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Forecasting Empty Container availability for Vehicle Booking System Application

Arthur Cartel Foahom Gouabou, Mohammed Al-Kharaz, Faouzi Hakimi, Tarek Khaled, Kenza Amzil

TL;DR

This study tackles predicting hourly empty container availability at container terminal depots to support Vehicle Booking System planning over a five-business-day horizon. It evaluates four forecasting approaches—Naive, ARIMA, Prophet, and LSTM—on real terminal data, with time-series cross-validation to ensure realistic assessment. The results show that LSTM provides the most accurate and consistent forecasts, while Prophet underperforms, highlighting the importance of model choice for complex depot dynamics. The work contributes to operational decision-making in maritime logistics by enabling proactive container allocation and reduced dwell time, and points to future improvements through ensembles and more granular, real-time data integration.

Abstract

Container terminals, pivotal nodes in the network of empty container movement, hold significant potential for enhancing operational efficiency within terminal depots through effective collaboration between transporters and terminal operators. This collaboration is crucial for achieving optimization, leading to streamlined operations and reduced congestion, thereby benefiting both parties. Consequently, there is a pressing need to develop the most suitable forecasting approaches to address this challenge. This study focuses on developing and evaluating a data-driven approach for forecasting empty container availability at container terminal depots within a Vehicle Booking System (VBS) framework. It addresses the gap in research concerning optimizing empty container dwell time and aims to enhance operational efficiencies in container terminal operations. Four forecasting models-Naive, ARIMA, Prophet, and LSTM-are comprehensively analyzed for their predictive capabilities, with LSTM emerging as the top performer due to its ability to capture complex time series patterns. The research underscores the significance of selecting appropriate forecasting techniques tailored to the specific requirements of container terminal operations, contributing to improved operational planning and management in maritime logistics.

Forecasting Empty Container availability for Vehicle Booking System Application

TL;DR

This study tackles predicting hourly empty container availability at container terminal depots to support Vehicle Booking System planning over a five-business-day horizon. It evaluates four forecasting approaches—Naive, ARIMA, Prophet, and LSTM—on real terminal data, with time-series cross-validation to ensure realistic assessment. The results show that LSTM provides the most accurate and consistent forecasts, while Prophet underperforms, highlighting the importance of model choice for complex depot dynamics. The work contributes to operational decision-making in maritime logistics by enabling proactive container allocation and reduced dwell time, and points to future improvements through ensembles and more granular, real-time data integration.

Abstract

Container terminals, pivotal nodes in the network of empty container movement, hold significant potential for enhancing operational efficiency within terminal depots through effective collaboration between transporters and terminal operators. This collaboration is crucial for achieving optimization, leading to streamlined operations and reduced congestion, thereby benefiting both parties. Consequently, there is a pressing need to develop the most suitable forecasting approaches to address this challenge. This study focuses on developing and evaluating a data-driven approach for forecasting empty container availability at container terminal depots within a Vehicle Booking System (VBS) framework. It addresses the gap in research concerning optimizing empty container dwell time and aims to enhance operational efficiencies in container terminal operations. Four forecasting models-Naive, ARIMA, Prophet, and LSTM-are comprehensively analyzed for their predictive capabilities, with LSTM emerging as the top performer due to its ability to capture complex time series patterns. The research underscores the significance of selecting appropriate forecasting techniques tailored to the specific requirements of container terminal operations, contributing to improved operational planning and management in maritime logistics.

Paper Structure

This paper contains 16 sections, 7 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Problem Definition
  5. Naive method
  6. ARIMA
  7. Prophet forecasting algorithm
  8. LSTM
  9. Performance Metrics
  10. Data
  11. Forecasting Model Development and Parameter Tuning
  12. ARIMA Model Configuration
  13. Prophet Model Configuration
  14. LSTM Model Configuration
  15. Results and Discussion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Proposed Approach for Forecasting Empty Container Availability.
  • Figure 2: Trends in Empty Container Stock by Type Over Time.