Abstract: With the expansion of offshore oil and gas exploration into deep-water regions, the efficient scheduling of Platform Supply Vessels (PSVs) is critical to offshore operations. The Platform Supply Vessel Routing and Scheduling Problem (PSVRSP) is an NP-hard combinatorial optimization problem, which is further complicated by uncertainty in offshore demand. Existing studies reveal a methodological gap: heuristic approaches cannot guarantee optimality, while exact algorithms often ignore demand uncertainty. To address this gap, this study proposes a Branch-and-Price (B&P) method for the Platform Supply Vessel Routing and Scheduling Problem with Uncertain Demand (PSVRSP-UD). A scenario-based Mixed-Integer Linear Programming (MILP) model is formulated, in which demand uncertainty is captured using Latin Hypercube Sampling (LHS) combined with Cholesky Decomposition and Sample-Based Reduction (SBR). Based on Dantzig–Wolfe Decomposition, the proposed B&P algorithm integrates NG-Route labeling and a two-level branching strategy to achieve global optimization. Computational experiments show that the B&P algorithm outperforms CPLEX in both computational efficiency and solution quality. Sensitivity analyses examine the impacts of scenario number, demand fluctuation, and weight coefficients on the results. The new results in this study can provide a practical decision-support tool for offshore logistics operations.

Abstract: Concept-phase planning of diesel-engined hybrid vehicles requires rapid engine synthesis, including brake specific fuel consumption (BSFC) estimation, with minimal input data. Fuel savings from hybridization arise partly through engine downsizing and engine-off operation, so trade studies depend on knowing the dependence of BSFC on engine sizing and speed and load conditions. This paper presents a method for synthesizing hypothetical modern diesel engines of any given size for the purpose of trade studies, while matching the performance and efficiency capabilities of commercially available units. Relationships are developed between rated power, rated speed, peak torque, displacement and cylinder count for four vehicle application classes. Together with a BSFC estimation method, these relationships form a complete engine synthesis chain from rated power to a full torque curve and BSFC map, with provision for substituting known data, including minimum BSFC, where available. The method supports continuous scaling.

Abstract: As cities worldwide face challenges of rapid urbanization and declining public transit ridership, traditional fixed-route systems often fail to meet evolving mobility needs. Urban planning issues, such as suburban sprawl and fragmented land use, exacerbate these limitations, leading to underutilized services, higher operational costs, and accessibility gaps, particularly for underserved communities. Demand-Responsive Transit (DRT) systems have emerged as an effective solution, offering flexible, on-demand services that dynamically adjust routes based on user demand. This review synthesizes insights from 65 studies, including 20 real-world implementations, examining DRT's potential to enhance accessibility, cost efficiency, and environmental sustainability. Key findings demonstrate that DRT systems reduce operational costs by 25-35% while increasing ridership up to 300%. Integration of AI-driven routing algorithms improves service reliability by 90-98% and reduces travel times by 35-50%. Multiple booking interfaces increase adoption by 40-60%, while multimodal integration expands service coverage by 100-150%. However, significant barriers persist, with 58% of DRT system models requiring subsidies and 51% facing equity challenges. The study proposes hybrid funding models, integrated multimodal platforms, and inclusive design approaches to address these challenges. By aligning with urban design principles and leveraging advanced technologies, DRT systems can enhance urban resilience while promoting sustainable development.

Abstract: The rapid evolution of urban environments and the growing demand for efficient transportation systems have accelerated the transition toward smart cities. In this context, traffic modeling and urban mobility analysis play a critical role in understanding, predicting, and optimizing complex transportation dynamics. This study explores contemporary approaches to traffic modeling, integrating data-driven methodologies, simulation techniques, and intelligent transportation systems to enhance urban mobility in Petrosani city from Romania. Emphasis is placed on the use of big data, Internet of Things (IoT) technologies, and machine learning algorithms for real-time traffic monitoring, demand forecasting, and adaptive traffic management. The paper examines the interaction between traditional modeling frameworks and emerging smart city infrastructures, highlighting how advanced analytics can improve congestion mitigation, reduce environmental impact, and support sustainable mobility solutions. Furthermore, it discusses multimodal transportation integration, user behavior analysis, and policy implications for urban planners and decision-makers. A conceptual framework is proposed to bridge the gap between theoretical models and practical implementations within smart city ecosystems. The findings suggest that the convergence of digital technologies and traffic modeling significantly enhances the resilience, efficiency, and sustainability of urban mobility systems. The study contributes to the ongoing discourse by identifying key challenges, opportunities, and future research directions in the development of intelligent, data-driven transportation networks.

