This study examines the relationship between public transport provision and urban equity through a comparative spatial analysis of bus rapid transit (BRT) systems in two Indonesian coastal cities: Jakarta and Semarang. The novelty of this study lies in its comparative spatial analysis of BRT accessibility between a metropolitan and an intermediate coastal city, highlighting how contrasting urban morphologies shape spatial equity outcomes in developing coastal contexts. Using a geographic information system based approach, the research evaluates residential coverage within a 500-m walking buffer as a proxy for accessibility equity. The findings reveal that while Jakarta’s extensive BRT corridors coexist with urban sprawl and severe congestion, Semarang’s more compact urban form enables higher spatial inclusion of its residents. These results highlight how differing urban structures and governance capacities shape patterns of transport accessibility. However, as equity is assessed primarily through spatial accessibility, the findings should be interpreted as a spatial approximation rather than a comprehensive measure of transport equity. To address identified disparities, this study proposes targeted mitigation strategies, including the integration of feeder and microtransit services to improve first–last mile connectivity in Jakarta’s peripheral areas, as well as the application of transit-oriented development (TOD) and land-use control measures to promote compact growth around BRT corridors. In Semarang, strategies focus on strengthening intermodal integration and preventing future urban sprawl to maintain high coverage efficiency. Despite limitations in temporal and socio-economic data, this research provides important insights for advancing more equitable and resilient transport planning in rapidly growing coastal cities.
. This paper presents a systems dynamics model for analyzing interactions in the Gdansk-Malaszewicze intermodal corridor, which is crucial for the Trans-European Transport Network and the New Silk Road in Poland. The model considers demand variability, capacity constraints in Malaszewicze, and the interdependencies among port operations, rail, and road transport. Traditional methods often fail to account for non-linearities, delays, and feedback. Vensim (R) software was utilized to construct a simulation model to define the system's boundaries from the arrival of containers at the Port of Gdansk to their departure from the Malaszewicze area. The model comprises the following subsystems: port operations, rail transport, terminal operations in Malaszewicze, road transport, and demand and policy. Key variables and feedback loops were identified, including the congestion and modal attractiveness loops. The model validation strategy relies on available historical data and simulation scenarios that address changes in NJS volumes, infrastructure upgrades, or pricing policies. Key performance indicators include transit time, unit cost, and reliability. The model aims to enhance understanding of corridor dynamics, assess potential changes, and support corridor development decisions.
This manuscript introduces a multi-criteria decision-making (MCDM) methodology to address the complexities of the multi-depot heterogeneous vehicle routing problem with time windows (MDHVRPTW), incorporating pickups and deliveries, and validates it through a Fast-Moving Consumer Goods (FMCG) industry case study. The primary scientific achievement is the development and validation of a four-stage VRP algorithm that comprehensively models and solves real-world operational challenges. These include managing split customer orders (FTL/LTL), intricate vehicle capacity and axle load balancing (considering unloading sequences), route duration constraints (via tachograph data), precise delivery time windows, and an integrated multi-depot network structure. The methodology effectively minimizes routes/vehicles and maximizes route efficiency (e.g., km/ton), while strategically managing vehicle relocations. The findings demonstrate that the proposed approach is an effective decision support tool for VRPs with multiple, conflicting criteria. Furthermore, the research establishes a coherent set of evaluation criteria that are applicable across diverse problem instances, enabling robust solution comparison and fostering an objective basis for complex route planning.
Road accidents remain a major public safety concern in Algeria, with 26,272 accidents, 3,740 deaths, and 35,556 injuries recorded in 2024. Over 90% of these accidents are caused by human error, primarily due to behaviors such as the use of mobile phones while driving and failure to wear seat belts. In response to this critical issue, this paper introduces an advanced detection system to enhance road safety through real-time monitoring of mobile phone usage and seat belt compliance among vehicle occupants. The proposed system uses the state-of-the-art YOLOv11 architecture and consists of three interconnected components: seat belt detection, mobile phone usage detection, and windshield detection. YOLOv11 incorporates key innovations, including C2PSA blocks that enhance spatial awareness and C3k2 modules with smaller kernel sizes, enabling better identification of small and partially occluded objects. The system was developed using a dataset from Roboflow comprising 1,116 images, which was expanded to 2,139 images through augmentation techniques such as flipping, brightness adjustment, and rotation. Training was conducted in Google Colab's graphics processing unit environment with 100 epochs and a batch size of 16. Performance evaluation shows the system’s reliability, achieving a mean average precision of 94.2%, with detection accuracies of 96.5% for seat belts, 99.5% for windshields, and 86.5% for mobile phone usage. These results indicate that the proposed system is a promising tool for monitoring driver behavior and supporting the enforcement of safety regulations, thereby contributing to efforts to reduce accident rates in Algeria.
