Delay Prediction Research Articles

The uneven geography of US air traffic delays: Quantifying the impact of connecting passengers on delay propagation

Sustained airport congestion periods translate into delays, especially in hub-and-spoke networks in which delay propagation is more evident.We examine the impact of connecting passenger arrival delays on network delay propagation by using passenger level data combined with flight delay data that allow us to analyse the correlation between delayed incoming flights and departure delays at the 21 U.S. airports with most delays, in July 2018.Results show that correlation between daily arrival delays and daily carrier induced departure delays are statistically significant only for flights carrying high proportions of connecting passengers. Correlation values are also higher for short-to-moderate arrival delays. In addition, a Neural Network model was trained for six major airports to build a delay prediction model and map the potential delay propagation. The results of the propagation scenarios suggest that the presence of a unique dominant carrier at an airport translates into a stronger correlation between arrival and carrier delays than that at airports where different carriers compete for connecting passengers. Furthermore, airline hubs located near the areas of the network with more traffic density, independently of the hub's volume of traffic, are more likely to propagate the delay than hubs located in the periphery. The results of this study can be relevant for airline, airport, and traffic control policies aimed at mitigating airport and network congestion.

Localized ignition delay prediction from temperature measurements of n-heptane in the combustion chamber of the ignition quality tester

The constant-volume combustion chamber apparatus, known as the Ignition Quality Tester (IQTTM), is used in some refineries to measure the ignition delay (ID), the time between fuel injection and fuel auto-ignition, of compression ignited fuels using pressure measurements, and to calculate the derived cetane number (DCN) from correlations developed with the cetane number (CN). The IQTTM is also used in research due to its capability to manipulate combustion controller parameters, including pressure, temperature, oxygen content and mass of fuel injection. In this study, in addition to the traditional global pressure measurements, the temperature was recorded at 46 locations inside the IQT chamber during fuel (n-heptane) injection and combustion. Two sets of experiments were conducted: n-heptane injection into nitrogen (no chemical reaction or combustion), and n-heptane injection into air (chemical reaction or combustion) and the ranges of ambient air temperatures and pressures were 530–590 °C and 10 – 21.4 bar, respectively. A method developed to estimate the physical and total ignition delay based on the temperature measurements is introduced in this study. The combustion initiation location was predicted from the results of the ignition delay, and it was determined that during long periods of ID, the fuel–air mixture becomes pseudo-homogeneous and the combustion initiates everywhere in the main part of the IQT chamber. At higher pressure and temperature conditions, the combustion was initiated at the end of the main part of the chamber. The validation of the ID by temperature method showed good agreement with the predicted ID from pressure measurements. The charge air temperature did not show a significant effect on the average percentage of the total physical ignition delay, while the charge pressure has a significant effect on the average percentage of the total physical ID, which increases with the charge pressure.

A Hybrid LSTM-CPS Approach for Long-Term Prediction of Train Delays in Multivariate Time Series

In many big cities, train delays are among the most complained-about events by the public. Although various models have been proposed for train delay prediction, prior studies on both primary and secondary train delay prediction are limited in number. Recent advances in deep learning approaches and increasing availability of various data sources has created new opportunities for more efficient and accurate train delay prediction. In this study, we propose a hybrid deep learning solution by integrating long short-term memory (LSTM) and Critical Point Search (CPS). LSTM deals with long-term prediction tasks of trains’ running time and dwell time, while CPS uses predicted values with a nominal timetable to identify primary and secondary delays based on the delay causes, run-time delay, and dwell time delay. To validate the model and analyse its performance, we compare the standard LSTM with the proposed hybrid model. The results demonstrate that new variants outperform the standard LSTM, based on predicting time steps of dwell time feature. The experiment results also showed many irregularities of historical trends, which draws attention for further research.

머신러닝(Machine Learning) 기법을 활용한 제주국제공항의 운항 지연과의 상관관계 분석 및 지연 여부 예측모형 개발

Due to the recent rapid increase in passenger and cargo air transport demand, the capacity of Jeju International Airport has been approaching its limit. Even though in COVID-19 crisis which has started from Nov 2019, Jeju International Airport still suffers from strong demand in terms of air passenger and cargo transportation. However, it is an undeniable fact that the delay has also increased in Jeju International Airport. In this study, we analyze the correlation between weather and delayed departure operation based on both datum collected from the historical airline operation information and aviation weather statistics of Jeju International Airport. Adopting machine learning techniques, we then analyze weather condition Jeju International Airport and construct a delay prediction model. The model presented in this study is expected to play a useful role to predict aircraft departure delay and contribute to enhance aircraft operation efficiency and punctuality in the Jeju International Airport.

Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet

The next-generation cellular systems, including fifth-generation cellular systems (5G), are empowered with the recent advances in artificial intelligence (AI) and other recent paradigms. The internet of things (IoT) and the tactile internet are paradigms that can be empowered with AI solutions and integrated with 5G systems to deliver novel services that impact the future. Machine learning technologies (ML) can understand examples of nonlinearity from the environment and are suitable for network traffic prediction. Network traffic prediction is one of the most active research areas that integrates AI with information networks. Traffic prediction is an integral approach to ensure security, reliability, and quality of service (QoS) requirements. Nowadays, it can be used in various applications, such as network monitoring, resource management, congestion control, network bandwidth allocation, network intrusion detection, etc. This paper performs time series prediction for IoT and tactile internet delays, using the k-step-ahead prediction approach with nonlinear autoregressive with external input (NARX)-enabled recurrent neural network (RNN). The ML was trained with four different training functions: Bayesian regularization backpropagation (Trainbr), Levenberg–Marquardt backpropagation (Trainlm), conjugate gradient backpropagation with Fletcher–Reeves updates (Traincgf), and the resilient backpropagation algorithm (Trainrp). The accuracy of the predicted delay was measured using three functions based on ML: mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Acceleration of Chemical Kinetics Computation with the Learned Intelligent Tabulation (LIT) Method

In this work, a data-driven methodology for modeling combustion kinetics, Learned Intelligent Tabulation (LIT), is presented. LIT aims to accelerate the tabulation of combustion mechanisms via machine learning algorithms such as Deep Neural Networks (DNNs). The high-dimensional composition space is sampled from high-fidelity simulations covering a wide range of initial conditions to train these DNNs. The input data are clustered into subspaces, while each subspace is trained with a DNN regression model targeted to a particular part of the high-dimensional composition space. This localized approach has proven to be more tractable than having a global ANN regression model, which fails to generalize across various composition spaces. The clustering is performed using an unsupervised method, Self-Organizing Map (SOM), which automatically subdivides the space. A dense network comprised of fully connected layers is considered for the regression model, while the network hyper parameters are optimized using Bayesian optimization. A nonlinear transformation of the parameters is used to improve sensitivity to minor species and enhance the prediction of ignition delay. The LIT method is employed to model the chemistry kinetics of zero-dimensional H2–O2 and CH4-air combustion. The data-driven method achieves good agreement with the benchmark method while being cheaper in terms of computational cost. LIT is naturally extensible to different combustion models such as flamelet and PDF transport models.

Investigation on the ignition delay prediction model of multi-component surrogates based on back propagation (BP) neural network

The ignition delay prediction model of three-component surrogates was established based on the back propagation (BP) neural network. The ambient temperature, ambient pressure, molar fraction of n-heptane, iso-octane and toluene were utilized as the input parameters. The ignition delay was utilized as the output parameter. The training and validation set only contained the 0-D simulation ignition delay of single- and two-component surrogates. But the trained BP neural network also showed a strong predictive ability towards the ignition delay of three component toluene primary reference fuel (TPRF) surrogates. Results show that the BP neural network with two hidden layers performs better than that with single hidden layer. With the optimization of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, the correlation coefficient is higher than 0.9996. The mean relative error (MRE) and the mean square error (MSE) are also maintained at a low level. The computational costs of 0-D simulation and BP neural network methods are compared. In 0-D simulation, the computational time of one case is 28 s. When the BP neural network is utilized, the computational time of 176 cases is just 3.2 s, which shows a significant improvement in the computation time. Through the mean impact value (MIV) analysis, the significance of each input variable to the output results is investigated. The input parameters of ambient temperature and molar fraction of iso-octane obtain the highest and lowest absolute value of MIV, respectively, indicating the major and minor effects on the ignition delay. Based on the predicted ignition delay of three-component TPRF surrogates, the research octane number (RON) and motor octane number (MON) can also be accurately predicted with the maximum deviance no more than 3 units. For real fuels of fuels for advanced combustion engines (FACE) gasolines, such as FACE A and FACE C gasolines, the surrogate fuel which has the same ignition delay at the specific pressure and temperature can be determined through the construction of the ignition delay look-up table of TPRF surrogates by the BP neural network. Following this method, the ignition delay of the real fuels can be matched accurately and the molar fraction of each component of the corresponding TPRF surrogates can also be acquired.

