railway artery, it is impossible to eliminate the competitive advantages of German ports that provide effi cient supply chain with the area, especially the western Czech Republic. Until such route is created, the competition will be doomed from the start, and the considerable funds invested in the development of the Świnoujście seaport will remain largely unused. Keywords: Railway connections; Czech marine transit; Transport network

Xiaohan Ma, Wenhao Yu, Chengxiang Zhao et al.

Compliance with traffic laws is a fundamental requirement for human drivers on the road, and autonomous vehicles must adhere to traffic laws as well. However, current autonomous vehicles prioritize safety and collision avoidance primarily in their decision-making and planning, which will lead to misunderstandings and distrust from human drivers and may even result in accidents in mixed traffic flow. Therefore, ensuring the compliance of the autonomous driving decision-making system is essential for ensuring the safety of autonomous driving and promoting the widespread adoption of autonomous driving technology. To this end, the paper proposes a trigger-based layered compliance decision-making framework. This framework utilizes the decision intent at the highest level as a signal to activate an online violation monitor that identifies the type of violation committed by the vehicle. Then, a four-layer architecture for compliance decision-making is employed to generate compliantly trajectories. Using this system, autonomous vehicles can detect and correct potential violations in real-time, thereby enhancing safety and building public confidence in autonomous driving technology. Finally, the proposed method is evaluated on the DJI AD4CHE highway dataset under four typical highway scenarios: speed limit, following distance, overtaking, and lane-changing. The results indicate that the proposed method increases the vehicle's overall compliance rate from 13.85% to 84.46%, while reducing the proportion of active violations to 0%, demonstrating its effectiveness.

Weilong Ding, Tianpu Zhang, Zhe Wang

Inter-city highway transportation is significant for citizens' modern urban life and generates heterogeneous sensory data with spatio-temporal characteristics. As a routine analysis in transportation domain, daily traffic volume estimation faces challenges for highway toll stations including lacking of exploration of correlative spatio-temporal features from a long-term perspective and effective means to deal with data imbalance which always deteriorates the predictive performance. In this paper, a deep spatio-temporal learning method is proposed to predict daily traffic volume in three phases. In feature pre-processing phase, data is normalized elaborately according to latent long-tail distribution. In spatio-temporal learning phase, a hybrid model is employed combining fully convolution network (FCN) and long short-term memory (LSTM), which considers time, space, meteorology, and calendar from heterogeneous data. In decision phase, traffic volumes on a coming day at network-wide toll stations would be achieved effectively, which is especially calibrated for vital few highway stations. Using real-world data from one Chinese provincial highway, extensive experiments show our method has distinct improvement for predictive accuracy than various traditional models, reaching 5.269 and 0.997 in MPAE and R-squre metrics, respectively.

Vinit Katariya, Ghazal Alinezhad Noghre, Armin Danesh Pazho et al.

Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can be valuable for various traffic safety and management applications. Despite the abundance of trajectory datasets, few offer a comprehensive and diverse range of driving scenes, capturing multiple viewpoints of various highway layouts, merging lanes, and configurations. This limits their ability to capture the nuanced interactions between drivers, vehicles, and the roadway infrastructure. We introduce the \emph{Carolinas Highway Dataset (CHD\footnote{\emph{CHD} available at: \url{https://github.com/TeCSAR-UNCC/Carolinas\_Dataset}})}, a vehicle trajectory, detection, and tracking dataset. \emph{CHD} is a collection of 1.6 million frames captured in highway-based videos from eye-level and high-angle POVs at eight locations across Carolinas with 338,000 vehicle trajectories. The locations, timing of recordings, and camera angles were carefully selected to capture various road geometries, traffic patterns, lighting conditions, and driving behaviors. We also present \emph{PishguVe}\footnote{\emph{PishguVe} code available at: \url{https://github.com/TeCSAR-UNCC/PishguVe}}, a novel vehicle trajectory prediction architecture that uses attention-based graph isomorphism and convolutional neural networks. The results demonstrate that \emph{PishguVe} outperforms existing algorithms to become the new state-of-the-art (SotA) in bird's-eye, eye-level, and high-angle POV trajectory datasets. Specifically, it achieves a 12.50\% and 10.20\% improvement in ADE and FDE, respectively, over the current SotA on NGSIM dataset. Compared to best-performing models on CHD, \emph{PishguVe} achieves lower ADE and FDE on eye-level data by 14.58\% and 27.38\%, respectively, and improves ADE and FDE on high-angle data by 8.3\% and 6.9\%, respectively.

C. Ezemenike, O. Aderinlewo, I. O. Oladele et al.

Highway engineers and researchers are constantly trying to enhance the performance of asphalt concrete pavement, in order to keep up with the global adequate and sustainable highway infrastructure drive.   Modification of asphalt concrete could improve certain properties of the mixture and reduce cost of road construction materials. In this research, evaluation of Marshal Stability and flow of hybrid asphalt modified concrete was studied, the wastes from various industries such steel industry, plastic industry and coal-fired power plant industries were combined together to form hybrid composite. Steel slag, fly ash, waste plastic, aggregate and bitumen were used to produce the asphalt concrete in a hybrid manner to evaluate effect of stability and flow properties of asphalt concrete mixture. The physical properties of steel slag, fly ash aggregate, bitumen and waste plastic based polypropylene were determined by carrying out tests such as sieve analysis, moisture content, flakiness and elongation, aggregate impact, aggregate crushing, specific gravity, hygrometer, penetration, softening point, flash and fire point, viscosity, ductility and water-in-bitumen test. Bitumen with penetration grade 60/70 was used and the content varied from 5.0 to 7.0 % at 0.5% interval. A cylindrical sample with the composite mixture of asphalt concrete was produced using varying proportions of hybrid composite which comprises of steel slag (SS) and fly ash (FA) at 2%, 4%, 6% and 8% by weight of the asphalt concrete to partially replace the conventional materials (aggregate and filler) and bitumen which was partially replaced with polypropylene (PP) at 5%, 10%, 15% and 20% respectively. The Marshal Stability test conducted on the hybrid composite asphaltic concrete gives optimum stability values of 10.50 kN, 12.50 kN, 12.30 kN and 13.50 kN and optimum flow values of 3.30 mm, 2.80 mm, 2.80 mm, and 2.70 mm at 2%, 4%, 6% and 8% respectively. Comparing with Asphalt Institute specification, the non-conventional material used as fillers, coarse and fine aggregate can be conveniently used in light, medium and heavy traffic.   Based on the findings, the application of these wastes material which constitute higher stability and flow will improve the properties of asphalt concrete, reduce the cost of constituent material and decrease environmental problem that arise from various industries.