With the increasing expectation for high-quality medical services, intelligent logistics systems have become important means to enhance logistics efficiency in hospitals. Among them, the robotic logistics system, as an emerging technology, plays an increasingly significant role in hospital intelligent logistics. This study takes the robotic logistics systems of several large tertiary hospitals-such as the First Affiliated (Nansha) Hospital of Sun Yatsen University and Foshan Maternal and Child Health Hospital-as cases. From both common and context-specific perspectives, the study proposes key principles and planning procedures for the design of hospital robotic logistics transmission systems. Furthermore, an empirical analysis is conducted at Bazhou People's Hospital. The results indicate that the design essentials of a hospital robotic logistics system include an overall logistics line design characterized by "hierarchical separation, functional concentration, and rapid linkage"; an internal spatial organization emphasizing "zonal coordination, streamlined circulation, and seamless connection"; and supporting facilities construction based on "functional compatibility, system integration, and pre-planned reservation." At Bazhou People's Hospital, the system was pre-planned during the architectural design stage, forming an "internal-external, horizontal-vertical circulation network" for the robotic logistics system. The findings contribute to improving the overall efficiency of hospital robotic logistics systems and provide valuable theoretical guidance and a practical reference for the planning and implementation of similar systems in other healthcare institutions.
This study explores the application of generative design in improving bicycle crank geometry through an iterative workflow. The research utilized Autodesk Fusion 360 to develop a lightweight, structurally efficient crank, considering additive manufacturing via laser powder bed fusion (LPBF), 5-axis CNC milling, and casting as manufacturing routes. Candidate materials included Scalmalloy (an LPBF-optimized Al-Mg-Sc alloy), cast aluminum A356-T6, a titanium alloy Ti-6Al-4V, stainless steel 17-4PH, and fiber-reinforced polymers. Four iterations of a generative design with progressively refined boundary conditions were conducted to evaluate how loads, constraints, and geometric modifications shape the resulting candidates. The results indicate that an LPBF-manufactured Scalmalloy crank provided the most favorable balance between mass and static structural response among the evaluated alternatives, achieving approximately 27% mass reduction relative to the reference design. The findings demonstrate that generative design-framed as an iterative design exploration rather than a formal mathematical optimization-can inform the redesign of bicycle components when boundary conditions are progressively refined and material- process compatibility is respected.
Today, aviation is a leading industry. The market is growing yearly, and the expectations required from supporting aviation sectors have spiked to keep up with the requirements of a dynamic and safety-demanding industry. This paper explores the critical role of aircraft maintenance in ensuring operational accuracy and effective project management within the aviation industry. Besides describing the impact of maintenance activities on efficiency, reliability, and readiness, this paper presents a new way of prioritizing the development of projects according to the structured roadmap; introduces a novel artificial intelligence-based methodology for accurately predicting the real tasks’ man-hours depending on the archived previous data; and shows a model development for capacity and resource planning and scheduling for maintenance activities. A robust procedure is presented for increasing the technological level with the highest margin, improving resource allocation, minimizing aircraft downtime, and ultimately setting a new standard for efficiency and reliability in aircraft maintenance.
