A mathematical model has been formulated to optimize the setup of an end-of-production-line (EOL) test bench that is used to evaluate the efficiency of electric motors or axles designed for electric vehicles. The model can forecast the performance of EOL testing benches and electric motors/axles under a variety of conditions, thereby eliminating the need for extensive physical trials and minimizing the associated energy usage. The proposed model can be adjusted to handle different power ratings of electric motors or axles. The model takes the maximum performance that the electric motors or axles need to guarantee according to the vehicle manufacturer’s specifications as inputs. Subsequently, the necessary performance of each primary EOL test bench component is computed, and the corresponding systems available in the market are chosen based on manufacturers’ catalogues. In this research, an EOL test bench for low-power e-axles (approximately 22 kW) has been designed according to the outputs of the proposed model.

In modern surveillance systems intended for surveilled areas, Unmanned Aerial Vehicles (UAVs) equipped with computer vision capabilities fulfill an essential role in tracking objects within dynamic and high-risk monitored regions. This paper presents a novel approach SP-TSA to estimate the areas where objects are likely to be present by analyzing their trajectories, which are estimated through UAV-based computer vision. Each trajectory is represented by a series of points in 3D space, with each point acting as the center of a sphere. The spatial uncertainty of the object’s position is captured by the sphere’s radius, providing a comprehensive probabilistic model of potential object locations. To model the area where an object could be present, the intersections of these spheres are analyzed, and the regions where the spheres overlap are used to form a continuous tubular surface along the trajectory. We introduce a Non-Linear Objective Function to optimize the estimation of these areas and minimize uncertainties in object location. This innovative approach ensures computational efficiency and adaptability to complex trajectories, making it suitable for real-time applications. The method offers a precise and robust way to predict the object’s presence within a given space, providing valuable insights for decision-making in dynamic surveillance environments. Simulation results validate the SP-TSA method, demonstrating its superior accuracy in estimating object presence compared to traditional methods, particularly in scenarios involving non-linear and erratic object trajectories.

This paper aimed to adopt the Value Engineering philosophy in enhancing the car seat cushion performance that emphasizes quality and productivity improvements. Value Engineering approaches are used to validate the design of car seat cushion to make the product more cost effective in terms of function and quality. Value Engineering methods are identified in the process of improving the design of car seat component cushion. The model of car seat component is developed using Autodesk® Inventor®. The design of the model is then analyzed using Boothroyd Dewhurst Design for Manufacture and Assembly (DFMA) software. Computer-aided engineering (CAE) ANSYS® software will be used in the analysis of displacement and stress. studies. The result shows that the force applied on the seat frame and seat cushion is set at 1177.2 N (120 kg). Maximum Von Mises Stresses for seat cushion and seat frame are 0.02337 MPa and 13.7 MPa. Maximum displacements for seat cushion and seat frame are 0.4026 mm and 0.006119 mm. FMEA was conducted on the model of car seat components to predict the possible failure and effect on the model. Hence, this paper provides valuable insight on potential car seat cushion improvement through Value Engineering approach.

The sustainability of urban development embodies settlement patterns and transport systems that are affected by residents’ travel preferences and the urban spatial structure. This study aims to analyze the influence of settlement and social and economic systems on movement actors toward the confirmation of cumulative integration in the structure of urban service centers. The research approach was quantitative based on multivariate statistics with the structural equation analysis method (SEM-PLS). Data were collected through observation, documentation, and survey. The results illustrate the distribution and concentration of residents, as well as the socio-economic conditions of the movers that influence the movers. Whereas movement actors have a weak influence on service centers, this is because access and connectivity to service centers can be reached from the periphery to the city center for medium-sized cities. The cumulative integration pattern illustrates that the service center is still dominated by a monocentric spatial structure as evidenced by the distribution of economic and socio-economic activities as well as movement actors who perform mobility to the city service center. This research contributes to urban planning to encourage sustainable urban growth.

The shift to electric vehicles in transportation is essential to mitigate pollution and achieve global climate goals. Recent EU regulations and incentives have accelerated the adoption of this strategy, especially in cities, facilitating the development of fleets of electric city buses. This paper explores the integration of biomass gasifiers and battery energy storage systems to develop environmentally sustainable high-power charging stations, focusing on Carpi, Italy, as a case study. By using locally available biomass resources, this approach aims to disconnect power from the electricity grid and reduce emissions. Through the analysis of different configurations, the study demonstrates once again how the economic sustainability of projects based on biomass gasifiers is strongly dependent both on the cost of biomass and the current energy market with which it competes. Only extending the use of the charging station to private vehicles generates a return on investment of around 7 years. However, through gasification is possible to achieve carbon capture and storage that, in the analyzed case study, is almost equivalent to the annual CO_2eq emission of 4 diesel buses.

