Effect of Real-Time Passenger Information Systems on Perceptions of Transit’s Favorable Environmental and Traffic Reduction Roles

A two-wave survey of faculty, staff, and students at a large university was conducted to study the perceptions of and attitudes toward several dimensions of the university bus service before and after the implementation of a real-time passenger information system. In this study, community perceptions of the bus service’s role in enhancing the environment and reducing traffic were investigated. Results showed that both users and nonusers of the bus service had positive perceptions of the bus service’s environmental and traffic reduction roles, that those who noticed the recently implemented real-time information system had more positive attitudes, and that the effect of the information system on the perceptions was as great or greater for those who did not use the bus service as it was for those who used the service. It is hypothesized that these results, especially if confirmed in different communities, could motivate transit agencies to promote environmental and traffic reduction benefits of transit to gain public support of nonusers for transit subsidies and to market high-tech and progressive investments to increase support among nonusers

Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network

Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced using random changes in the stiffness of different parts of the structure. Contrary to the assumption of inherent reliability, undetected defects in crucial structural elements can lead to catastrophic failures. Then, the vibration equations of healthy and damaged models are analyzed to find the displacement, velocity, and acceleration of the different crane parts. The learning vector quantization neural network is used to train and detect the defects. The output is the location of the damage and the damage severity. Noisy data are then used to evaluate the network performance robustness. This research also addresses the limitations of traditional inspection methods, providing early detection and classification of defects in gantry cranes. The study’s relevance lies in the need for a comprehensive and efficient damage detection method, especially for components not easily accessible during routine inspections

Machine Learning-Based Modelling and Meta-Heuristic-Based Optimization of Specific Tool Wear and Surface Roughness in the Milling Process

The purpose of this research is to investigate different milling parameters for optimization to achieve the maximum rate of material removal with the minimum tool wear and surface roughness. In this study, a tool wear factor is specified to investigate tool wear parameters and the amount of material removed during machining, simultaneously. The second output parameter is surface roughness. The DOE technique is used to design the experiments and applied to the milling machine. The practical data is used to develop different mathematical models. In addition, a single-objective genetic algorithm (GA) is applied to numerate the optimal hyperparameters of the proposed adaptive network-based fuzzy inference system (ANFIS) to achieve the best possible efficiency. Afterwards, the multi-objective GA is employed to extract the optimum cutting parameters to reach the specified tool wear and the least surface roughness. The proposed method is developed under MATLAB using the practically extracted dataset and neural network. The optimization results revealed that optimum values for feed rate, cutting speed, and depth of cut vary from 252.6 to 256.9 (m/min), 0.1005 to 0.1431 (mm/rev tooth), and from 1.2735 to 1.3108 (mm), respectively