Abstract: Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-time Responsive (CTR) scheme by selecting the phase with the highest cumulative travel time (CTT) first and then identifying the compatible phase combination with the greatest group CTT, allowing more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion aware mechanism, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. By comparing with other baselines, our simulation results show that under moderate-to-heavy traffic conditions, CATS reduces mean end-to-end travel time by up to 23.72% and improves throughput by up to 93.19% over the baselines, confirming that the co-design of navigation and signal control produces complementary benefits.

Abstract: Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer, and 100% effectiveness in sending alerts. It is concluded that the proposed system is a viable and reliable technological solution to mitigate the "forgetfulness factor" among private drivers, improving road safety and vehicle lifespan.

Abstract: Curbside saturation in dense commercial corridors compromises the sustainability of last mile logistics. This study investigates the impact of "authorized but non functional occupancy" (Class S), referring to service and tradespeople vehicles, on the operational capacity of loading and unloading zones (LUZ). Based on direct field observations of 474 real vehicle entries in a zone in Zaragoza (Spain), an Erlang B no wait queuing model (M/M/1/1) using weighted occupancy time was applied to contrast current saturation levels with a regulated functional scenario. The results demonstrate that the infrastructure is structurally sufficient: removing inefficient uses reduces traffic intensity from 1.31 to 0.48 Erlangs, increasing service potential by 121.84%. Class S was identified as consuming 36.62% of the lost capacity, exceeding the impact of unauthorized private cars. Total Hidden Carbon Emissions (HCE) amounted to 45.34 kg CO₂, establishing an environmental impact of 0.066 kg CO₂ per misused linear meter. The study concludes that proper utilization of loading zones is sufficient to accommodate logistics demand and effectively reduce CO₂ emissions.

Abstract: Pedestrian-involved road traffic accidents represent a serious challenge for traffic safety and require a comprehensive analysis of the interactions within the driver–vehicle–road–environment system. The objective of this study is to develop a methodology for risk assessment in road traffic accidents involving pedestrians based on the analysis of real court cases and dynamic modeling of vehicle motion. A database of 105 court cases was analyzed, enabling the identification of the main factors influencing the occurrence of pedestrian-related accidents. Based on this analysis, a system of 31 linguistic variables was developed to characterize driver behavior, vehicle technical characteristics, and road environment conditions. These variables were integrated into a mathematical model for quantitative risk assessment that enables the evaluation of the relative influence of different groups of factors on accident probability. In addition, a dynamic model of vehicle motion was developed to analyze the influence of driver reaction time, vehicle speed, and road surface conditions on the possibility of avoiding a collision. The results of the numerical analysis demonstrate that even minimal delays in hazard perception and driver reaction significantly increase the probability of pedestrian-related accidents. These findings highlight the importance of early hazard detection and automated emergency braking systems. The proposed methodology provides a framework for integrating intelligent driver assistance systems and automated braking control aimed at improving the safety of vulnerable road users.

Abstract: The main objective of the study is to reduce the dwell time of wagons at stations and to improve the efficiency of shunting locomotive utilization. The problem has a combinatorial nature, since an increase in the number of loading and unloading fronts leads to a sharp growth in the number of feasible service variants. During the research, a mathematical model describing the servicing process of industrial sidings was developed. The study addressed the problem of determining the optimal sequence of wagon deliveries and the optimal distribution of workload among shunting locomotives. Under conditions where two or more shunting locomotives are used, an optimization method based on the indicator of wagon-hours reduction (σ) was proposed for allocating loading and unloading fronts. Using combinatorial properties, it was shown that many possible allocation variants are symmetric, which allowed the development of a mathematical solution that simplifies the search for an optimal solution. Computational results demonstrated that, at the hypothetical railway station “N-1”, applying the optimal service sequence reduces wagon dwell time by 21% compared with an arbitrary sequence. At the hypothetical station “N-2”, distributing wagon groups between two shunting locomotives improves the efficiency of the servicing process by 26% compared with using a single locomotive. Based on the proposed mathematical model and algorithm, a practical software tool was developed that enables the automatic determination of service sequences for loading and unloading fronts. The software allows the identification of optimal servicing orders, analysis of alternative variants, and evaluation of the efficiency of shunting locomotive utilization.