The study presents a comprehensive bibliometric analysis of academic literature focusing on the intersection of transportation and its impact on climate change. Covering the period from 2018 to 2023, the research scrutinized a substantial dataset of 4,373 papers from the Scopus database. Employing VOSviewer for network visualizations and Microsoft 365 for data analysis, the study meticulously mapped publication trends and citation impacts within this period. This systematic approach provided a thorough understanding of the evolution and current state of research in this vital field, highlighting how academic focus on transportation’s role in climate change has intensified over time. Key findings from the study revealed a significant increase in research output, with the number of publications nearly doubling from 483 in 2018 to 932 by 2022, indicating a growing scholarly interest in this area. However, the analysis also uncovered notable variations in citation rates, with a peak citation per publication (CPP) of 24.04 for highly influential papers. This suggests a disparity in the influence of research output, with some studies gaining more recognition than others. Additionally, the study highlighted significant differences in scholarly production and impact across different countries, with the United States leading in publications and citations, followed by China and India. These findings underscore the importance of international collaboration in research, pointing to the need for policies that encourage and support collaborative efforts to tackle the global challenge of climate change through effective transportation strategies.

In big cities, traffic congestion is a prevalent issue. In order to decide how to manipulate traffic in order to alleviate congestion, traffic regulators, who supervise traffic flow, must conduct an analysis of present conditions. Classifying traffic conditions from road information is a critical step that impacts these decisions. Traffic conditions can be categorized using a variety of techniques, each with benefits and drawbacks of its own. Recently, the rapid development of machine learning techniques has accelerated their use in a variety of sectors, including intelligent transportation systems (ITS). In this study, a competitive machine learning system is introduced to support the decision-making process in ITS, specifically in traffic condition classification. The proposed system operates in two stages: first, identifying the best model configuration from various machine learning methods, and second, deciding through a voting system based on the selected models. The proposed system employs six machine learning methods, each with 4-5 variations in model configurations. The methods tested include Neural Networks, k-Nearest Neighbor, Logistic Regression, Bayesian Networks, Decision Trees, and Random Forests, with individual accuracy rates of 66.2%, 70.5%, 44.4%, 46.1%, 72.2%, and 72.6%, respectively. The models that achieved the highest performance for each method proceed to a voting system, both non-weighted and weighted. The experimental results indicate that the non-weighted system achieved an accuracy of 68.6% to 69.3%, while the weighted system reached 71.9% to 72.5%. The findings show that the proposed competitive machine learning system offers a viable solution for classifying traffic conditions with promising results, especially for implementation in Bandung City, Indonesia.

In Italy, the transport sector contributes significantly to carbon dioxide (CO₂) emissions, accounting for 30.7% of the total emissions, with road freight transport alone responsible for 25% of this figure. This situation demands urgent emissions reductions to meet the country’s national commitment to achieving net-zero by mid-century. The growing affordability of electric vehicles (EVs) due to improved energy densities and reduced lithium storage system costs is extending to heavy transport, promising emissions reductions. Additionally, short-term alternatives like hydrogen and liquefied natural gas (LNG) are being considered. To evaluate the carbon footprint of emerging transportation technologies, including internal combustion engine vehicles (ICEVs), fuel cell electric vehicles (FCEVs), LNG vehicles, and battery electric vehicles (BEVs), a detailed life cycle analysis (LCA) is essential. This research aims to inform decision-making processes, investment initiatives, and regulatory compliance by assessing emissions per kilometer within future scenarios. The study employs an LCA model integrating global supply chain contributions, offering regional context and scenario analysis. Findings indicate higher GHG emissions per kilometer for FCEVs and diesel vehicles, with BEVs emerging as promising alternatives. Moreover, the study highlights significant Scope 3 emissions associated with FCEV supply chains, emphasizing the broader environmental impacts of different vehicle types.

This study aims to identify the key factors influencing road accidents in Thailand, with a specific focus on unsafe motorcycle riding behaviors. Data were collected through an online survey targeting 496 motorcycle riders. The survey explored the impact of demographic factors such as marital status, income, age, and riding experience on riding behavior and accident risk. Key unsafe behaviors identified include eating while riding and transporting passengers, which compromise rider control. ANOVA analysis revealed that marital status is significantly related to traffic violations and injury severity, while higher income levels correlate with safer riding practices. Factor analysis classified risky behaviors into two groups: Traffic Violations (TV) and Safety Violations (SV), both of which are significantly associated with injury severity in accidents. Structural Equation Modeling (SEM) confirmed a clear relationship between these factors and accident outcomes. The findings highlight the importance of addressing risky behaviors, particularly helmetless riding and passenger transportation, in reducing accidents and injuries. The study provides insights for developing road safety strategies in Thailand, although it acknowledges limitations such as potential duplicate responses and the impact of social media platforms on data collection.

