Search Results (13)

In periodic technical inspections (PTIs), evaluating the braking efficiency of light passenger vehicles at their Maximum Authorized Mass (MAM) presents a practical challenge, as bringing laden vehicles to inspection is often unfeasible due to logistical and infrastructure limitations. The fBrake method is proposed to overcome this issue by estimating braking efficiency at MAM based on measurements taken from vehicles in more accessible loading conditions. In this study, the fBrake method is validated by demonstrating the equivalence of its efficiency estimates extrapolated from two distinct configurations: an unladen state near the curb weight and a partially laden condition closer to MAM. Following the UNE 26110 standard (Road vehicles. Criteria for the assessment of the equivalence of braking efficiency test methods in relation to the methods defined in ISO 21069), roller brake tester measurements were used to obtain force data under both conditions. The analysis showed that the extrapolated efficiencies agree within combined uncertainty limits, with normalized errors below 1 in all segments tested. Confidence intervals were reduced by up to 74% after electronics update. These results confirm the reliability of the fBrake method for M1 and N1 vehicles and support its adoption as an equivalent procedure in compliance with UNE 26110, particularly when fully laden testing is impractical. Full article

This study presents a novel artificial intelligence-driven architecture for real-time sensor fusion in autonomous vehicles (AVs), leveraging Apache Kafka and MongoDB for synchronous and asynchronous data processing to enhance resilience against sensor failures and dynamic conditions. We introduce Chaotic AdaBoost (CAB), an advanced variant of AdaBoost that integrates a logistic chaotic map into its weight update process, overcoming the limitations of deterministic ensemble methods. CAB is evaluated alongside k-Nearest Neighbors (kNNs), Artificial Neural Networks (ANNs), standard AdaBoost (AB), Gradient Boosting (GBa), and Random Forest (RF) for speed and acceleration prediction using CARLA simulator data. CAB achieves a superior 99.3% accuracy (MSE: 0.018 for acceleration, 0.010 for speed; MAE: 0.020 for acceleration, 0.012 for speed; R²: 0.993 for acceleration, 0.997 for speed), a mean Time-To-Collision (TTC) of 3.2 s, and jerk of 0.15 m/s³, outperforming AB (98.5%, MSE: 0.15, TTC: 2.8 s, jerk: 0.22 m/s³), GB (99.1%), ANN (98.2%), RF (97.5%), and kNN (87.0%). This logistic map-enhanced adaptability, reducing MSE by 88% over AB, ensures robust anomaly detection and data fusion under uncertainty, critical for AV safety and comfort. Despite a 20% increase in training time (72 s vs. 60 s for AB), CAB’s integration with Kafka’s high-throughput streaming maintains real-time efficacy, offering a scalable framework that advances operational reliability and passenger experience in autonomous driving. Full article

The acoustic performance of automotive micro-motors directly impacts the comfort and driving experience of both drivers and passengers. However, various motor production and testing uncertainties can lead to noise fluctuations during operation. Thus, predicting the operational noise range of motors on the production line in advance becomes crucial for timely adjustments to production parameters and process optimization. This paper introduces a prediction model based on a Multi-Branch Channel–Spatial Adaptive Weighting Strategy (MCSAWS). The model includes a multi-branch feature extraction (MFE) network and a channel–spatial attention module (CSAM). It uses the vibration and noise data from micro-motors’ idle operations on the production line as input to efficiently predict the operational noise uncertainty interval of automotive micro-motors. The model employs the VAE-GAN approach for data augmentation (DA) and uses Gammatone filters to emphasize the noise at the commutation frequency of the motor. The model was compared with Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Experimental results demonstrate that the MCSAWS method is superior to conventional methods in prediction accuracy and reliability, confirming the feasibility of the proposed approach. This research can help control noise uncertainty in micro-motors’ production and manufacturing processes in advance. Full article

