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Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy <i>c</i>-Means Clustering
Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale...
Chen, Yongjun;Huang, Ming;Song, Kaixuan;Wang, Tengfei
Hindawi Journal of Advanced Transportation Volume 2023, Article ID 7175863, 12 pages https://doi.org/10.1155/2023/7175863 Research Article Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy c-Means Clustering 1 2,3,4 1 4 Yongjun Chen, Ming Huang , Kaixuan Song, and Tengfei Wang Airport College, Binzhou University, Binzhou 256600, China Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China National Engineering Research Center for Water Transport Safety (WTSC), Wuhan 430063, China School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China Correspondence should be addressed to Ming Huang; email@example.com Received 20 June 2022; Revised 30 October 2022; Accepted 26 November 2022; Published 20 May 2023 Academic Editor: Jing Zhao Copyright © 2023 Yongjun Chen et al. Tis is anopen access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurately predicting short-term congestions in ship trafc fow is important for water trafc safety and intelligent shipping. We propose a method for predicting the trafc fow of ships by applying the whale optimization algorithm to an extreme learning machine. Te method considers external environmental uncertainty and complexity of ships navigating in trafc-intensive waters. First, the parameters of ship trafc fow are divided into multiple modal components using variational mode decomposition and extreme learning machine. Te machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship trafc fow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship trafc congestion. To verify the efectiveness of the proposed method, ship trafc fow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship trafc fow parameters are consistent with measurements. Specifcally, the prediction accuracy of the ship trafc congestion reaches 76.04%, which is reasonable and practical for predicting ship trafc congestion. practitioners, and government managers to develop an 1. Introduction approach for predicting ship trafc fow. Te status of ex- Te booming growth in trade has led to the development of pected ship trafc fow can be used to identify congestion the waterway transportation sector, and ship trafc fow early, improve canal trafc, and prevent accident. prediction has become extremely important with the ever- Methods for predicting trafc fow, which were frst only increasing waterway transport demand. Te Yangtze River, employed for predicting automobile trafc fow, have found as the largest river that runs through the east and west of widespread use now. Xiao et al.  proposed and built China, is the main channel to the sea for 11 inland provinces diferential information from the car-following inertia gray and autonomous regions of China and the main trans- model and gray system based on the classic car-following portation artery connecting the three economic zones in model and trafc status to establish diferential equations. southwest, central, and east part of China. Te Yangtze River Yan et al.  constructed a comprehensive trafc fow in- plays a pivotal role in the country’s economic and social dication system based on the robust L1 model minimum development because of its superior navigable conditions two-multiplier twin support vector regression. Based on and huge transit capacity. Once the ships in the Yangtze a recurrent neural network, Cui et al.  used a two-way/ River are blocked, the cost loss and environmental pollution one-way stacked long short-term memory (LSTM) network will be a great loss. Terefore, it is a goal of researchers, for trafc prediction. Tey combined a recurrent neural 2 Journal of Advanced Transportation vehicle path problem and designed a local search second network and its variants into a trafc prediction method. Li et al.  used the dense geometric correspondence network algorithm for a particular terrain. Mei  applied an ef- fcient clustering algorithm for the k-core decomposition of for multistep trafc prediction. By applying dynamic re- lations between trafc stations in space and time, Peng  large networks. Guo  applied a method for early warning proposed a prediction framework based on a neural network of trafc congestion areas based on dynamic identifcation, to dynamically map urban trafc fow. Cai et al.  derived which can efectively track the target and detect congestion the posttest estimation of the maximum correlation in the warning areas throughout the congestion evolution. Gao fxed embedding algorithm update such that a Kalman flter  applied a method to quantify the degree of trafc provides suitable trafc fow prediction. Wang et al.  congestion, constructed an image-based trafc congestion assessment framework, and integrated a trafc parameter combined trafc fow data and considered weather condi- tions to build a short-term trafc fow prediction model layer into a basic convolutional neural network that accel- erates processing and avoids complex postprocessing. Zhou based on an attention mechanism and a one-dimensional convolutional neural network with LSTM. Fang et al.   proposed a clustering integration method based on structured hypermap learning to improve the clustering found that a typical LSTM network is prone to small fuc- tuations and can