The modified xTRoad (MxT) model, an innovative route optimization framework, is presented to enhance urban traffic management within disaster-prone regions. This model uniquely integrates static and real-time data derived from the social media platform X (formerly known as Twitter) to improve route mitigation strategies, particularly during emergencies. The methodology employs a refined social media data extraction process using Boolean logic and a score-based evaluation system to identify disruptions, including flooding, obstructive debris, and public demonstrations. To validate the accuracy of the model, ground truth validation techniques were implemented, confirming the system’s efficacy in detecting obstacles and generating alternative routes. Performance testing was conducted on key transport arteries in Jakarta, where the MxT model demonstrated a detection accuracy exceeding 91.6% for traffic disruptions. Furthermore, the model achieved an average reduction in travel time by 15% compared to traditional traffic management systems. The MxT model dynamically adapts to real-time conditions, offering safer and more efficient navigation options in complex urban settings. The results underscore the MxT model’s potential as a scalable, adaptable solution for intelligent transport management during disaster scenarios, thereby contributing to the advancement of resilient urban infrastructure.

The increasing number of private vehicles has caused massive traffic congestion, and public transport (PT) services are one of the ways to reduce the problem. Even though few types of PT are provided to people, they still prefer private vehicles over the PT. Thus, to encourage them to use PT, we need to understand the factors that trigger people to use PT. This research aims to determine the effects of service quality dimensions on passenger preference for PT by using the structural equation model (SEM) approach. A study was conducted in the main cities of Sarawak state, Malaysia. A total of 199 respondents voluntarily participated in the survey. The result of PLS-SEM showed a significant relationship between customer service (β = 0.443, p < 0.001), safety (β = 0.199, p < 0.001), and accessibility (β = 0.175, p < 0.001) with passenger preferences towards PT services in main cities of Sarawak. The customer service achieved the highest coefficient and showed that customer service is an essential factor that PT providers need to consider in service delivery. Then, safety elements should be emphasized for passenger security, and PT providers should improve their accessibility to passengers’ welfare by increasing the availability of PT when passengers need it.

Modern cars use a hierarchical system of sensors, controlling devices, and controllers, linked via various intra-vehicle systems, to regulate and monitor the vehicle’s status. Researchers have confined numerous academic papers on intrusion detection in the Internet of Things (IoT), employing data mining and machine learning (ML) techniques to secure autonomous vehicles and detect potential attacks. To identify malicious attacks on the Internet of Vehicles (IoV), however, a competent and accurate method is required. This paper presents a model for cyber-attack detection in IoV that employs tree-based ML methods, an Improved Random Forest Classifier (IRFC), and Extra Tree (ET). We build the proposed model using Improved Random Forest (IRF) and ensemble learning techniques. The proposed IRF model employs optimized feature selection and tuning strategies to enhance intrusion sensitivity and decrease false positive rates. We evaluate the proposed model’s performance using the CI-CIDS 2018 dataset. Also, this work focuses mostly on the reduced feature selection and ensemble learning (EL) methods to get a high detection rate while keeping the cost of computing low. The test results show that the proposed method can find DDoS attacks and vehicle intrusions with a 0.99 accuracy rate.

The Kariangau Container Terminal serves as a port facilitating loading and unloading operations in Balikpapan City and the New Capital of the Archipelago. However, its service quality has not yet reached an optimal level for all customers. This is evident from the relatively low user perception ratings across several indicators, including tangibles, reliability, responsiveness, assurance, empathy, and credibility. This study aims to evaluate the quality of container loading and unloading services at Kariangau Container Terminal by examining user perceptions and expectations. The methods employed include Gap Analysis and the Customer Satisfaction Index (CSI). The key assessment indicators are tangibles, reliability, responsiveness, assurance, empathy, and credibility. The findings indicate that all customer satisfaction dimensions have negative gap values, suggesting that the service quality does not fully align with customer expectations. The analysis revealed an overall satisfaction level with an average gap of -0.365, indicating that while the service dimensions meet customer expectations to a certain extent, there is still room for improvement by Kariangau Container Terminal operators.