The traditional method of allocating passenger seats based on compartments does not effectively manage an aircraft’s center of gravity (CG), resulting in a notable divergence from the desired target CG (TCG). In this work, the Boeing B737-800 aircraft was employed as a case study, and row-based and compartment-based integer programming models for passenger allocation were examined and constructed with the aim of addressing the current situation. The accuracy of CG control was evaluated by comparing the row-based and compartment-based allocation techniques, taking into account different bodyweights and numbers of passengers. The key contribution of this research is to broaden the range of the mobilizable set for the aviation weight and balance (AWB) model, resulting in a significant reduction in the range of deviations in the center of gravity outcomes by a factor of around 6 to 16. The effectiveness of the row-based allocation approach and the impact of passenger weight randomness on the deviation of an airplane’s CG were also investigated in this study. The Monte Carlo method was utilized to quantify the uncertainty associated with passenger weight, resulting in the generation of the posterior distribution of the aircraft’s center of gravity (CG) deviation. The outcome of the row-based model test is the determination of the range of passenger numbers that can be effectively allocated under different TCG conditions. Full article

Pneumatic actuator is one of the key technologies in the field of active suspension due to its low cost, cleanliness, and high power-to-weight ratio characteristics. However, the dynamic models and control strategies of the pneumatic suspension have not been well demonstrated because they are nonlinear systems. Besides, the vertical displacement stability of sprung mass is very important for ensuring ride comfort, but accurate control is still a challenging problem in the presence of parametric uncertainties. In this study, an adaptive neural networks backstepping scheme is designed for the stability control of the pneumatic suspension. Firstly, a mathematical model of the pneumatic system is studied to investigate the dynamic system behavior and to obtain the control algorithm. Secondly, an extended state observer is applied to estimate uncertain parameters, unmodeled dynamics, and external disturbances. Thirdly, unknown masses of various load passengers are approximated by using radial basis function neural networks (RBFNNs). To enhance the system stability, a proposed control with a prescribed performance function (PPF) is designed to guarantee the vertical displacement of the chassis. Adaptive backstepping control with PPF is developed to stabilize the perturbed system and guarantee tracking performance. Finally, the simulation examples for the pneumatic suspension are employed to investigate the effectiveness of the proposed method. Full article

The quality of the public transport sector affects the economy and the daily livelihoods of passengers. One of the most important objectives of policymakers is to choose the influencing criteria for performance evaluations. A variety of factors are crucial for raising the standards of public transportation services. In this investigation, we used a decision-based model with uncertainty in order to identify significant criteria in the public transport sector. We also performed a comparative analysis to rank the Rajasthan State Road Transport Corporation (RSRTC) bus depots based on their performance using hybrid multi-criteria decision-making (MCDM) techniques such as TOPSIS, VIKOR, and ELECTRE. To handle judgement ambiguities, in this work we incorporated the Delphi method (DM) and the analytic hierarchy process (AHP), along with fuzzy set theory. The fuzzy Delphi method was used to filter the important criteria. Using a fuzzy AHP approach, the screening criterion weights and rankings were determined. Furthermore, the bus depots were ranked using TOPSIS, VIKOR, and ELECTRE. Our findings can be applied in assisting policy-managers in formulating appropriate policies targeted at improving the overall health and competitiveness of bus depots using significant criteria and associated key indicators. In this study we investigated performance measures and proposed recommendations for the sustainable development of transportation in India. Full article

In the context of the decarbonization of the shipping industry, the application of clean energy technologies is a catalyst for decarbonization. With the number of potential clean energy technologies expanding, the uncertainties in terms of technology maturity, policy regulation, and economics make clean energy technologies decision much more difficult. Therefore, it is urgent to establish a clean energy technology selection scenario for the green ship industry to assist shipowners in decision-making. Based on this, a technology selection model based on rough set (RS) and approximate ideal solution ranking (TOPSIS) is constructed. Using RS to reduce the evaluation index and calculate the weight can avoid the one-sidedness of subjective weighting. Using the TOPSIS method to rank alternatives. This paper selects seven clean energy technology alternatives, namely LNG power, LPG power, methanol power, HVO power, pure battery power, hydrogen fuel cell, and ammonia fuel cell, respectively, as the evaluation objects. Taking two types of vessels as examples, it is concluded that LNG power technology is suitable for large coastal ro-ro passenger vessels, and pure battery power technology is suitable for small inland river short-distances vessels. The results are in line with reality, which verifies the scientificity and validity of the proposed model. Full article

