Departure Delays Research Articles

Departure Delay of a Semi Container Passenger Ship: A Case Study in KM Nggapulu

Departure schedule is an important thing to be considered by the companies and ship crews providing passenger ship. KM Nggapulu experienced a delay in departure at Fak-Fak Port on January 26, 2023. This raised complaints from passengers due to their inconvenience. This study aims to analyze the problem of departure delay of KM Nggapulu by identifying the factors and impacts of the delays, as well as actions to prevent delays in passenger ship. Qualitative method using fishbone analysis was applied in this research. Data were collected through observation, interviews, documentation studies, and literature studies. To test the validity of the data, researchers used triangulation. The research found that the delay in departure of KM Nggapulu is due to the late loading of containers. This occurred due to the negligence of the container loading and unloading party and the lack of coordination between PT SBN, the owner of the goods (shipper), and the ship’s crew. The delay of containers loading resulted in the ship's delayed departure for 5 (five) hours and caused complaints from passengers. Optimizing coordination between related parties, increasing manpower, and optimizing loading and unloading facilities at the port were the actions taken to prevent the delay.

Optimization berth allocation in container terminals: A Pyomo and Google Colab approach

Efficient berth allocation profoundly influences container terminal operations, affecting vessel waiting and turnaround times, and overall performance. This study presents a mixed-integer linear programming (MILP) model addressing the Berth Allocation Problem (BAP) in a Malaysian container port. By incorporating the Pyomo optimization library and CBC (Coin-or Branch and Cut) solver in Google Colab, optimal berth allocations are determined, minimizing vessel turnaround times. Visualized in a Space-Time diagram, the results highlight efficient allocation strategies. Despite limitations, the study optimally resolved three instances, achieving a remarkable 38.54% reduction in overall vessel turnaround time compared to FCFS (First-Come-First-Serve) allocation. By prioritizing port turnaround time, the optimization substantially reduced berthing and departure delays, aligning with UNCTAD's call for enhanced port efficiency and accelerated decarbonization efforts.

An Examination of Machine Learning Methods for Predicting Station Departure Delay Time Based on Actual Stopping Time Data of Trains at Stations

An Examination of Machine Learning Methods for Predicting Station Departure Delay Time Based on Actual Stopping Time Data of Trains at Stations

WAYS OF INCREASING THE RELIABILITY OF CONTAINER TRANSPORTATION IN INTERNATIONAL COMMUNICATION IN CONDITIONS OF WAR

The article proposes an improved automated technology for transporting goods by rail in international traffic, taking into account the likelihood of delays in ship departure from the seaport, resulting in additional costs in the form of penalty sanctions. It is known that the majority of goods arriving at seaports for further transportation are in containers. Therefore, in this work we consider and improve the part of the intermodal transportation route associated with raand seaports. To achieve this, it is necessary to adopt a systematic approach, which will allow describing all events occurring during the organization of such a complex process within one probabilistic framework and also consider the operation of each type of transport, especially in conditions of war where such probabilistic events become increasingly prevalent.In this research, the objective function of the intermodal transportation system is formed when interacting with railway stations and ports as the total specific costs of container transportation, taking into account the throughput capacity of the transportation infrastructure and considering the probability of the risk of delayed ship departure due to the arrival delay of the necessary batch of containers.Implementing this technology in automated workplaces of operational personnel will reduce the impact of the «human factor» on decision-making during the organization of rail transportation and enable quick response in an emergency, thereby enhancing the reliability of transportation and the probability of their execution within the planned time frame, especially in wartime conditions.The implementation of this model has shown that the cost of transporting one container to the port by container train from the terminal will reduce specific expenses by approximately 3 % compared to the existing technology. The optimal accumulation time for containers at terminal stations will be 5 and 5.6 hours respectively.