Abstract: The CO₂-emissions are rapidly rising with new records and the transport sector is considerably contributing to GHG emissions. The critical transition towards electrification and sustainable development demands a radical change in the transport industry. One of many solutions is to analyze the environmental benefits of optimized vehicle production and recycling of the vehicle components after its usable life to reduce dependency on limited raw materials. Electric motor is one of the most crucial powertrain components, yet studies on the overall ecological profile of production and end of its useable life is limited. This study examines the life cycle assessment (LCA) impacts of electric motors used in passenger cars and potential recycling of its materials. The analysis covers production and recycling of components, crucial elements, and permanent magnets. The results show that housing and rotor production have the highest impacts mainly due to steel, aluminum and permanent magnets. The findings discuss e-motor recycling innovations, state-of-the-art methods and emission reduction potentials of recycling. This paper also covers the understanding that a significant transformation to optimize the resource consumption in manufacturing of crucial vehicle powertrain component and reduce waste after end-of-life could bring combined ecological advantages.

Abstract: Detecting small objects in drone imagery remains challenging because of extreme object scale variations, dense scenes, and limited pixel information. Although recent YOLOv8 variants provide multiple model scales and architectural options, systematic guidance on their practical use in UAV-based detection remains limited. Accordingly, this study conducted a comprehensive empirical evaluation of the complete YOLOv8 family on the VisDrone dataset to assess the effects of the model capacity, input resolution, and architectural modifications on the small-object detection performance. The results showed that increasing the model capacity exhibited diminishing returns: YOLOv8l achieved the best overall accuracy (15.9% mAP50), while the larger YOLOv8x model exhibited a substantial performance degradation (7.32% mAP50) owing to training instability under data-constrained conditions. Scaling the input resolution from 640 to 1280 yielded a 25% improvement in the detection performance, substantially exceeding the gains obtained through architectural modifications, such as adding a P2 detection layer (+6%). The optimal configuration (YOLOv8l @ 1280) achieved a 488% improvement compared to the YOLOv5 baseline. These findings demonstrate that, for UAV-based small-object detection, prioritizing an appropriate model capacity and input resolution is more effective than increasing the architectural complexity.

Abstract: Highway–rail grade crossing (HRGC) safety research relies on federal incident and inventory datasets that span multiple decades. However, inconsistencies in geographic identifiers and incomplete reconstruction of crossing denominators can distort exposure-based rate metrics. This study develops, documents, and validates a reproducible nine-stage reconciliation pipeline applied to 51 years (1975–2025) of national HRGC incident data from the Federal Railroad Administration Form 57 and Form 71 datasets. The hierarchical pipeline integrated deterministic alignment and AI-assisted inference to produce an audited, geographically consistent dataset. The study formalizes four longitudinal county-level exposure metrics that quantify spatiotemporal risk. These metrics include accumulated incidents per million population (AIPM), accumulated incidents per crossing (AIPC), crossings per million population (CPM), and crossings per 100 square miles (CPHSM). All four metrics exhibited pronounced right-skewness: AIPM, CPM, and CPHSM approximated exponential forms, and AIPC approximated a log-normal form. Anderson–Darling tests detected statistically significant tail deviations in three metrics; CPM did not reject the exponential fit at conventional significance levels. Spatial analysis shows coherent regional concentration in incident rates in the Central Plains and lower Mississippi corridors. The national time series exhibits a late-1970s plateau, sustained exponential decline beginning around 1980, and stabilization but persistent incident rates after 2001. Population-normalized AIPM remained statistically indistinguishable between the reconciled and record-dropped datasets; however, crossing-based metrics changed materially when reconstructing denominators from the reconciled crossing universe. Median ratio comparisons confirmed that incident-only denominators introduced substantial measurement bias in local risk assessment. State-level rank reversals persisted even when omnibus distributional tests failed to reject equality. By formalizing multistage data cleaning and quantifying its analytical impact over an unprecedented longitudinal horizon, this study establishes denominator integrity and geographic reconciliation as prerequisites for valid HRGC exposure assessment and provides a replicable platform for future predictive modeling.

