Hasil untuk "Transportation engineering"

Rishav Sen, Amutheezan Sivagnanam, Aron Laszka et al.

The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.

H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

Mohammad Adoul Amin, Taoufik Najeh, Naveen Venkatesh Sridharan et al.

Kassem Danach, Hassan Harb, Louai Saker et al.

Disaster logistics presents a highly complex decision-making challenge under conditions of uncertainty, where the timely and efficient allocation of scarce resources is essential to minimize human suffering. In this context, we propose a novel Quantum-Inspired Hyperheuristic Framework (QHHF) designed to solve Dynamic Multi-Objective Combinatorial Optimization Problems (DMOCOPs) arising in disaster relief operations. The proposed framework integrates Quantum-Inspired Evolutionary Algorithms (QIEAs), which facilitate diverse and explorative solution generation, with a Reinforcement Learning (RL)-based hyperheuristic capable of dynamically selecting the most suitable low-level heuristic in response to evolving disaster conditions. A dynamic multi-objective mathematical model is formulated to simultaneously minimize total travel cost and risk exposure, while maximizing priority-weighted demand satisfaction. The model captures real-world complexity through time-dependent variables, stochastic demand variations, and fluctuating transportation risks. Extensive simulations using real-world disaster scenarios demonstrate the effectiveness of the proposed approach in generating high-quality solutions within stringent response time constraints. Comparative evaluations reveal that QHHF consistently outperforms traditional heuristics and metaheuristics in terms of adaptability, scalability, and solution quality across multiple objective trade-offs. Notably, our method achieves a 9.6% reduction in total travel cost, a 6.5% decrease in cumulative risk exposure, and a 4.7% increase in priority-weighted demand satisfaction when benchmarked against existing techniques. This work contributes both to the advancement of hyperheuristic theory and to the development of practical, AI-enabled decision-support tools for emergency logistics management.

Wei Liu, Haonan Liu, Qian Xu et al.

Abstract In urban rail flexible traction power supply system (FTPSS), conventional energy-saving strategies for reversible converter (RC) predominantly rely on offline optimization with fixed parameters. However, inherent uncertainties in train operations, such as timetable deviations and stochastic load fluctuations, result in energy consumption volatility, rendering traditional approaches suboptimal. To address this, we propose a multi-timescale model predictive control (MPC) framework that integrates day-ahead scheduling and intraday rolling optimization. Second, we propose a novel data processing method for neural network training in the intraday to construct a neural network-based load prediction model, which is used as the model prediction control (MPC) input for rolling optimization. Validated on Qingdao Metro Line 11 datasets, the prediction model achieves a correlation coefficient (R 2) value of 95.2%, and the mean squared error (MSE) is 0.078, outperforming conventional prediction methods. By integrating MPC-based rolling optimization with day-ahead scheduling, the proposed strategy improves the energy-saving rate by 2.00% over traditional offline optimization methods. Demonstrating robustness against timetable perturbations and load uncertainties.

Dina Koishiyeva, Jeong Won Kang, Teodor Iliev et al.

Class imbalance is a persistent limitation in polyp segmentation, commonly resulting in biased predictions and reduced accuracy in identifying clinically relevant structures. This study systematically evaluated 12 loss functions, including standard, weighted, and compound formulas, applied to colon polyp segmentation using the UNet-VGG16 fixed architecture on the Kvasir-SEG dataset. The encoder was frozen to isolate the effect of loss functions under the same training conditions. A fixed random seed was used in all experiments to ensure reproducibility and control variance during training. The results reveal that the combined loss functions, namely WBCE combined with Dice and Tversky combined with Focal, achieved the top Dice scores of 0.8916 and 0.8917, respectively. Tversky plus Focal also provided the highest sensitivity of 0.8885, and WBCE obtained the best average IoU of 0.8120. Tversky loss showed the lowest error rate of 4.99, indicating stable optimization. These results clarify the influence of loss function selection on segmentation performance in scenarios characterized by considerable class imbalance.

Davide Maffiola, Stefano Longari, Michele Carminati et al.

Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management. Traditionally, road traffic information has been collected using on-site sensors. However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative. State-of-the-art solutions currently follow a centralized model where only the service provider has complete access to the collected traffic data and represent a single point of failure and trust. In this paper, we propose GOLIATH, a blockchain-based decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network's participants. Our approach mitigates the limitations of existing crowd-sourcing centralized solutions by guaranteeing trusted information collection and exchange, fully exploiting the intrinsic distributed nature of vehicles. We demonstrate its feasibility in the context of vehicle positioning and traffic information management. Each vehicle participating in the decentralized network shares its position and neighbors' ones in the form of a transaction recorded on the ledger, which uses a novel consensus mechanism to validate it. We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication. We evaluate the proposed framework in a simulated (but realistic) environment, which considers different threats and allows showing its robustness and safety properties.