Risk Prediction of Delay in the Execution of Public Works Through Fuzzy Logic

In Manaus, delays in public constructions are not uncommon as their execution deadlines are often extrapolated, even though such deadlines are obtained through preliminary technical studies. The causes that give rise to such delays are varied ranging from the occurrence of rain to the addition of quantities of existing services or additions of new services. In this research, we sought to obtain a model, using fuzzy logic, to predict the risk of delay that some variables may cause in the period of execution of the work, allowing the public administration or the contracted company to adopt measures they consider essential to mitigate this delay. Initially, documentary and bibliographic research were carried out to identify the causes that most contribute to the occurrence of delays in the works. Once these causes were identified, the construction of the fuzzy inference system was started, with six of the most significant causes identified in the research being considered as linguistic variables, namely: the hiring factor, which corresponds to the quotient between the value of the proposal. of the company and the amount budgeted by the administration; the value of the work, which is the value of the contract for the work; the engineering execution drawing, which are the engineering drawings used in the works; the alteration of quantities, which are changes in the quantities of existing services or addition of new services; authorization from public agencies, which corresponds to the permission or support of any public agency or public company for the execution of the work; and the rain. For the simulation of the created fuzzy inference system, real data from four public works were entered, and the answers of this simulation were satisfactory because they are confirmed by the documentation of the respective works. It is concluded that the system proved to be useful, as it was possible to predict the risk of delay in the execution of public works in the city of Manaus, and it can be used by both the public administration and the contractor to mitigate the causes of delays in the execution of public works.

Using the gradient boosting decision tree (GBDT) algorithm for a train delay prediction model considering the delay propagation feature

Accurate prediction of train delay is an important basis for the intelligent adjustment of train operation plans. This paper proposes a train delay prediction model that considers the delay propagation feature. The model consists of two parts. The first part is the extraction of delay propagation feature. The best delay classification scheme is determined through the clustering method of delay types for historical data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN), and combining the best delay classification scheme and the k-nearest neighbor (KNN) algorithm to design the classification method of delay type for online data. The delay propagation factor is used to quantify the delay propagation relationship, and on this basis, the horizontal and vertical delay propagation feature are constructed. The second part is the delay prediction, which takes the train operation status feature and delay propagation feature as input feature, and use the gradient boosting decision tree (GBDT) algorithm to complete the prediction. The model was tested and simulated using the actual train operation data, and compared with random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP). The results show that considering the delay propagation feature in the train delay prediction model can further improve the accuracy of train delay prediction. The delay prediction model proposed in this paper can provide a theoretical basis for the intelligentization of railway dispatching, enabling dispatchers to control delays more reasonably, and improve the quality of railway transportation services.

Applied artificial intelligence for predicting construction projects delay

This study presents evidence of a developed ensemble of ensembles predictive model for delay prediction – a global phenomenon that has continued to strangle the construction sector despite considerable mitigation efforts. At first, a review of the existing body of knowledge on influencing factors of construction project delay was used to survey experts to approach its quantitative data collection. Secondly, data cleaning, feature selection, and engineering, hyperparameter optimization, and algorithm evaluation were carried out using the quantitative data to train ensemble machine learning algorithms (EMLA) – bagging, boosting, and naïve bayes, which in turn was used to develop hyperparameter optimized predictive models: Decision Tree, Random Forest, Bagging, Extremely Randomized Trees, Adaptive Boosting (CART), Gradient Boosting Machine, Extreme Gradient Boosting, Bernoulli Naive Bayes, Multinomial Naive Bayes, and Gaussian Naive Bayes. Finally, a multilayer high performant ensemble of ensembles (stacking) predictive model was developed to maximize the overall performance of the EMLA combined. Results from the evaluation metrics: accuracy score, confusion matrix, precision, recall, f1 score, and Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) indeed proved that ensemble algorithms are capable of improving the predictive force relative to the use of a single algorithm in predicting construction projects delay.

Causal Discovery of Flight Service Process Based on Event Sequence

The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.

Using of an Artificial Neural Networks with Particle Swarm Optimization (ANN-PSO) Model in Prediction of Cost and Delay in Construction Projects

Construction project delay is a global phenomenon. The delay risk being regarded as a main challenge that is tackled via the firms of construction. It possessed an inverse effect upon the performance of the project resulting in cost overruns and productivity reduction. In Iraq, most construction projects surpassed their prearranged time and were delayed, resulting in a loss of productivity and income. The objective of this paper was to predict the cost and delay of construction projects to illustrate their risks effects by using of artificial neural networks with the particle swarm optimization method (ANN-PSO). Thereby, risk factors were identified and analysed using Probability and Impact Analysis which were embraced as the model inputs. In comparison, the outputs for the models were represented by the ratio of the contractor's profit to project costs and the delay in construction projects. An ANN model was additionally evolved with a backpropagation (BP) optimization method to assess the exhibition of the ANN-PSO model. To evaluate the accuracy of the results of the ANN-PSO model, coefficient of correlation (R), determination coefficient (R2), and root mean squared error (RMSE) was utilized as performance evaluation of the models. The ANN-PSO model showed a significant performance in the delay prediction. The performance evaluation for the cost and delay prediction were (R=0.929, R2=0.863, RMSE=0.044), and (R=0.998, R2=0.996, RMSE=0.094), respectively. The model of ANN-PSO has a virtuous performance in the delay prediction better than the cost. However, the ANN-BP model showed better performance than ANN-PSO in term of cost prediction.