Abstract: The city of Baghdad is witnessing a continuous increase in traffic and urbanization, which has led to frequent traffic jams and deterioration of the urban environment and quality of life due to pollution and the waste of time and energy. Hence, it has become necessary to adopt integrated planning concepts that regulate land uses, promote transport efficiency, and support sustainable urban development. This research aims to investigate the concept of transport-oriented development (TOD) and explore its applicability in the city of Baghdad, focusing on identifying obstacles and challenges that may face the implementation of this concept in the local context, whether related to transport infrastructure, urban planning, or community participation, to provide an analytical framework that can be relied upon in the development of effective strategies to promote sustainable transport and integrated urban development. The BRT bus rapid transit system is an essential part of the Comprehensive Development Plan for Baghdad 2030, aiming to improve mass transit and reduce congestion on major streets such as Palestine Street by providing fast, efficient transportation that connects the city's neighborhoods and encourages walking and the use of sustainable transport. The project supports sustainable urban development by integrating the principles of TOD, increasing residential and commercial density around the stations, and adopting an integrative methodology that analyzes the relationships among transport, land uses, and urban density to provide a scientific framework to support planning and future decision-making.

Abstract: This article examines the impact of intermodal wagon technical specifica-tions and railway infrastructure parameters on electricity consumption in rail freight transport. To conduct this investigation, a three-stage analytical model was developed. The first stage establishes core assumptions, encompassing train lengths, rolling stock types, container configurations, infrastructure constraints, and the characteristics of the energy-consumption model. The second stage identifies technical constraints of specific wagons, determines representative train compositions, and executes loading simulations. The third stage focuses on evaluating energy efficiency across diverse loading scenarios. The case study demonstrates that specific energy consumption varies significantly with wagon type, train mass, and route characteristics, challenging the use of static energy-consumption values prevalent in current literature. Results indicate that 40-foot wagons incur high energy penalties due to their tare weight and axle count, despite maximizing loading capacity. While 60-foot wagons consume less energy, they result in a high frequency of empty slots under a 20 t/axle limit. Conversely, 80-foot wagons emerge as the most energy-efficient, particularly at a 22.5 t/axle limit. Mixed consists offer a balance of operational flexibility and competitive performance. Inter-estingly, extending train length from 600 m to 730 m increases volume but does not inherently reduce unit energy consumption. These findings underscore the necessity of aligning wagon fleet selection with infrastructure capabilities and cargo characteris-tics. Ultimately, this study provides actionable recommendations for planning ener-gy-efficient intermodal operations.

Abstract: A vertiport is a key supporting infrastructure for providing takeoff and landing facilities for aircraft within the rapidly growing Advanced Air Mobility (AAM) ecosystem, particularly in urban areas or Urban Air Mobility (UAM) which faces significant challenges such as dense urban obstacles and limited available space, urging improvement in the vertiport space usage. The configuration variation on a two-pad vertiport is modeled and simulated using AnyLogic Discrete Event Simulation (DES), varying number of stands, operational concepts, and aircraft turnaround times, while considering Linear and Satellite topology to analyze their influence on the vertiport output capacity. The results indicate that each configuration responds differently with turnaround time variation, showing delays and capacity reduction for shorter time. Increasing number of stands provides a significant capacity benefit for longer turnaround times across all simulated configurations, with capacity gains ranging from 100–120%. Overall, the linear–independent vertiport achieves the highest total capacity of 163 and 229 aircraft for the 4 and 6 stand variants, respectively, while the satellite vertiport attains the highest total capacity of 266 and 299 aircraft for the 8 and 10 stand variants. However, considering urban space usage, the satellite vertiport benefits in lower stands number while linear vertiport benefits in higher stands number.

Abstract: Self-awareness is the result of logical relationships between mathematics and language. Language the brain's neurons are numbers and the logical relationships between them. The connection between cognitive phenomena such as self-awareness and language lies within algebra and mathematics. Numbers are an independent language with algebraic laws independent of time. Based on this, the arithmetic sequences of natural numbers are placed on separate angles. These angles constitute manifolds of digital root that exist within a compact polar coordinate system and are classified into one group in terms of digital root. This mathematical model can instantly decrypt and compress information. This mathematical model can pave the way for simulating artificial self-awareness.