Baogui Zhou, Huabin Zhong, Kaipeng Yang et al.

Based on a real engineering case, this study employs the MIDAS finite element software to model the reinforced high embankment slope using anti-sliding piles. The accuracy of the finite element method is verified by comparing calculated outcomes with field monitoring data. Expanding on this foundation, an analysis of factors influencing the reinforced high embankment slope is undertaken to scrutinize the impact of diverse elements on the slope and ascertain the optimal reinforcement strategy. The results reveal the following: The principal displacement observed in the high embankment slope is a vertical settlement, which escalates with the backfill height. Notably, the highest settlement does not manifest at the summit of the initial slope; instead, it emerges close to the summits of the subsequent two slopes. However, the maximum horizontal displacement at the slope’s zenith diminishes as the fill height increases—a trend that aligns with both field observations and finite element computations. The examination of the influence of anti-sliding pile reinforcement on the high embankment slope unveils that factors like the length, diameter, spacing, and positioning of the anti-sliding piles exert minor impacts on vertical settlement, while variations in the parameters of the anti-sliding piles significantly affect the slope’s horizontal displacement. When using anti-sliding piles to reinforce multi-level high embankment slopes, factoring in the extent of horizontal displacement variation and potential cost savings, the optimal parameters for the anti-sliding piles are a length of 15 m, a diameter of 1.5 m, and a spacing of 2.5 m, presenting the most effective combination to ensure superior slope stability and support.

Hardik Gajera, Srinivas S. Pulugurtha, Sonu Mathew et al.

Sahima Nazneen, Ahmed Farid, Khaled Ksaibati

XU Pei-jun, LI Hong-fu, LIAO Zhen, LIU Le-le, CHENG Meng-meng, TAO Wen-jin, LI Yuan-yuan, WANG Chen, LV Xiang-hong

On the basis of the investigation on the operating conditions of the pipeline and the operating environment of the cathodic protection system，the typical pipelines and test points were selected according to the principles of universality，representativeness and pertinence，and the corrosion rate and cathodic protection degree of the pipeline were measured by the on-site coupon hanging to evaluate the cathodic protection effect.Results showed that the corrosion rate of the pipeline was significantly lower than 0.01 mm/a and the protection degree was above 85%after applying cathodic protection in accordance with the minimum protection potential and cathode potential negative offset criteria specified in GB/T 21448-2017.The overall cathodic protection effect was good.For pipelines with high resistivity to soil，100 mV cathode potential negative offset criterion was recommended.In the actual operation process of pipeline cathodic protection，soil microorganism amount and types，stray current and the maximum protection potential needed to be heavily monitored for timely adjustment of the cathodic protection potential of the pipeline for the purpose of avoiding relatively serious uniform corrosion，local corrosion，hydrogen damage and unnecessary waste of resources.

Liyang Xiao, Jialiang Zhang, Chenghao Wang et al.

China has taken a number of positive measures to meet the requirement of environmental protection. The switch to electricity especially in transport sector is considered as a promising way to reducing greenhouse gas (GHG) emissions and facilitating to meet China's carbon neutral target in 2060. Besides, because of the overall impact of the COVID-19 on the transport sector, the future measures imposed by the government on electric vehicles (EVs) remain in high uncertainty. Taking the characteristics of different vehicles, business models, uncertainty of government financial subsidies and environmental factors into consideration, a replacement optimization model for a taxi fleet is proposed in this study under the cap-and-trade system. We assume that the taxi company has four types of vehicles to purchase or lease and manage to maximum the pecuniary advantages and environmental benefits simultaneously. Experimental results analyze that EVs and battery-swap electric vehicles (BSVs) are highly competitive when government subsidies do not decline. During the early stage of the planning horizon, adjusting the fleet continuously and timely can help the company to realizing the maximum revenue.

Qidong Han, Shuo Feng, Xing Wu et al.

Path planning in complex environments remains a challenging task for unmanned vehicles. In this paper, we propose a decoupled path-planning algorithm with the help of a deep reinforcement learning algorithm that separates the evaluation of paths from the planning algorithm to facilitate unmanned vehicles in real-time consideration of environmental factors. We use a 3D surface map to represent the path cost, where the elevation information represents the integrated cost. The peaks function simulates the path cost, which is processed and used as the algorithm’s input. Furthermore, we improved the double deep Q-learning algorithm (DDQL), called retrospective-double DDQL (R-DDQL), to improve the algorithm’s performance. R-DDQL utilizes global information and incorporates a retrospective mechanism that employs fuzzy logic to evaluate the quality of selected actions and identify better states for inclusion in the memory. Our simulation studies show that the proposed R-DDQL algorithm has better training speed and stability compared to the deep Q-learning algorithm and double deep Q-learning algorithm. We demonstrate the effectiveness of the R-DDQL algorithm under both static and dynamic tasks.

Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza et al.

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.

Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real et al.

Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token \textit{dataset} to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.

Neil A. Ernst, Maria Teresa Baldassarre

Registered reports are scientific publications which begin the publication process by first having the detailed research protocol, including key research questions, reviewed and approved by peers. Subsequent analysis and results are published with minimal additional review, even if there was no clear support for the underlying hypothesis, as long as the approved protocol is followed. Registered reports can prevent several questionable research practices and give early feedback on research designs. In software engineering research, registered reports were first introduced in the International Conference on Mining Software Repositories (MSR) in 2020. They are now established in three conferences and two pre-eminent journals, including Empirical Software Engineering. We explain the motivation for registered reports, outline the way they have been implemented in software engineering, and outline some ongoing challenges for addressing high quality software engineering research.

Kashin Sugishita, Kazuki Arisawa, Shinya Hanaoka

We experience air traffic delays every day, but are there any recurrent patterns in these delays? In this study, we investigate the recurrence of delay propagation patterns in Japan's domestic air transport network in 2019 by integrating delay causality networks and temporal network analysis. Additionally, we examine characteristics unique to delay propagation by comparing delay causality networks with corresponding randomized networks generated by a directed configuration model. As a result, we found that the structure of the delay propagation patterns can be classified into several groups. The identified groups exhibit statistically significant differences in total delay time and average out-degree, with different airports playing central roles in spreading delays. The results also suggest that some delay propagation patterns are particularly prominent during specific times of the year, which could be influenced by Japan's seasonal and geographical factors. Moreover, we discovered that specific network motifs appear significantly more (or less) frequently in delay causality networks than their corresponding randomized counterparts. This characteristic is particularly pronounced in groups with more significant delays. These results suggest that delays propagate following specific directional patterns, which could significantly contribute to predicting air traffic delays. We expect the present study to trigger further research on recurrent and non-recurrent natures of air traffic delay propagation.

Oliver Nett, Alexander Salomon

Arash Rasaizadi, Seyedehsan Seyedabrishami

By predicting and informing the future of traffic through intelligent transportation systems, there is more readiness to avoid traffic congestion. In this study, an ensemble learning process is proposed to predict the hourly traffic flow. First, three base models, including K-nearest neighbors, random forest, and recurrent neural network, are trained. Predictions of base models are given to the XGBoost stacking model and bagged average to determine the final prediction. Two groups of models predict traffic flow of short-term and mid-term future. In mid-term models, predictor features are cyclical temporal features, holidays, and weather conditions. In short-term models, in addition to the mentioned features, the observed traffic flow in the past 3 to 8 hours has been used. The results show that for both short-term and mid-term models, the least prediction error is obtained by the XGBoost model. In mid-term models, the root mean square error of the XGBoost for the Saveh to Tehran direction and Tehran to Saveh direction is 521 and 607 (veh/hr), respectively. For short-term models, these values are decreased to 453 and 386 (veh/hr). This model also brings less prediction error for predicting the first and fourth quartiles of the observed traffic flow as rare events.

Linhao Li, G. Long, C. Bai et al.

As a massive solid waste, the high value-added utilization of coal gangue has received more and more attention in China. This study focuses on the utilization of coal gangue aggregate in railway engineering for coal transportation passage. Coal gangue aggregate was employed as high-grade railway subgrade filler andprepared concrete for roadbed drainage (named coal gangue roadbed protecting concrete—CGRPC). First, the basic properties of coal gangue such as particle size distribution, ignition loss, strength change under water softening, and compression performance were measured. Then, the technology to use coal gangue as filler in railway subgrade was put forward based on a real engineering application with the Jingang coal-carrying railway special line. Field tests showed that the coal gangue roadbed had excellent performance. The dynamic stiffness expressed as K30 was more 130 MPa/m, which meets the requirement for high-speed railway roadbeds. The distribution of vertical earth pressure according to the backfill depth showed a linear growing tendency. Finally, the technical and economic benefits of using coal gangue railway roadbeds were analyzed. The application of coal gangue near the railway line not only solved the problem of aggregate shortage in engineering construction, but it also consumes the coal gangue waste and leads to huge social benefits.