PREDICTION OF FUTURE FLIGHT DELAYS BASED ON CURRENT DATA ANALYSIS USING MACHINE LEARNING

Airplane transportation has many advantages compared to other means of transportation, where this means of transportation is able to cover long distances in a short time. Besides having advantages, of course, air transportation often experiences flight delays which can be caused by several things, including delayed flights due to late arrival (Arrival Delay), delays of the airline itself (Carrier Delay), previously late arrivals (Late Aircraft Delay), air traffic congestion (Nas Delay), security issues (Security Delay) and weather conditions (Weather Delay) The tests carried out were 101,315 US flight delay data from 2017 to 2022. By using the Machine Learning method, the results obtained were that the largest flight delays were caused by late arrivals, namely 386,124,672, where the largest flight delays were by airlines. The airline, namely Southwest Airlines, is 61,474,379 of the total airlines, which is 20, and the biggest departure delay is at Chicago O'Hare International airport, which is in the city of Chicago, IL province, which is 20,912,928 of the total airport, which is 596. This research aims to predict future flights by analyzing current flight data, so that future flights can be better by overcoming or avoiding previous flight delay problems.

Quasi-Stochastic Departure Metering Model Considering Non-Deterministic Taxi Time, Standard Instrument Departure Track Time, and a New Delay–Conflict Relationship

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal flight departure schedule and sequence at airports. The proposed formulation addresses two key gaps in the existing departure metering strategies: (i) the variability and effects of the standard instrument departure (SID) procedure and (ii) the prospect of en route conflicts because of departure delays. The variability and effects of the SID procedure are captured through a non-deterministic parameter defined as the SID track time. The probability of en route conflicts because of departure delay is estimated based on a new delay–conflict relationship. Data used for implementation and validation of the model was collected from operations on Runway 7 of Chennai International Airport, India. Overall results indicate that the proposed departure metering model can act as a pre-emptive departure delay strategy framework that takes care of the SID procedure and en route conflict possibility with no harm to the runway throughput. The implications of this work hint toward better management of the airport operations and the performance of the overall air traffic management system.

COORDINATED MANAGEMENT OF AIRCRAFT DEPARTURES TO ENSURE THE SAFETY OF THEIR MOVEMENT AT TRANSPORT HUB

The task is to coordinate the timing of departure from different airports of a large number of aircraft in order to eliminate cases of their dangerous approach in busy transport hub of a group flight. The conflict management function involves reducing the risk of an aircraft colliding with a source of danger to an acceptable level. A conflict situation is any situation involving an aircraft and a source of danger in which existing separation minima may be violated. The formulation of the problem of optimizing the delay time in the departure of aircraft when they approach at transport hubs is formulated and its solution is proposed on the basis of multiplicative convolution according to the criterion of maximum passenger comfort. It is required to form a single criterion for passenger comfort during takeoff and in flight itself, as well as to propose an algorithm for optimizing the departure delay times of a group of aircraft. When assessing comfort, penalty functions for delay in departure, the risk of unsafe approach of ships at transport hubs, and the factor of undesirable maneuvering when changing the flight level and flight speed are taken into account. An algorithm has been developed for the optimal four-alternative assignment of the delay time in the departure of each aircraft, taking into account the parameters of its approach to other aircraft. The examples of calculation and the results of comparing the optimal solution with the known approach are given. A gain has been established in significantly reducing the duration of the “conflict rapprochement”.

Probabilistic Pretactical Arrival and Departure Flight Delay Prediction with Quantile Regression

Airports plan their resources well in advance based on anticipated traffic. Currently, the only traffic information accessible in the pretactical phase is the flight schedules and historical data. In practice, however, flights do not always depart or arrive on time for a variety of reasons, such as air traffic flow management or reactionary delays. Because neither air traffic flow management regulations nor aircraft rotations are known during the pretactical phase, predicting the precise arrival and departure delays of individual flights is challenging given current technologies. As a result, probabilistic flight delay predictions are more plausible. This paper presents a machine learning model trained on historical data that learned the various quantiles of the departure and arrival delay distributions of individual flights. The model makes use of input features available during the pretactical phase, such as the airline, aircraft type, or expected number of passengers, to provide predictions of the delay distribution several days before operations. The performance of the model trained on operational data from Geneva airport is compared to a statistical baseline, providing evidence that machine learning is superior. Furthermore, the contribution of the various input features is quantified using the Shapely method, stressing the importance of the expected number of passengers. Finally, some practical examples are presented to illustrate how such a model could be applied in the pretactical phase.