A technology assessment is conducted for battery electric and conventional fossil-fueled passenger vehicles for three Australian scenarios and seven Australian states and territories. This study uses a probabilistic life-cycle assessment (pLCA) to explicitly quantify uncertainty in the LCA inputs and results. Parametric input distributions are developed using statistical techniques. For the 2018 Australian electricity mix, which is still largely fossil fuels based, the weight of evidence suggests that electric vehicles will reduce GHG emission rates by 29% to 41%. For the ‘fossil fuels only’ marginal electricity scenario, electric vehicles are still expected to significantly reduce emission rates by between 10% and 32%. Large reductions between 74% and 80% are observed for the more renewables scenario. For the Australian jurisdictions, the average LCA GHG emission factors vary substantially for conventional vehicles (364–390 g CO₂-e/km), but particularly for electric vehicles (98–287 g CO₂-e/km), which reflects the differences in fuel mix for electricity generation in the different states and territories. Electrification of the Tasmanian on-road fleet has the largest predicted fleet average reduction in LCA greenhouse gas emissions of 243–300 g CO₂-e/km. A sensitivity analysis with alternative input distributions suggests that the outcomes from this study are robust. Full article

The condition of railway infrastructure, such as rails, ballasts and sleepers, should always be monitored and analyzed to ensure ride safety and quality for both passengers and freight. It is hard to assess the condition of railway infrastructure due to the existence of various components. The existing condition assessment models are mostly limited to only assess track geometry conditions and structural condition of the railway infrastructure. Therefore, the present research develops a defect-based structural and geometrical condition model of railway infrastructure. The defects of each component are identified and examined through literature and experts in the field. Two main inputs are used to develop the model: (1) the relative weight of importance for components, defects and their categories and (2) defects severities. To obtain the relative weights, the analytic network process (ANP) technique is adopted. Fuzzy logic is used to unify all the different defect criteria and to interpret the linguistic condition assessment grading scale to a numerical score. Hence, the technique for order preference by similarity to ideal Solution (TOPSIS) is used to integrate both weights and severities to determine the railway infrastructure condition. The developed model gives a detailed condition of the railway infrastructure by representing a three-level condition state, for defect categories, components and an overall railway infrastructure. The developed model is implemented to five case studies from Ontario, Canada. The developed model is validated by comparing its results with the real case studies results, which shows similar results, indicating the robustness of the developed model. This model helps in minimizing the inaccuracy of railway condition assessment through the application of severity, uncertainty mitigation and robust aggregation Full article

To increase the level of adequacy in multi-criteria decision-making in the case of uncertainty, it is essential to reduce the subjectivism and to increase the reality of obtained results. The study aims to propose a novel fuzzy multi-criteria method based on the fuzzy linear programming method and sequential interactive model for urban systems method (SIMUS), named fuzzy SIMUS. This paper is something completely different because it links the power of fuzzy with the advantage of the SIMUS method. Indeed, not using weights, it works with optimal values. Here, this procedure is presented for the first time. The methodology consists of three stages. The first stage includes forming the parameters of a multi-criteria model in the case of uncertainty. The initial matrix has three values: lower, medium, and upper. In the second stage, the fuzzy SIMUS model for each objective is formed based on fuzzy linear programming method. The third stage deals with the ranking of the alternatives. The methodology was experimented for planning railway intercity passenger transport in Bulgarian’s railway network. Nine alternative transport plans and eight criteria were studied. It was found that the objectives which influence ranking the most are the frequency of train stops (15%), direct operational costs (15%), train’s capacity (14.7%), and reliability (14.3%). A transport plan for railway passenger transport is proposed. A verification of the results was performed. It was found that the stability of the choice presented a suitable alternative. Full article

