Truck no-show behavior has posed significant disruptions to the planning and execution of port operations. By delving into the key factors that contribute to truck appointment no-shows and proactively predicting such behavior, it becomes possible to make preemptive adjustments to port operation plans, thereby enhancing overall operational efficiency. Considering the data imbalance and the impact of accuracy for each decision tree on the performance of the random forest model, a model based on the Borderline Synthetic Minority Over-Sampling Technique and Weighted Random Forest (BSMOTE-WRF) is proposed to predict truck appointment no-shows and explore the relationship between truck appointment no-shows and factors such as weather conditions, appointment time slot, the number of truck appointments, and traffic conditions. In order to illustrate the effectiveness of the proposed model, the experiments were conducted with the available dataset from the Tianjin Port Second Container Terminal. It is demonstrated that the prediction accuracy of BSMOTE-WRF model is improved by 4%-5% compared with logistic regression, random forest, and support vector machines. Importance ranking of factors affecting truck no-show indicate that (1) The number of truck appointments during specific time slots have the highest impact on truck no-show behavior, and the congestion coefficient has the secondhighest impact on truck no-show behavior and its influence is also significant; (2) Compared to the number of truck appointments and congestion coefficient, the impact of severe weather on truck no-show behavior is relatively low, but it still has some influence; (3) Although the impact of appointment time slots is lower than other influencing factors, the influence of specific time slots on truck no-show behavior should not be overlooked. The BSMOTE-WRF model effectively analyzes the influencing factors and predicts truck no-show behavior in appointment-based systems.

1. Introduction
2. Related works
    2.1 The methods of forecasting in Container TerminalScheduling
    2.2 Classification prediction model
    2.3 The application of oversampling in imbalanced datasets
3. Data and Model Construction
    3.1 Data Collection and Processing
    3.2 BSMOTE-WRF model
4. Result analysis
    4.1 Performance evaluation for BSMOTE-WRF model
    4.2 Importance ranking of factors affecting truck no-show
5. Discussion
6. Conclusion
Acknowledgements
References

• Mengzhi MA(Transportation Engineering College, Dalian Maritime University, China)
• Xianglong LI(Transportation Engineering College, Dalian Maritime University, China)
• Jian SUN(Transportation Engineering College, Dalian Maritime University, China) Corresponding Author