Prediction of flight departure delays caused by weather conditions adopting data-driven approaches

In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and infrastructure available to support it. In addition, abnormal weather patterns caused by climate change contribute to the frequent occurrence of flight delays. In light of the extensive network of international flights covering vast distances across continents and oceans, the importance of forecasting flight delays over extended time periods becomes increasingly evident. Existing research has predominantly concentrated on short-term predictions, prompting our study to specifically address this aspect. We collected datasets spanning over 10 years from three different airports such as ICN airport in South Korea, JFK and MDW airport in the United States, capturing flight information at six different time intervals (2, 4, 8, 16, 24, and 48 h) prior to flight departure. The datasets comprise 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW, respectively. We employed a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory, to predict flight delays. Our models achieved accuracy rates of 0.749 for ICN airport, 0.852 for JFK airport, and 0.785 for MDW airport in 2-h predictions. Furthermore, for 48-h predictions, our models achieved accuracy rates of 0.748 for ICN airport, 0.846 for JFK airport, and 0.772 for MDW airport based on our experimental results. Consequently, we have successfully validated the accuracy of flight delay predictions for longer time frames. The implications and future research directions derived from these findings are also discussed.

Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction

Accurate and interpretable delay predictions are vital for decision-making in the aviation industry. However, effectively incorporating spatiotemporal dependencies and external factors related to delay propagation remains a challenge. To address this challenge, we propose the SpatioTemporal Propagation Network (STPN), a novel space-time separable graph convolutional network that models delay propagation by considering both spatial and temporal factors. STPN uses a multi-graph convolution model that considers both geographic proximity and airline schedules from a spatial perspective, while employing a multi-head self-attention mechanism that can be learned end-to-end and explicitly accounts for various types of temporal dependencies in delay time series from a temporal perspective. Experiments on two real-world delay datasets show that STPN outperforms state-of-the-art methods for multi-step ahead arrival and departure delay prediction in large-scale airport networks. Additionally, the counterfactuals generated by STPN provide evidence of its ability to learn explainable delay propagation patterns. Comprehensive experiments also demonstrate that STPN sets a robust benchmark for general spatiotemporal forecasting. The code for STPN is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Kaimaoge/STPN</uri> .

A graph multi-attention network for predicting airport delays

Predicting airport delays is of great importance for aviation operations, from the development of effective air traffic management strategies to the reallocation of airline resources. In this paper, the long-term prediction of the next 24 h of network-wide delays is investigated. The sensitivity to error propagation over long time periods as well as dynamic spatial correlations and non-linear temporal correlations of the aviation network are considered. An external impact modeling module is introduced to account for the influence of weather on flight delay patterns. A Spatial-Temporal Gated Multi-Attention Graph Network (STGMAGNet) considering external impact to predict airport delays is then developed. We validate our model on a flight delays dataset collected from the Bureau of Transportation Statistics of US, covering January 1, 2019, to December 31, 2019. In long-term (input-24-predict-24 setting) forecasting, STGMAGNet provides state-of-the-art accuracy, with a MAE reduction of at least 21% averaged in arrival delay prediction, 18% averaged MAE reduction in departure delay prediction compared to MLP, LSTM, Seq2Seq and Transformer. Our model can enable aviation management to shift from a reactive to proactive approach, thus enhancing its operational efficiency and overall performance.