. Rapid population growth and urbanization in Gresik Regency have triggered chronic transportation problems, resulting in severe congestion, limited public transportation, and unequal accessibility between regions. This study aims to identify the spatial distribution of congestion and formulate an initial integrated transportation planning strategy based on the transit-oriented development (TOD) framework, which prioritizes high-density mixed-use areas around public transit hubs to encourage public transportation use and walkability. We use a quantitative descriptive approach with multidimensional spatial analysis using Geographic Information System technology, a tool for capturing, managing, and analyzing spatial and geographic data. Our analysis includes buffering techniques-creating zones at set distances around transit points to assess their influence-and overlay analysis, which combines multiple layers of land use data to reveal spatial relationships. This study identifies four main congestion points on industrial and logistics roads. Private vehicles vastly outnumber mass transportation modes. As a solution, we propose developing a new bus stop network in which transportation nodes (key points where different transport modes intersect) are arranged to provide alternative routes. We also propose designing a transit-oriented development (TOD) area with a radius of 500-1,000 m, integrating office, commercial, and residential functions. We conclude that transit-oriented development TOD-based transportation planning is essential to improving mobility efficiency and ensuring equal accessibility in Gresik Regency. Our policy implications include recommendations for environmentally friendly infrastructure, stronger zoning regulations, and cross-sector synergy. These actions aim to reduce dependence on private vehicles. This research positions Gresik Regency as a strategic model for spatial transportation planning in a developing peri-industrial area with regions on the urban-rural fringe characterized by mixed land uses. We integrate GIS technology and the transit-oriented development (TOD) framework to transform irregular movement patterns into a structured and inclusive mobility system.
This paper presents the results of an experimental study conducted to determine the time needed to increase the braking deceleration of vehicles to reach the steady-state value after brake system activation (hereafter referred to as deceleration build-up time). This parameter is critical for vehicle dynamics analysis and road accident reconstruction, as it directly affects stopping distance and collision speed estimation. The present study investigates various road vehicles, including passenger cars, trucks, motorcycles, bicycles, electric scooters, and agricultural tractors towing trailers. Experimental tests were conducted under controlled conditions using high-precision measuring equipment. The braking process was analyzed to identify the transient phase from brake activation to maximum deceleration. A comprehensive statistical analysis was performed to ensure the scientific reliability of the findings. Detailed descriptive statistics are presented. Furthermore, 95% confidence intervals were calculated to establish the precision of the results. The results show significant differences in deceleration build-up time among vehicle categories and braking system types. These parameters can be used in forensic engineering and expert investigations of road traffic accidents to improve the accuracy of accident reconstruction and court expert analyses.
This article explores the emerging role of manned-unmanned teaming (MUMT) in modern military aviation, focusing on its integration with advanced technologies such as artificial intelligence (AI). MUM-T represents a pivotal shift in air warfare, allowing for collaboration between manned and unmanned platforms like Unmanned Aerial Vehicles, enhancing operational efficiency, reducing human risk, and enabling new combat tactics. The article highlights NATO and EU initiatives that emphasize AI and MUM-T for improving decision-making and situational awareness. It also delves into the tactical use of collaborative combat aircraft in air operations, including counterair missions, and the integration of AI for managing drone swarms and other autonomous systems. This paper proposes a conceptual framework for MUM-T and collaborative combat aircraft integration that incorporates higher levels of autonomy and AI-assisted coordination. A key contribution is the introduction of an operator capacity model, which describes the relationship between the number of unmanned systems, communication latency, and AI support. Furthermore, the article underscores the importance of robust, secure communication technologies and the future potential of AI-driven systems to support human-machine collaboration in complex military operations.
Integrating timetable flexibility with crew operational constraints into the scheduling of heterogeneous bus fleets is a significant challenge in public transport. To address this challenge, we developed in this paper a mixed-integer linear programming model that minimizes fleet size and operational costs while ensuring feasible crew assignments. The proposed model captures essential fleet constraints, including battery capacity limitations and non-linear charging profiles of electric vehicles. Our key contribution is an efficient solution methodology that first optimizes vehicle scheduling with timetable flexibility and then ensures crew feasibility through constraint satisfaction techniques. The effectiveness of our two-step approach was assessed through extensive computational experiments utilizing real-world data from an urban transportation system. The results demonstrate that strategic timetable shifting reduces fleet size and operational costs compared to fixed-timetable approaches without violating real-life vehicle constraints and crew regulation requirements. In addition, sensitivity analyses quantify the trade-offs between schedule flexibility, operational costs, and crew utilization. Our findings provide practical insights for transit operators transitioning to electric fleets, enabling the efficient management of both vehicle and crew resources.
