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  1. Article ; Online: Exploring the Correlation Between Preexisting Knowledge and Public Perception of Self-Driving Cars

    Kareem Othman

    Transactions on Transport Sciences, Vol 14, Iss 3, Pp 61-

    2023  Volume 68

    Abstract: Self-driving vehicles (SDVs) possess the potential to provide novel benefits while also presenting new risks. Consequently, SDVs are expected to not only influence the transportation network but also reshape urban landscapes, markets, economies, and ... ...

    Abstract Self-driving vehicles (SDVs) possess the potential to provide novel benefits while also presenting new risks. Consequently, SDVs are expected to not only influence the transportation network but also reshape urban landscapes, markets, economies, and public behavior. The public's willingness to utilize or ride in SDVs is a critical factor determining the extent to which their implications can be realized. Previous research has indicated that awareness of SDVs is a key factor influencing the public's decision-making and attitude toward this nascent technology. However, none of these studies have exclusively examined the relationship between the public's level of knowledge about SDVs and their attitudes. Thus, this study employs a questionnaire survey to investigate the relationship between the public's attitudes and their knowledge of SDVs. The study analyzes 2447 complete responses collected from participants in the United States. The findings suggest that individuals possessing prior knowledge of SDVs are more likely to use them. However, participants with intermediate knowledge were the most likely to use SDVs compared to those with no knowledge and those with extensive knowledge. Moreover, the analysis demonstrates that the relationship between the level of knowledge and acceptance of SDVs is non-linear and peaks at the intermediate knowledge level.
    Keywords interest ; trust ; concern ; self-driving cars ; knowledge ; public attitude ; Social sciences (General) ; H1-99 ; Transportation and communications ; HE1-9990
    Subject code 306
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher Palacký University Olomouc
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Prediction of the hot asphalt mix properties using deep neural networks

    Kareem Othman

    Beni-Suef University Journal of Basic and Applied Sciences, Vol 11, Iss 1, Pp 1-

    2022  Volume 14

    Abstract: Abstract Background Marshall design process is the most common method used for estimating the Optimum asphalt content (OAC) and this process is called the asphalt mix design. However, this method is time-consuming, labor-intensive, and its results are ... ...

    Abstract Abstract Background Marshall design process is the most common method used for estimating the Optimum asphalt content (OAC) and this process is called the asphalt mix design. However, this method is time-consuming, labor-intensive, and its results are subjected to variations. Results This paper employs artificial neural network (ANN) for the estimation of Marshall test parameters (OAC, Stability, Flow, Air voids, Voids in mineral aggregate) using the aggregate gradation as the input of the prediction process. Multiple ANNs are tested in order to optimize the NN hyperparameters and produce accurate predictions. Different activation functions, number of hidden layers, and number of neurons per hidden layer are tested and heatmaps are generated to compare the performance of every ANN. Results show that the optimum ANN hyperparameters change depending on the predicted parameter. Finally, the deep NN can predict the OAC, stability, flow, density, air voids, and voids in mineral aggregate with R values of 0.91, 0.8, 0.53, 0.65, 0.77, and 0.66. Conclusion The linear activation function is the most efficient activation function and generates more accurate results than the logistic and the hyperbolic tangent functions. Additionally, it is shown that the deep neural network approach represents a major innovative tool for the prediction of the asphalt mix properties as results of this approach outperforms results of the shallow ANN that consists of a single hidden layer which is the only approach used in the literature. Thus, the use of the deep ANN can be useful during the phase of the design of the asphalt mix process because of its ability to predict variables with high accuracy. For example, the ANN with 3 hidden layers and 16 neurons per layer with the linear activation function can predict the OAC with high accuracy (R = 0.91), which can be helpful in the design process as the ANN can be employed for the prediction of the OAC of the asphalt mix.
    Keywords Artificial neural networks ; Early stopping technique ; Hot asphalt mix ; Marshall mix design ; Mechanical properties ; Optimum asphalt content ; Medicine (General) ; R5-920 ; Science ; Q
    Subject code 620
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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