Prediction of departure delays at original stations using deep learning approaches: A combination of route conflicts and rolling stock connections

Rolling stock connections and route conflicts of trains at terminal stations are critical to train delay propagation and prediction on the railway networks. Previous studies primarily focused on delay prediction/propagation from a macro perspective, without considering the two factors. To address this problem, a hybrid neural network architecture, called TLF-net, is proposed to predict departure delays at original stations (DDOSs), considering the rolling-stock connection and potential route conflicts in the railway network. TLF-net consists of a transformer, a long short-term memory (LSTM), and a fully-connected neural network (FCNN) block, to separately address variables with different characteristics. Based on the real-world data from two terminal stations in China, the experimental results show that the consideration of the potential route conflicts from the network can considerably improve TLF-net's prediction performance over the model that only considering train interactions on a single railway line. Also, it is proven that arrival/departure routes of consecutive trains are crucial for delay prediction, while using the transformer block can efficiently reveal the route conflict severity between arrival/departure routes. The sensitivity analysis to influence factors demonstrates the significance of considering the rolling stock connection and potential route conflicts. Finally, to support real-time railway traffic management, a train arrival delay prediction model from our previous study is integrated to predict the input of TLF-net (i.e., train arrival delays), enabling the proposed model to dynamically predict the DDOSs. This dynamic updating lengthens the prediction horizon (the prediction time ahead), making it better support real-time train traffic control and management.

Delays in the Landing Process When Loading at Gurimbang Jetty Impact of Financial 101 Barge Bill Leaking Due to Anchor Chain Friction

In the incident of a tugboat approaching the dock at Gurimbang Jetty, they experienced friction between the barge hull and the anchor chain, resulting in a hull breach in the barge belonging to PT. Mitra Bahtera Segara Sejati's branch in Berau, East Kalimantan. Consequently, the coal loading activity faced departure delays. Therefore, as a ship owner company, they are responsible for all vessel-related activities from before entering the Port until the vessel departs. The aim of this research is to identify the causes of the barge leakage during coal loading at the dock and the efforts made by PT. Mitra Bahtera Segara Sejati's branch in Berau to address the issue. The research method employed in this study is a qualitative descriptive research method. The research data consists of primary and secondary data collected through data processing from observations, interviews, and documentation with the branch office of PT. Mitra Bahtera Segara Sejati. The data was then analyzed by conducting daily operational reports provided by the ship's personnel. At the same time, interviews were conducted to obtain more in-depth information regarding the daily operational reports and to understand the efforts to address the issues. The research findings indicate that the leakage of the barge significantly affects the company, leading to increased costs for docking services and a decrease in the monthly target of coal long towing from 4 to 3 due to the barge's hull leakage. Efforts have been made to prevent barge hull leakage by conducting regular inspections, routine monitoring, and coordination with relevant parties to address the issue and enhance the safety and operational performance of the vessel.

Improving flight delays prediction by developing attention-based bidirectional LSTM network

Recently, the significance of accurate aircraft delay forecasting has grown in the aviation sector, which caused multi-billion-dollar losses faced by airlines and airports and passenger loyalty losses. Due to the importance of accurate flight delay prediction for all stakeholders involved, the aviation sector seeks to develop techniques for more robust flight delay prediction. Is quickly becoming an important research issue to improve airport and airline service performance and offer customers dependable travel itineraries. Machine learning techniques have been used in a number of studies to evaluate and resolve issues with flight delay prediction. This paper proposes a framework integrated network called ‘Attention-based Bidirectional long short-term memory’ (ATT-BI-LSTM) for flight delay prediction. The Bidirectional LSTM model extracts the spatial and temporal of the flight network with weather features. The ‘Attention mechanism’ has been proposed to enable the model to discover significant and discriminating features that contribute to categorization. The first stage of the proposed framework is the ‘preprocessing of dataset’ which is performed through two steps. The first step is data transformation using MinMax scaler to reduce the variation in the data. The second step is ‘balancing the dataset’ using SMOTE technique for balancing data. The second stage is the establishment of the ATT-BI-LSTM network through the deep tuning experiments of network structure to identify the best combination of parameters and network architecture. To validate the performance of the proposed framework, a wide network of US domestic flights tested in two scenarios. In Scenario 1, the objective is to predict the delay of flight arrival and departure, by using basic flight features with the weather. In Scenario 2, the objective is to predict the delay of flight arrival, by using basic flight features with departure delays. Simulation results show that in scenario 1, the training accuracy of flights’ delay is 88% in both flight delay arrivals and departure, and the testing accuracies of flights’ delay 83% and 82% in departure and arrival respectively. On the other hand, in scenario 2, testing accuracies are 94.30% and 93.71% in the two datasets respectively. The simulation results show that the ATT-BI-LSTM model outperforms other models found in the literature. Therefore, the developed ATT-BI-LSTM framework can contribute strongly to mitigating flight delays by providing a high accuracy prediction system in real-time monitoring to airport and aviation authorities.