19P Evaluation of different pharmacokinetic-pharmacodynamic models for tumor growth delay prediction after chemotherapy

In preclinical studies to rank anticancer agents for their efficacy the delay in tumor growth after impermanent drug exposure is often used. For a more detailed description of the anticancer drug action, mathematical pharmacokinetics/pharmacodynamics (PK/PD) models were developed. Such models allow predicting tumor growth time evolution and particularly the delay for various drug dosages. Each PK/PD model is based on the mathematical formulation of the biological processes underlying the antitumor drug action.

Numerical Study of a Horizontal Wind Turbine under Yaw Conditions

Over recent years, considerable attention has been devoted to the optimization of energy production in wind farms, where yaw angles can play a significant role. In order to quantify and maximize such potential power, the simulation of wakes is vital. In the present study, an actuator line model code was implemented in the OpenFOAM flow solver. A tip treatment was applied to involve the tip effect induced by the pressure equalization from the suction and pressure sides. The Leishman–Beddoes dynamic stall (LB-DS) model modified by Sheng et al. was employed to consider the dynamic stall phenomenon. The developed ALM-CFD solver was validated for the NREL Phase VI wind turbine reference case. The solver was then used in simulating the yawed wind turbine, and power variation was compared with UBEM and CFD. Overall, according to the obtained data, the coupled solver compared well with CFD. There was an improvement in terms of prediction of the phase delay that is due to the dynamic stall. However, there was still negligible overestimation in deep stall conditions. Based on the obtained results, it is suggested that the reduction of power output follows a cosine to the power of X function of the yaw angle. In terms of visualizing wake, the results demonstrated that the current ALM code was satisfying enough to simulate skewed wake and vortices trajectory. The effect of advancing and retreating blade was captured. It was found that yaw led to the concentration of the induced velocity downstream, resulting in a lower velocity deficit on a broader area, which is essential for wind farm optimization.

Application of Random Forest Algorithm for the Prediction of Online Food Delivery Service Delay

Online shopping industry nowadays has been growing rapidly with the evolution of technology. Consumers have started to shop according to certain criteria with the spread of the online shopping sector. One of the sectors that enlightens the future customer in terms of service quality by getting feedback from purchases (comments or ratings) is the online food sector. In this study, a classification study is conducted to investigate the observance with the fast delivery criteria, which is one of the cornerstone criteria in the online food industry. Random Forest (RF) algorithm is applied for the classification. The most important advantage of the RF is it handles a large number of input variables also it is speediness. More than that RF algorithm reduces the overfitting problem and as a result variance is small and therefore it improves the accuracy. The application is implemented through R programming language. In this study, online food delivery variable is created as two categories (On time or Early and Late) and is estimated by RF algorithm that is applied this data for the first time. According to the results, the correct classification rate of the testing data for the estimation of the online food delivery status variable is found as 95.85%. In addition, the performances of the restaurants are compared for the customers. It turns out that the traffic situation does not greatly affect the result of the delivery status. As a result, RF algorithm is applied to the data obtained by web scraping techniques and the delivery status performance of restaurants is revealed with this study.

Semi-Markov model for delivery delay prediction in multi-copy opportunistic networks with heterogeneous pairwise encounter rates

This work proposes an analytical model based on absorbing semi-Markov process for predicting the expected pairwise delivery delay for single and multi-copy opportunistic forwarding schemes. The framework can be applied to any inter-meeting times distribution. For the exponential case, it only requires the number of nodes and the pairwise encounter rates, which can be completely heterogeneous. Results show that the model predictions are quite accurate, with error values being less than 1% in some cases.