A fast and reliable vertical transportation system is an important component of modern office buildings. Optimization of elevator control strategies can be easily done using the state-of-the-art artificial intelligence (AI) algorithms. This study presents a novel method for optimal dispatching of conventional passenger elevators using the information obtained by surveillance cameras. It is assumed that a real-time video is processed by an image processing system that determines the number of passengers and items waiting for an elevator car in hallways and riding the lifts. It is supposed that these numbers are also associated with a given uncertainly probability. The efficiency of our novel elevator control algorithm is achieved not only by the probabilistic utilization of the number of people and/or items waiting but also from the demand to exhaustively serve a crowded floor, directing to it as many elevators as there are available and filling them up to the maximum allowed weight. The proposed algorithm takes into account the uncertainty that can take place due to inaccuracy of the image processing system, introducing the concept of effective number of people and items using Bayesian networks. The aim is to reduce the waiting time. According to the simulation results, the implementation of the proposed algorithm resulted in reduction of the passenger journey time. The proposed approach was tested on a 10-storey office building with five elevator cars and traffic size and intensity varying from 10 to 300 and 0.01 to 3, respectively. The results showed that, for the interfloor traffic conditions, the average travel time for scenarios with varying traffic size and intensity improved by 39.94% and 19.53%, respectively. Full article

Passenger satisfaction is an important factor that affects the choice of travel modes for municipalities, especially in big cities. This evaluation is an important task for managers when they are considering improving the competitiveness of the public transportation system. However, passenger satisfaction evaluation is difficult as the information provided by passengers is often vague, imprecise, and uncertain. This paper aims to propose a new method, using Pythagorean fuzzy sets and multi-objective optimization by a ratio analysis plus full multiplicative form method (MULTIMOORA), to evaluate the passenger satisfaction level of the public transportation system under large group environment. The former is employed to represent the satisfaction assessments of rail transit network provided by passengers. The latter is extended and used to determine the passenger satisfaction levels of rail transit lines. In addition, a combination weighting method is suggested to compute the relative weights of evaluation criteria. A case study of the rail transit network in Shanghai is provided to demonstrate the effectiveness of the proposed passenger satisfaction evaluation method. The result shows that the new method proposed in this study can not only model passengers’ satisfaction evaluation information with more uncertainties, but also determine more reasonable and credible satisfaction levels of rail transit lines. Full article

The aim of this study is to elaborate on an integrated approach for transport planning in railway passenger transport in the case of uncertainty. The methodology consists of four stages. In the first stage, the parameters of a multi-criteria model in the case of uncertainty were determined. This includes defining the criteria for selection of a transport plan; formulation of the alternatives of the transport plan; formulation of the strategies and probability variants of passenger flow variation for each strategy. In the second stage, the weights of the probability variants of the strategies for change in passenger flow were determined using the analytic hierarchy process (AHP) method. The alternatives of the transport plan were ranked by applying the sequential interactive modeling for urban systems (SIMUS) method based on linear programming. The results for the values of the criterion of ranking obtained through the SIMUS method and the weights of the variants of passenger flow variation calculated with the AHP method were used as input in the expected values in the decision tree. The selection of a suitable alternative in the case of uncertainty was conducted in the third stage by applying the decision tree method. In the fourth stage, verification of the results was made using Laplace’s criterion and Hurwitz’s criterion. The integrated multi-criteria approach was applied for Bulgaria’s railway network. The multi-criteria approach elaborated herein could be used for decision-making in the case of uncertainty about passenger flow; to investigate different strategies of passenger flow variation and to make decisions in case of instability of passenger flow or lack of sufficient travel data. Full article