Impact of cost approximation on the efficiency of collaborative regulation resolution mechanisms

Air Traffic Flow Management (ATFM) measures are often used to alleviate capacity demand imbalances. Usually, these measures impose a departure delay on flights crossing the airspace in question, where delay is assigned on a “First Planned First Served” (FPFS) base, which minimises total delay. Since delays typically have a different impact on individual flights in terms of cost, a procedure based on cost minimisation could reduce delay-related costs. User-Driven Prioritisation Process (UDPP) is developing a set of solutions aimed at allowing airlines to rearrange their flights within their own slots assigned by the FPFS rule. Inter-airline cost reducing approaches are still missing, but several works have been launched in this direction, using either central optimisations or market-based mechanisms.We analyse the impact of cost approximations procedures, integral part of certain inter-airline mechanisms, overall likely to be used by airlines or the Network Manager (that manages ATFM measures). Using cost models and simulations to collect the true cost of delay profiles, we show that the impact of cost approximations have been severely underestimated, leaving little room for new mechanisms to improve over UDPP. Moreover, we show that the errors made by the airlines on their own costs, expected at least for some airlines, further deteriorate the situation, including UDPP. However, we find that approximation procedures create a strong resilience to these errors, showing how both UDPP and inter-airline procedures may benefit from not having the airlines communicate their detailed costs. Thus, we find that any design of new mechanisms could include a cost approximation procedure in order to increase its resilience.

Flight departure delay forecasting

The most accurate methods to predict delays have become essential to mitigate their generation and proliferation (and prevent their spread to other airports in the network). Given that both the delay time dependence function and the input variables on which it depends are not fully known, the objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. The novel contribution of this work is, first, to make a comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented (ten in total) and, secondly, to determine the coefficients of the importance of the features or flight attributes. Using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. The data for the analysis was obtained from the Colombian airport system for the year 2018. From the results obtained, it is worth mentioning that the model that presents the best performance is the Ensemble, or combination method of Random Forest Regressor models, configured with 2,000 trees within the forest, with an acceptable prediction range measured with the root-mean-square-error (RMSE) metric of 16 to 33 minutes (prediction of time of the flight departure delay) depending on the scenario analysed.

Modeling non-structural strategies to reduce pedestrian evacuation times for mitigating local tsunami threats in Guam

Reducing the potential for loss of life from local tsunamis is challenging for emergency managers given the need for self-protective behavior of at-risk individuals within brief windows of time to evacuate. There has been considerable attention paid to discussing the use of tsunami vertical-evacuation structures for areas where there may be insufficient time to evacuate. This strategy may not be feasible or needed for at-risk populations in island communities for multiple reasons. We examine the influence of three non-structural interventions (reducing departure delays, increasing travel speeds, and managing vegetation to create new paths) that may improve the evacuation potential for at-risk individuals in island communities and use the United States territory of Guam as our case study. We model pedestrian travel times out of a modeled inundation zone for a local tsunami generated by a Mw 8.3 earthquake within the Mariana subduction zone. Evacuation-modeling results indicate that reducing departure delays has a larger impact than increasing travel speeds or creating evacuation corridors through heavy brush on reducing the number of at-risk individuals with insufficient time to evacuate. Travel times to safety are less than wave-arrival times for almost all at-risk individuals in the tsunami-hazard zone if one assumes all three interventions are implemented.

