컨테이너 터미널 내 반출입 차량 체류시간 예측 모형
Prediciton Model for External Truck Turnaround Time in Container Terminal
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초록
코로나 팬데믹 이후 컨테이너 터미널 내 혼잡도 증가에 따라 반출입 차량 작업 대기 및 체류시간이 급증하여 반출입 작업 비효율이 극심한 실정이다. 이에 항만 당국은 반출입예약시스템(Vehicle Booking System; VBS)을 구축하여 시범운영 중에 있으나 이해관계자 간 정보공유 문제 및 컨테이너 운송 주체의 미온적 참여 등으로 인해 개선효과가 뚜렷하지 않다. 따라서 본 연구에서는 반출입 차량의 작업 대기 및 체류시간 문제의 해결을 위한 기초자료로써, 딥러닝 기반의 반출입 차량 체류시간 예측 모형을 제시하였다. 실제 컨테이너 터미널의 반출입 운영 데이터를 통해 제시한 예측 모형을 실험하고 실제 데이터와 비교하여 예측 정확도를 검증한 결과 제시한 예측 모형이 높은 예측 정확도를 보이는 것을 확인하였다.
Abstract
Following the COVID-19 pandemic, congestion within container terminals has led to a significant increase in waiting time and turnaround time for external trucks, resulting in a severe inefficiency in gate-in and gate-out operations. In response, port authorities have implemented a Vehicle Booking System (VBS) for external trucks. It is currently in a pilot operation. However, due to issues such as information sharing among stakeholders and lukewarm participation from container transport entities, its improvement effects are not pronounced. Therefore, this study proposed a deep learning-based predictive model for external trucks turnaround time as a foundational dataset for addressing problems of waiting time for external trucks’ turnaround time. We experimented with the presented predictive model using actual operational data from a container terminal, verifying its predictive accuracy by comparing it with real data. Results confirmed that the proposed predictive model exhibited a high level of accuracy in its predictions.
참고문헌 (15)
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