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Traffic Analysis and Safety Enhancement through Deep Learning Models
굴람 무스타파 선문대학교 일반대학원 2024 국내석사
Traffic Analysis and Safety Enhancement through Deep Learning Models This thesis explores the use of deep learning models to improve urban transportation systems, addressing issues like traffic congestion, travel times, productivity reduction, and reduced quality of life. It consists of three interconnected studies, each contributing to a better understanding of urban transportation dynamics and road safety enhance- ment. The first study, conducted in Cheonan city, South Korea, focuses on bus travel time prediction using digital tachograph data from intracity buses. The research evaluates six deep learning models, including Long Short-Term Memory (LSTM) and Gated Recur- rent Unit (GRU) architectures, to predict travel times between Namchang Village bus stop and Dongnam-gu Public Health Center. Comparative analyses reveal that LSTM- based models, particularly Stacked-LSTM, exhibit the highest accuracy. The inclusion of weather data enhances prediction accuracy, providing valuable information for transit agencies and policymakers. The study reports a 47.69% reduction in root mean squared error compared to the baseline ARIMA model, offering data for optimizing public trans- portation schedules and efficiency. The second study focuses on the classification of driver behaviors, with applica- tions in enhancing driver safety, preventing traffic accidents, enabling usage-based insur- ance, and optimizing ridesharing services. The Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC) model adeptly distinguishes between nor- mal, drowsy, and aggressive driving behaviors. Experimental assessments on the UAH- DriveSet dataset reveal better performance than the previous research using the same database, with an accuracy rate of 94.12%, precision score of 94.24%, recall score of 94.12%, F1-score of 94.12%, and ROC AUC of 98.83%. These findings offer detailed in- formation regarding the precise recognition of driver behaviors, which in turn contributes to safer roads, more accurate risk assessment, and enhanced driving experiences. The third study analyzes traffic accidents in Seoul, South Korea, employing the ”TrafficNet” hybrid CNN-FNN model. The model proficiently categorizes accidents into groups, including minor injuries, slander, fatalities, and injury reports. Experiments conducted on traffic accident data from the Seoul Metropolitan Government demonstrate the model’s effectiveness, with an accuracy rate of 93.98%, precision score of 94.31%, recall score of 93.98%, and F1-score of 93.89%. These findings shed light on the primary determinants of traffic accident severity in Seoul, with a specific focus on the influence of vehicle-related factors. This study provides information that can guide the decisions of local authorities and policymakers, with the ultimate goal of improving road safety and mitigating the social and economic consequences of traffic accidents. Keyword: bus travel time prediction; deep learning techniques; digital tachograph data (DTG); Driver behavior classification; Advanced driver-assistance systems (ADAS); Traffic accident prevention; UAH-DriveSet dataset; CNN-FNN hybrid model.
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(A) micromechanical approach for the prediction of fatigue life in composite laminates
Mustafa, Ghulam 한양대학교 대학원 2011 국내석사
A micromechanics based fatigue life prediction of composite laminates under multi-axial loading was developed. Many mechanical tests are required to fully characterize the composite laminates made of various materials and having different layup sequences. In order to reduce number of tests, a methodology was presented in this paper to predict fatigue life of composite laminates based on fatigue life of constituents, i.e. the fiber, matrix and interface, using micromechanics of failure (MMF). For matrix, the equivalent stress model which is generally used for isotropic materials was employed to take care of multi-axial fatigue loading. For fiber, a maximum stress model considering only stress along fiber direction was used. Critical plane model was introduced for the interface of the fiber and matrix, but interface fatigue strength was presumed strong enough than that of matrix and fiber and, therefore, its failure was ignored. The modified Goodman approach was utilized to take into account the mean stress effect. In order to validate the proposed methodology, the fatigue life of three different GFRP laminates, UDT[90°], BX[±45°]S and TX[0°2/±45°]S, was examined experimentally. The predictions are compared with the experimental data, and are shown in good agreement. A comprehensive implementation example is also presented for the case of composite wind turbine rotor blade structure.
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