Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain)

Ruiz-Aguilar, Juan Jesús; Turias, Ignacio J.; Moscoso-López, José Antonio; Jiménez‐Come, María Jesús; Cerbán, Mar

doi:10.15446/dyna.v83n195.47027

Cited by 12 publications

(3 citation statements)

References 58 publications

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“…One main parameter of kNN is the number of neighbours (k). If k is optimised, kNN can perform better [30]. There are many studies regarding optimisation of k. Ling et al used a gap of the distance sequence to select neighbours where the gap implies the inherent boundary of a small region centred at new query instance.…”

Section: Adaptive Knn Methodsmentioning

confidence: 99%

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Sun

Cheng

Goswami

et al. 2017

IET Intelligent Transport Systems

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Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training for the parameters. A real-world dataset with more than one year traffic records is used to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

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Section: Adaptive Knn Methodsmentioning

confidence: 99%

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Sun

Cheng

Goswami

et al. 2017

IET Intelligent Transport Systems

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“…Forecasting traffic flows of vessels has been recognized as a challenging task in the maritime intelligent transportation system, since it could be affected by various complex factors [1,2]. Accurate and timely traffic information is of significant importance to both maritime managers and individual vessels, as it not only helps the former to better conduct port planning [3,4], alleviate congestion [5,6], mitigate emission of GHG (greenhouse gases) and improve public security [7,8], but also enables the latter to better operate the ship navigation system and plan a route [9,10], so as to avoid collisions and reduce the potential cost due to late arrival [11].…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Forecast Maritime Vessel Flows

Zhou

Liu

Wang

et al. 2020

Sensors

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Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

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“…During last decades, the solution of many traffic problems has been addressed by several forecasting approaches as statistical methods and artificial neural networks (ANNs). ANNs are commonly used to solve non-linear functions as freight flows (Amin et al, 1998;Moscoso-López et al, 2014;Ruiz-Aguilar et al, 2014, 2016Sun et al, 2012;Vlahogianni et al, 2005Vlahogianni et al, , 2004. Recently, Support Vector Machines (SVM) have been applied in solution of forecasting problems in transport field obtained good performance (Bhattacharya et al, 2014;Castro-Neto et al, 2009;Marković et al, 2015)..…”

Section: Introductionmentioning

confidence: 99%

Short-term forecasting of intermodal freight using ANNs and SVR: Case of the Port of Algeciras Bay.

Moscoso-López

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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Forecasting of future intermodal traffic demand is very important for decision making in ports operations management. The use of accurate prediction tools is an issue that awakens a lot of interest among transport researchers. Intermodal freight forecasting plays an important role in ports management and in the planning of the principal port activities. Hence, the study is carried out under the motivation of knowing that modeling the freight transport flows could facilitate the management of the infrastructure and optimize the resources of the ports facilities. The use of advanced models for freight forecasting is essential to improve the port level-service and competitiveness. In this paper, two forecasting-models are presented and compared to predict the freight volume. The models developed and tested are based on Artificial Neural Networks and Support Vector Machines. Both techniques are based in a historical data and these methods forecast the daily weight of the freight with one week in advance. The performance of the models is evaluated on real data from Ro-Ro freight transport in the Port of Algeciras Bay. This work proposes and compares different approaches to determine the best prediction. In order to select the best model a multicomparison procedure is developed using several statistical test. The results of the assessed models show a promising tool to predict Ro-Ro transport flows with accuracy.

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Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain)

Cited by 12 publications

References 58 publications

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Using Deep Learning to Forecast Maritime Vessel Flows

Short-term forecasting of intermodal freight using ANNs and SVR: Case of the Port of Algeciras Bay.

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