Gunwoo Lee 교수

Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels’ technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a

The container shipping industry is receiving growing attention in driving the performance of global supply chains. This phenomenon has accelerated supply chain integration (SCI) within the industry. Although SCI could offer numerous benefits, it is often quoted to be implemented easier in theory than in practice. The high failure rate that is associated with SCI is often not addressed in the literature. Grounded on resource-based view (RBV) theory, this paper is aimed at identifying the critical

The San Pedro Bay Ports (SPBP) complex of Los Angeles and Long Beach in Southern California is one of the largest container port complexes in the world. This complex contributes significantly to both regional and national economies in California and the United States, respectively. However, the ongoing growth and economic benefits of the SPBP are threatened by negative externalities associated with port operations, particularly increasing congestion and air pollution. The objective of this paper

Accurately estimating fuel consumption of ships is crucial for shipping companies, port authorities, and environmental protection agencies. The bottom-up approach is becoming increasingly popular because it can estimate ship fuel consumption by accounting for ship activity conditions, such as changes in voyage speed, time, and distance; however, its use is still limited when estimating ship fuel consumption. Ship-specific information, such as the daily fuel consumption rate for main and auxiliar

The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network mo

Recently, the number of foreign tourists to Korea via cruise ships has increased dramatically. We attempted to estimate the shore excursion expenditure function during cruise tourism in Korea. To this end, we collected data from a survey of foreign tourists who visited Korea via cruise ships and conducted the ordered probit model with sample selection to correct for the sample selection bias. Statistical tests indicate that the sample selection model provides unbiased estimates of the ordered pr

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.

The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

Vehicle emissions are largely determined by the details of driving behaviours. Accordingly, emissions are often estimated by integrating micro-scale emission models into traffic simulations. Under this approach, it is essential to replicate the actual traffic situation being considered in an emission evaluation using a proper calibration procedure. Most previous research with respect to traffic flow has primarily focused on adjusting the complex combinations of parameters evaluated in these mode