. Although reliable schedule adherence is pivotal for service quality and passenger planning in urban transit, realized arrivals often deviate from timetables in non-Gaussian and time-dependent ways. This study provides a probabilistic-statistical assessment of how actual arrivals accord with schedules, thereby addressing the analytical gap of stop-level comparisons across time of day and weekday strata. Using one month of real-time GNSS records matched to static GTFS for Warsaw bus line 112, we analyzed 8.6 million validated stop events. Delay distributions were characterized by robust statistics (median, interquartile range, skewness, kurtosis) and normality diagnostics (Lilliefors, Anderson-Darling, Jarque--Bera), revealing heavy tails up to +/- 20 min and invalidating Gaussian assumptions. Non-parametric tests (Kruskal-Wallis; Dunn-& Scaron;id & aacute;k) showed statistically significant but practically small differences in median delay across seven time-of-day bands and weekdays (largest shift approximate to 1.2 min; typical deviations within +/- 1 min). We then clustered stop-level delay profiles (medians by time band) with K-means to uncover four interpretable punctuality archetypes (punctual, evening catch up, pre-schedule variability, and afternoon peak bottleneck), mapped along both travel directions and examined by weekday. We used these profiles to link delay patterns to infrastructure and operations (e.g., initial segments, intersections, recurring congestion) and furnish actionable outputs: targeted buffer allocation, identification of segments for control, and interpretable features to enhance learning-based arrival time prediction. Although demonstrated on a single line, the methodology is general and transferable to other networks with comparable GNSS and GTFS data.
Passenger satisfaction is a key indicator for assessing the service quality of urban railway systems. This paper examines causal relationships among passenger satisfaction, passenger expectations, perceived quality, perceived value, passenger complaints, and passenger loyalty in the context of urban railway services in Hanoi, Vietnam, using the American Customer Satisfaction Index (ACSI). This quantitative research employed structural equation modeling (SEM), using data gathered from 616 passengers via structured questionnaires. The results show that the American Customer Satisfaction Index is suitable for examining Hanoi's urban rail services in Vietnam. Perceived value, perceived quality, and passenger expectations are important factors that influence passenger satisfaction in decreasing order. Moreover, passenger satisfaction influences passenger complaints and passenger loyalty. Subsequently, passenger complaints also influence passenger loyalty. Based on these results, several policy implications have been proposed to increase passenger satisfaction using Hanoi’s urban railways.
Following the fostering of sustainable mobility policies in urban areas, accidents involving vulnerable road users have increased recently, prompting more detailed analyses of effective countermeasures. Solutions based on traffic calming may represent a valuable answer to this problem. However, the inner principles governing their performance have not been sufficiently studied. This work focuses on vertical traffic calming devices, with the aim of developing a new performance-based modeling framework to ensure proper and refined speed control by vehicles near the device, ensuring the safe passage of pedestrians. A key design variable is the vehicle fleet traveling on the road where the traffic calming device is installed. This aspect was investigated by analyzing vertical dynamic interaction by means of a mathematical model carried out for different vehicle types, highlighting similar vertical acceleration (RMS) experienced by different vehicles when passing at different speeds over a defined raised pedestrian crossing. The model was calibrated through speed profiles extracted from video-based measurements. Future developments will involve implementing a driving simulator scenario to generalize and further validate the current findings.
. This study examines the potential of automated and autonomous vehicles to improve road safety, with particular emphasis on Polish road conditions. The analysis begins with an assessment of the scale and structure of road accidents, identifying human error as the dominant contributing factor. The paper presents the DARTS-PL project, which aims to develop a comprehensive multimodal database for testing vehicles equipped with driving automation at SAE Levels 3-5 under real-world traffic conditions. Particular attention is given to the measurement infrastructure and the role of local factors, including road signage and road network characteristics. Based on the analysis of international and national datasets, key risk factors affecting their performance are identified, including infrastructure complexity and interactions with other road users. The results indicate that the deployment of automated and autonomous vehicles can significantly reduce the number of road accidents, with an estimated reduction of approximately 25% by 2035. Furthermore, the study evaluates the associated socio-economic benefits, demonstrating a potentially substantial decrease in accident-related costs. The findings highlight the importance of integrating technological development with infrastructure adaptation and regulatory frameworks to support the effective deployment of these vehicles.