Abstract: The integration of truck platooning and electrification presents a promising avenue for improving operational efficiency and environmental sustainability in freight transportation. Realizing the energy and cost saving as well as emission reduction benefits requires a holistic design of truck routing, scheduling, and platooning strategies that account for practical operational constraints. This study investigates the integrated planning problem of routing, scheduling, and platooning for a mixed fleet of conventional trucks (CTs) and electric trucks (ETs), referred to as mixed fleet truck platooning (MFTP) problem. The MFTP incorporates charging scheduling and key operational factors, such as platooning leader-follower positioning under the battery constraints of ETs and charging station availability and capacity, and the positional configuration of trucks within a platoon. The objective is to minimize the total operation cost of the MFTP system, including charging cost, fuel cost, travel labor cost, charging labor cost, and platoon formation labor cost, while ensuring timely arrivals across multiple origin–destination (OD) pairs. The proposed MFTP is formulated as a novel mixed-integer linear program (MILP). Extensive numerical experiments on the Illinois highway network are conducted to examine the effectiveness and efficiency of the proposed model. The findings shed light on planning mixed fleets of CTs and ETs with platooning, offering valuable managerial insights for decision-makers.

Abstract: The COVID-19 pandemic and the resulting mobility restrictions significantly disrupted urban traffic patterns. This study quantitatively assesses the impact of these restrictions on vehicle flow at a signalized central intersection in Cluj-Napoca, Romania, through an integrated methodology combining continuous radar-based traffic measurements and AI-assisted video analysis. Traffic data were collected before the pandemic (November 2019) and during the lockdown period (April 2020), enabling a comparative evaluation of flow characteristics and vehicle arrival patterns. Under constrained observational conditions, vehicle arrivals were modeled using a probabilistic framework grounded in the Poisson distribution. The findings indicate a dramatic contraction of mobility demand, with traffic volumes declining in 2020 to 9.55% of pre-pandemic levels. The probabilistic assessment highlights the predominance of free-flow regimes under reduced demand and confirms the adequacy of the Poisson model in low-density traffic scenarios. The proposed framework is transferable to other urban contexts and supports resilience-oriented, data-driven traffic management under extreme mobility disruptions.

Abstract: Adaptive Traffic Signal Control (ATSC) remains a critical challenge for urban mobility. In this direction, Deep Reinforcement Learning (DRL) has been widely investigated for ATSC, showing promising improvements in simulated environments. However, a noticeable gap remains between simulation-based results and practical implementations, due to reward formulations that do not address phase instability. Stochastic variations may trigger premature phase changes ("flickers”), affecting signal behavior and potentially limiting deployment in real scenarios. Although several works have examined delay, queues, and decentralized coordination, stability-focused variables remain comparatively less explored, particularly in single yet complex intersections. This study proposes a decentralized DRL model for ATSC with Noise Injection (ATSC-DRLNI) applied to a single intersection, introducing a stability-oriented reward function that integrates flickers, queue length, and Advantage Actor-Critic (A2C) learning feedback. The model is evaluated in Simulation of Urban MObility (SUMO) platform and compared against seven baseline methods, using real traffic data from a Mexican city for calibration and validation. Results suggest that penalizing flickers may contribute to more stable phase transitions, while reductions of up to 40\% in queue length were observed in heavy-traffic scenarios. These findings indicate that incorporating stability-related variables into reward functions may help bridge the gap between DRL-based ATSC studies.

Abstract: Extensive research has been conducted to develop technologies that enable paratransit systems to operate autonomously, including advanced sensing technologies and associated software. However, there remains a significant gap in research addressing the development of adaptive operational algorithms for such systems in urban environments. Autonomous Shuttles (AS) represent an emerging technology that has gained attention from industry, government, and academia as a novel public transit solution. AS hold the potential to enable Ride-shared Autonomous Mobility on Demand (RAMoD), which can improve accessibility and service equity in transportation-disadvantaged populations across urban and surrounding regions. To address this gap, this study applies an imitation-learning-assisted Deep Reinforcement Learning (DRL) approach to develop a routing method for AS under stochastic and dynamic passenger demand conditions. The proposed framework integrates Generative Adversarial Imitation Learning with Proximal Policy Optimization to enable real-time pickup and drop-off decision-making without centralized re-optimization. The DRL agent was trained over approximately 1.5 million training steps and evaluated across twenty episodes with stochastic passenger generation. Its performance was benchmarked against a deterministic Dial-a-Ride Problem (DARP) solver implemented using Google’s OR-Tools, which employs a Cheapest Insertion heuristic with Local Search refinement. Comparative analysis showed median percentage differences of 37%, –6%, 20%, and 44% in passenger wait time, in-vehicle time, total service time, and episode completion time relative to the DARP baseline. The OR-Tools implementation was selected as a benchmark due to the lack of established step-wise evaluation methods for dynamic routing optimization in simulation environments. These findings demonstrate the potential of learning-based routing policies to support scalable, demand-responsive autonomous mobility services and future smart urban transportation systems.