scholarly journals An Integrative Approach with Sequential Game to Real-Time Gate Assignment under CDM Mechanism

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Jun-qiang Liu ◽  
Ma-lan Zhang ◽  
Peng-chao Chen ◽  
Ji-wei Xie ◽  
Hong-fu Zuo
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computation Time ◽  
Integrative Approach ◽  
Small Scale ◽  
Flight Delays ◽  
Time Gate ◽  
Sequential Game ◽  
Assignment Model ◽  
Gate Assignment

This paper focuses on real-time airport gate assignment problem when small-scale or medium- to large-scale flight delays occur. Taking into account the collaborative decision making (CDM) of the airlines and the airport, as well as the interests of multiagent (airlines, airports, and passengers), especially those influenced by flight banks, slot assignment and gate assignment are integrated into mixed set programming (MSP), and a real-time gate assignment model is built and solved through MSP coupled with sequential game. By this approach, the delay costs of multiagent can be minimized simultaneously; the fuel consumption of each airline can be basically equalized; the computation time can be significantly saved by sequential game; most importantly, the collaboration of the airlines and the airport is achieved so that the transferring cost caused by the delay of flight banks can be decreased as much as possible. A case study on small-scale flight delays verifies that the proposed approach is economical, robust, timesaving, and collaborative. A comparison of the traditional staged method and the proposed approach under medium- to large-scale flight delays proves that the integrative method is much more economical and timesaving than the traditional staged method.

scholarly journals Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 152
Author(s):  
Micha Zoutendijk ◽  
Mihaela Mitici
Keyword(s):  
Assignment Problem ◽  
Absolute Error ◽  
Aviation Industry ◽  
Probabilistic Forecasting ◽  
Flight Delays ◽  
Mixture Density ◽  
Assignment Model ◽  
Gate Assignment ◽  
Flight Delay ◽  
Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

scholarly journals HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

2018 ◽  
Vol 7 (12) ◽  
pp. 467 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Wenze Luo ◽  
Luo Chen ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
High Performance ◽  
Large Scale ◽  
Computation Time ◽  
Buffer Analysis ◽  
Data Volume ◽  
Time Buffer ◽  
Real World Datasets ◽  
Spatial Indexes

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

scholarly journals Space-time clustering-based method to optimize shareability in real-time ride-sharing

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262499
Author(s):  
Negin Alisoltani ◽  
Mostafa Ameli ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computation Time ◽  
Clustering Method ◽  
Matching Problem ◽  
Solution Quality ◽  
Mobility Service ◽  
Ride Sharing ◽  
Large Scale Problems ◽  
Spatio Temporal

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

Real-Time Public Transport Operations: Library of Control Strategies

2017 ◽  
Vol 2647 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Mahmood Mahmoodi Nesheli ◽  
Avishai (Avi) Ceder
Keyword(s):  
Real Time ◽  
Public Transport ◽  
Large Scale ◽  
Real Life ◽  
Small Scale ◽  
Control Procedure ◽  
Service Reliability ◽  
Real Time Control ◽  
Time Control ◽  
Control Actions

Modern public transport (PT) operations have evolved into a complex multimodal system in which small-scale disorder can propagate. Large-scale disruptions to passengers and PT agencies result. Various studies have been developed to model PT control at the operational level; however, the main downside of possible real-time control actions is the lack of intelligent modeling and a systematic process that can activate such actions immediately. This study presents a real-time control procedure to increase service reliability and to improve successful coordinated transfers in a complex PT system. The developed method aims at minimizing total travel time for passengers and reducing the uncertainty of meetings between PT vehicles. A library of operational tactics is first built to serve as a basis of the real-time decision-making process. The methodology developed is applied to a real-life case study in Auckland, New Zealand. The results showed improvements in system performance and confirmed the use of real-time control actions to maintain reliable PT service.

COMBINED NEURAL-NET/KNOWLEDGE-BASED ADAPTIVE SYSTEMS FOR LARGE SCALE DYNAMIC CONTROL

1991 ◽  
Vol 05 (04) ◽  
pp. 503-522 ◽  
Author(s):  
ALISTAIR D. C. HOLDEN ◽  
STEVEN C. SUDDARTH
Keyword(s):  
Adaptive Systems ◽  
Large Scale ◽  
Dynamic Control ◽  
Back Propagation ◽  
Computation Time ◽  
Neural Nets ◽  
Small Scale ◽  
Neural Net ◽  
Training Time ◽  
Knowledge Based

The control of small-scale systems using either knowledge-based or neural net methods is quite feasible. Large scale systems, however, introduce complexities in modeling and excessive computation time. This paper attacks these difficulties by breaking down the problem into a hierarchy of control contexts. The lowest level of this hierarchy is implemented as rule sets and/or neural networks. A method using "hints" is shown to greatly reduce training time in back-propagation neural nets.

scholarly journals Fast Model Predictive Control Combining Offline Method and Online Optimization with K-D Tree

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Ding ◽  
Zuhua Xu ◽  
Jun Zhao ◽  
Zhijiang Shao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Computation Time ◽  
Online Optimization ◽  
Small Scale ◽  
Worst Case ◽  
Online Searching ◽  
Partial Enumeration ◽  
Speedup Factor

Computation time is the main factor that limits the application of model predictive control (MPC). This paper presents a fast model predictive control algorithm that combines offline method and online optimization to solve the MPC problem. The offline method uses a k-d tree instead of a table to implement partial enumeration, which accelerates online searching operation. Only a part of the explicit solution is stored in the k-d tree for online searching, and the k-d tree is updated in runtime to accommodate the change in the operating point. Online optimization is invoked when searching on the k-d tree fails. Numerical experiments show that the proposed algorithm is efficient on both small-scale and large-scale processes. The average speedup factor in the large-scale process is at least 6, the worst-case speedup factor is at least 2, and the performance is less than 0.05% suboptimal.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

1999 ◽  
Vol 09 (06) ◽  
pp. 1041-1074 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Back Propagation ◽  
Cellular Neural Network ◽  
Small Scale ◽  
Von Neumann ◽  
On Line

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

scholarly journals Log Data Real Time Analysis Using Big Data Analytic Framework with Storm and Hadoop

2018 ◽  
Vol 246 ◽  
pp. 03009
Author(s):  
Jia-Ke Lv ◽  
Yang Li ◽  
Xuan Wang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Knowledge Extraction ◽  
Streaming Data ◽  
Small Scale ◽  
Global Knowledge ◽  
Data Set ◽  
Log Data ◽  
Real Time Processing ◽  
Real Time Analysis

The log data real-time processing platform which is built using Storm On YARN integrated MapReduce and Storm that use MapReduce to complete large-scale off-line data global knowledge extraction, sudden knowledge extraction of small-scale data in Kafka buffers through Storm, and continuous real-time calculation of streaming data in combination with global knowledge. We tested our technique with the well-known KDD99 CUP data set. The experimentation results prove the system to be effective and efficient.

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