achieve high-accuracy trafc fow pre- efciency, stability, and robustness. diction. Shen et al.  performed trafc fow prediction Nguyen  used clustering to automatically label data, considering multi-intersection perception. Lu et al.  qualitatively assess the signifcance of the labels, and performed isolated-point short-term trafc fow prediction quantify the efect of label separation results in the feature based on a time-aware convolutional context block LSTM space. Costa  proposed an unsupervised approach for network and a novel loss switching mechanism. Li et al.  relating topic modelling and document clustering, seam- lessly unifying and jointly performing both tasks using performed deep feature learning by applying an advanced multitarget particle group optimization algorithm to the Bayesian generative modelling and post-ambiguous rea- soning. As a result, a method was derived to estimate the parameters of a deep belief network and by applying su- pervised learning to predict short-term trafc fow. Zhang congestion index using a speed transfer matrix and the corresponding center of mass. Te congestion index is es- et al. [12–14] proposed convolutional neural networks based on dynamic feature coding. Considering the selection of timated using fuzzy reasoning optimized by genetic algo- historical data, Zhang  proposed a selection method to rithms. Te index is evaluated using data from a receiver of collect appropriate historical data for daily trafc fow global satellite navigation system, and trafc status estimates prediction. can be obtained for most evaluated roads. Peixoto  Salamanis  proposed an efcient large-scale multi- devised clustering analysis for reducing trafc information at step trafc prediction model with fast inference. Almeida the edge of a vehicle network. Peter  applied the RSRU_TM method for improving trafc management.  used statistical algorithms and neural networks to de- scribe and predict trafc fow. Lee and Rhee  identifed Based on mobile crowd sensing for dynamic trafc efciency estimation, Ali  applied an algorithm for trafc con- two basic spatial dependencies in trafc, used distance, direction, and location diagram convolution networks, and gestion control. Huertas  combined an unsupervised transformed the three spatial relations into deep neural clustering algorithm with a predictive model based on networks for trafc speed prediction. Pavlyuk [17, 18] ap- multiple logistic regression for scalable prediction and plied integrated learning techniques in space-time structure analysis of trafc accidents. Chiabaut  proposed learning and applied them to predict short-term trafc fow a method for real-time assessment of trafc conditions. in cities. In addition, the researcher proposed a spatiotem- Bhatia  applied a data-driven approach to build artifcial poral cross-validation method to evaluate the model per- intelligence models for vehicle trafc behavior prediction. formance. Alves and Cordeiro  developed an adaptive Te aforementioned research fndings ofer a solid foundation for the study of the congestion risk of ship trafc algorithm to accurately predict trafc fow and monitor highways connected to a complex network using local trafc fow, but given the state of the research, there is no com- prehensive research on the variables afecting ship trafc measurements. Carpio  confrmed that LSTM-based trafc prediction can efectively reduce the complexity of fow and congestion risk . Wang et al.  designed trafc prediction. To overcome the limitation of trafc a ship trafc fow model based on a multiple hexagon-based prediction on a specifc road, Jin  adapted the bi- convolutional neural network (mh-CNN) method. How- directional encoder representations from transformers to ever, the large spatial resolution of the method makes it trafc modelling, suitably predicting trafc fow in various difcult to be used in inland waterway navigation studies. Xu routes. and Zhang  proposed a RNN-based method to predict the ship trafc fow of Yangtze River. Te method is simple With the rapid growth in transport demand worldwide, trafc congestion is gradually increasing owing to natural and efective; however, the spatiotemporal dependence of ship trafc fow was not considered in the method. Ship and artifcial factors. Trafc congestion not only causes unnecessary waste of time and human and fnancial re- trafc fow statistics, on the other hand, are highly spatially articulated and temporally correlated and are infuenced by sources for those afected but also causes delays and lags in world trade . Terefore, it is critical to study and mitigate a range of factors. From the standpoint of water trafc spatial trafc congestion. To solve trafc congestion in specifc correlation [38, 39], adjacent sections or channels are im- scenarios, Jin  formulated a dual-target cash transport pacted by nearby vessel trafc fow. Ship trafc fow exhibits Journal of Advanced Transportation 3 n+1 a particular time correlation in the close time distance . Te update of u and, according to the alternating n+1 n+1 So, it makes sense to look at how to create a suitable con- direction method of multipliers, the update of ω and λ k k gestion risk model for ship trafc fow. can be expressed as Considering the advantages and drawbacks of the f(ω) − u (ω) + 0.5λ(ω) i≠k i n+1 abovementioned developments, we use variational mode u (ω) � , (4) k 2 decomposition (VMD), an extreme learning machine 1 + 2α