Energy Efficient Localization Through Node Mobility and Propagation Delay Prediction in Underwater Wireless Sensor Network

In Underwater Wireless Sensor Network, for mobile target localization schemes, there exists long propagation delay of sensor nodes' messages. Further, the flexibility of node localization schemes to adapt in harsh underwater environment is the major issue. As propagation delay is one of the principal aspects that disturb the time harmonisation of underwater sensor nodes for communication. It must be adjusted to attain precise and accurate localization. In this article, we propose an energy-efficient localization scheme based on mobility and propagation delay prediction. Precise synchronization time is accomplished by forecasting the long propagation delay. Anchor node mobility prediction procedure is intended to analyse and record its speed at every localization period. Then, the propagation delay is compensated to attain rapid reporting of localization. The ordinary nodes make localization, utilizing the expected speed vectors obtained from the anchor nodes. The proposed scheme is tested in simulator NS-2.30 all-in-one with multiple runs and average of these runs is considered. The replicating outcomes exhibit, that the proposed method increases overall energy efficiency by 16%, localization cost by 26%, and delay by 28%, which are the major concern for localization scheme.

Flight Delay Classification Prediction Based on Stacking Algorithm

With the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection problem of machine learning technology. In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. The second-level learner is Logistic Regression. To verify the effectiveness of the proposed method, comparative experiments are carried out based on Boston Logan International Airport flight datasets from January to December 2019. Multiple indexes are used to comprehensively evaluate the prediction results, such as Accuracy, Precision, Recall, F1 Score, ROC curve, and AUC Score. The results show that the Stacking algorithm not only could improve the prediction accuracy but also maintains great stability.

Graph to sequence learning with attention mechanism for network-wide multi-step-ahead flight delay prediction

The primary objective of this study is to predict network-wide multi-step-ahead flight delay. A novel graph-to-sequence learning architecture with attention mechanism (AG2S-Net) is developed to predict the multi-step-ahead hourly departure and arrival delay of the entire network. The proposed AG2S-Net consists of a graph convolution neural network, a bi-LSTM neural network, and a sequence-to-sequence framework with embedded attention mechanism. Five-year flight data of 75 airports and 242 links are collected from the National Airspace System to illustrate the procedure. First, K-means clustering algorithm is applied to classify the hourly flight delay states of the entire network into four typical delay patterns, which are further used as explanatory variables in prediction model. Then, the network-wide multi-step-ahead flight delay prediction model is built with the proposed AG2S-Net. The result indicates that the model achieves better performance on large-scale airports than small-scale airports. The delay pattern variables and weather variables can greatly improve the prediction performance. In addition, compared with several benchmark methods, the AG2S-Net performs the best for all three airport types in terms of the lowest RMSE and MAE values. Finally, the graph network analysis further reveals that the proposed AG2S-Net can capture the hidden correlations between airports without links and collect information from more airports in the same community to enhance the prediction performance. The results of this study could provide insightful suggestions for aviation authorities and airport regulators to develop effective air traffic control strategies for alleviating flight delays and improving operation efficiency of the entire network.

Modeling bystander effects that cause growth delay of breast cancer xenografts in bone marrow of mice treated with radium-223

Rationale The role of radiation-induced bystander effects in cancer therapy with alpha-particle emitting radiopharmaceuticals remains unclear. With renewed interest in using alpha-particle emitters to sterilize disseminated tumor cells, micrometastases, and tumors, a better understanding of the direct effects of alpha particles and the contribution of the bystander responses they induce is needed to refine dosimetric models that help predict clinical benefit. Accordingly, this work models and quantifies the relative importance of direct effects (DE) and bystander effects (BE) in the growth delay of human breast cancer xenografts observed previously in the tibiae of mice treated with 223RaCl2. Methods A computational model of MDA-MB-231 and MCF-7 human breast cancer xenografts in the tibial bone marrow of mice administered 223RaCl2 was created. A Monte Carlo radiation transport simulation was performed to assess individual cell absorbed doses. The responses of the breast cancer cells to direct alpha particle irradiation and gamma irradiation were needed as input data for the model and were determined experimentally using a colony-forming assay and compared to the responses of preosteoblast MC3T3-E1 and osteocyte-like MLO-Y4 bone cells. Using these data, a scheme was devised to simulate the dynamic proliferation of the tumors in vivo, including DE and BE propagated from the irradiated cells. The parameters of the scheme were estimated semi-empirically to fit experimental tumor growth. Results A robust BE component, in addition to a much smaller DE component, was required to simulate the in vivo tumor proliferation. We also found that the relative biological effectiveness (RBE) for cell killing by alpha particle radiation was greater for the bone cells than the tumor cells. Conclusion This modeling study demonstrates that DE of radiation alone cannot explain experimental observations of 223RaCl2-induced growth delay of human breast cancer xenografts. Furthermore, while the mechanisms underlying BE remain unclear, the addition of a BE component to the model is necessary to provide an accurate prediction of the growth delay. More complex models are needed to further comprehend the extent and complexity of 223RaCl2-induced BE.