The present work proposes an advanced smart solution for the domain of autonomous cars and intelligent transportation. The suggested solution uses sophisticated AI techniques to detect drivers' drowsiness, Liquefied Petroleum Gas LPG leaks in and around the vehicle, the internal temperature, humidity, the safe distance from other vehicles for all sides (left, right, front, and rear), vehicle speed, the traveled distance requiring a rest, and other complementary functions. The main goal of this study is to ensure the safety of drivers, vehicles, and other persons on the road to minimize the harm that traffic accidents inflict on people and property.
This article presents an analysis of regular diesel fuel and synthetic diesel fuel-hydrogenated vegetable oil (HVO)-evaluating their economic and environmental performance under real-world operating conditions. Experimental tests were conducted on a BMW E46 330D, measuring fuel consumption, engine power, torque, CO2 emissions, and other exhaust gases while driving in city and suburban modes. The results show that average fuel consumption increased by approximately 10% when using HVO compared to regular diesel. The decrease in engine performance was minimal: power decreased by about 1.93%, and torque by 0.85%. From an environmental perspective, HVO significantly reduced CO2 emissions and smoke opacity (by up to 37.5%), as well as CO and Hydrocarbon emissions. However, a slight increase in NOx emissions was observed under certain load conditions. HVO is an alternative to regular diesel fuel that ensures better environmental performance with only minor economic and operational compromises.
A simulation study was employed to investigate the question of how an automated shuttle fleet can be sensibly parameterized in on-demand transport. The two main influencing parameters studied were lead time, which indicates the delay before starting the journey to the first demanding customer, and the number of shuttles to reach dedicated service levels. The operation should be able to replace a conventional bus route. In addition to passenger transport, the automated shuttles are also used to organize parcel delivery services. An innovative vehicle concept consisting of the drive unit and the exchangeable payload was used for this purpose. This paper examines the passenger transport use case, for which up to three shuttles will be available. The level of service that can be achieved with different operating logics was simulated. In terms of demand, a traffic count by the bus operator was used, although the shuttle service as a demonstrator will presumably generate different and additional demand. Based on the analyzed scenarios, the times for which a shuttle should wait before serving the first incoming demand can be estimated in order to balance bundling effects and total waiting times for passengers and to characterize the overall service quality.
Road accidents are an inevitable part of road traffic. Those involving trucks and vulnerable road users are especially tragic. Multiple solutions are being proposed to deal with this problem. Among them is the concept of using camera-monitor systems instead of traditional exterior mirrors. This paper presents results of research conducted using trucks equipped with conventional devices for indirect vision (i.e., external mirrors) as well as the camera-monitor system that replaced mirrors. Test drives took place in actual urban road traffic. An eye-tracker was used to assess the perceptions of drivers. The results showing the extent of usage of the abovementioned devices for indirect vision were compared. A significant difference between the fields of direct view of both systems was noticed. The camera-monitor system made it possible to eliminate some of the blind spots around the truck. Moreover, this research shows that the camera-monitor system can both significantly improve the safety of vulnerable road users and become a viable alternative for traditional mirrors. The ultimate aim of the ongoing research is to develop a driver perception model that could be utilized for autonomous vehicles in the future.
This paper describes the unberthing of container ships using conventional and unconventional maneuvers. The research has been carried out in the Maasvlakte in the Port of Rotterdam (Netherlands). This research analyzes the departure of ships with the onshore wind (various weather conditions) without tug assistance. The analysis of unberthing maneuvers, taking into consideration external conditions, draft, and load onboard, is based on the knowledge gained from the theory and experience in the performance of unconventional maneuvers without the assistance of a tugboat. Challenges posed to the maritime industry by developments in shipbuilding technology are discussed. This paper also presents the specifications of the ships under analysis and describes quays at the Port of Rotterdam that have been adapted for the berthing of container vessels. Unberthing maneuvers are also explained in detail. Additionally, the optimum unberthing method to be used in the Port of Rotterdam is presented. The results provide recommendations for captains of vessels of similar navigational and operational parameters.