Manajemen Komunikasi Pelayanan Jasa Tour Dan Travel Manca Negara di PT. Zafa Mulia Mandiri Wilayah Bengkulu

The purpose of this study is to describe or find out how PT Zafa Mulia Mandiri Bengkulu Region manages communication in order to serve consumers during departure delays and after being allowed to return to depart for Umrah. This study uses a qualitative approach with a case study type of research. This research focuses intensively on a particular object that is studied as a case. Data collection techniques in this study were (1) interviews, (2) observation, and (3) documentation. Data analysis techniques in this study were (1) data reduction, (2) data presentation, and (3) verification/conclusion. The results of the study show that (1) the company determines the criteria for consumers who are healthy and have financial ability, (2) the company prioritizes service to consumers, (3) the company conducts interviews during registration to find out current conditions, (4) the company provided services before the pandemic, during a pandemic, and after a pandemic in accordance with applicable regulations, (5) the company informs about delays in departure, due to current conditions, (6) when it is permitted to return pilgrims, in terms of service, the company prioritizes consumers whose departure is delayed, and (7 ) The company is moving quickly in response to the current situation, namely by dispatching pilgrims as soon as permission to depart has been obtained. The research results are based on the Management by Objectives (MBO) concept which includes: (1) understanding the desired goals, (2) finding facts about current conditions, (3) comparing the desired goals with existing facts, (4) looking for causes of problems, (5) having alternative solutions to problem solving, (6) alternative decisions by setting decision criteria, and (7) making decisions that have been determined.

Departure delay prediction and analysis based on node sequence data of ground support services for transit flights

Delays frequently occur during ground support services for transit flights. Due to the limitation of related data acquisition, the delay occurred during this process has seldom been explored. Toward the purpose of departure delay mitigation, the present research provides the node sequence data to identify the critical ground support service nodes that associate with departure delay. Machine learning techniques are used for accurate departure delay prediction of transit flights. First, the topological structure of the neural network is constructed according to the ground service support procedure of transit flights. The flight data from Shanghai Pudong and Kunming Changshui International airports are collected for delay description and model evaluation. Then, similarity of delay scenarios are illustrated and the typical delay patterns are explored based on clustering methods. It is found that there are some node sections that delays routinely occur. Third, a Bayesian network is constructed for delay probability analysis. The conditional probability table describing the impact of delay occurred during each section on the departure delay for transit flights is provided. Finally, the performance of two classes of approaches to predict departure delays are constructed and compared, including three traditional machining learning methods denoted as random forest, decision tree, XGBoost models, and four graph convolutional neural network models namely Graph convolutional network (GCN), Graph attention network (GAT), Graph sample and aggregate (GraphSAGE) and integrated Graph convolutional network and Graph sample and aggregate (GCN-GraphSAGE) models. In general, the proposed GCN-GraphSAGE method achieves the most stable and satisfactory performance for departure delay prediction. The research results can support the dynamic and accurate prediction of departure delays and the refined management of transit flights for delay alleviation.

Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model

Some delay patterns are correlated to historical performance and can reflect the trend of delays in future flights. A typical example is the delay from an earlier inbound flight causing delayed departure of a connecting and downstream outbound flight. Specifically, if an arriving aircraft arrives late, the connecting airline may decide to wait for connecting passengers. Due to the consistent flow of passengers to various destinations during a travel season, similar delay patterns could occur in future days/weeks. Airlines may analyze such trends days or weeks before flights to anticipate future delays and redistribute resources with different priorities to serve those outbound flights that are likely to be affected by feeder delays. In this study, we use a hybrid recurrent neural network (RNN) model to estimate delays and project their impacts on downstream flights. The proposed model integrates a gated recurrent unit (GRU) model to capture the historical trend and a dense layer to capture the short-term dependency between arrival and departure delays, and, then, integrates information from both branches using a second GRU model. We trained and tuned the model with data from nine airports in North, Central, and South America. The proposed model outperformed alternate approaches with traditional structures in the testing phase. Most of the predicted delay of the proposed model were within the predefined 95% confidence interval. Finally, to provide operational benefits to airline managers, our analysis measured the future impact of a potentially delayed inbound feeder, (PDIF) in a case study, by means of identifying the outbound flights which might be affected based on their available connection times (ACTs). From an economic perspective, the proposed algorithm offers potential cost savings for airlines to prevent or minimize the impact of delays.