FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network

• URL: http://arxiv.org/abs/2201.07606v2
• Date: Tue, 4 Apr 2023 01:45:22 GMT
• Title: FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network
• Authors: Jin Chen, Xingchen Li, Ye Xiao, Hao Chen, and Yong Zhao
• Abstract summary: This paper proposes an Automatic Identification System (AIS) data-driven long short-term memory (LSTM) method. The accuracy of our proposed method in predicting short-term and mid-term trajectories has increased by 96.8% and 86.5% on average compared with the BiLSTM and Seq2seq.
• Score: 13.714691033493347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In order to improve the vessel's capacity and ensure maritime traffic safety, vessel intelligent trajectory prediction plays an essential role in the vessel's smart navigation and intelligent collision avoidance system. However, current researchers only focus on short-term or long-term vessel trajectory prediction, which leads to insufficient accuracy of trajectory prediction and lack of in-depth mining of comprehensive historical trajectory data. This paper proposes an Automatic Identification System (AIS) data-driven long short-term memory (LSTM) method based on the fusion of the forward sub-network and the reverse sub-network (termed as FRA-LSTM) to predict the vessel trajectory. The forward sub-network in our method combines LSTM and attention mechanism to mine features of forward historical trajectory data. Simultaneously, the reverse sub-network combines bi-directional LSTM (BiLSTM) and attention mechanism to mine features of backward historical trajectory data. Finally, the final predicted trajectory is generated by fusing output features of the forward and reverse sub-network. Based on plenty of experiments, we prove that the accuracy of our proposed method in predicting short-term and mid-term trajectories has increased by 96.8% and 86.5% on average compared with the BiLSTM and Seq2seq. Furthermore, the average accuracy of our method is 90.1% higher than that of compared the BiLSTM and Seq2seq in predicting long-term trajectories.

Related papers

• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• End-to-end LSTM based estimation of volcano event epicenter localization [55.60116686945561]
  An end-to-end based LSTM scheme is proposed to address the problem of volcano event localization. LSTM was chosen due to its capability to capture the dynamics of time varying signals. Results show that the LSTM based architecture provided a success rate, i.e., an error smaller than 1.0Km, equal to 48.5%.
  arXiv  Detail & Related papers  (2021-10-27T17:11:33Z)
• Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks [13.193080011901381]
  We propose sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks.
  arXiv  Detail & Related papers  (2021-01-07T11:05:47Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)
• From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting [54.273455592965355]
  Uncertainty in future trajectories stems from two sources: (a) sources known to the agent but unknown to the model, such as long term goals and (b)sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness indecisions. We model the epistemic un-certainty through multimodality in long term goals and the aleatoric uncertainty through multimodality in waypoints& paths. To exemplify this dichotomy, we also propose a novel long term trajectory forecasting setting, with prediction horizons upto a minute, an order of magnitude longer than prior works.
  arXiv  Detail & Related papers  (2020-12-02T21:01:29Z)
• Prediction of Traffic Flow via Connected Vehicles [77.11902188162458]
  We propose a Short-term Traffic flow Prediction framework so that transportation authorities take early actions to control flow and prevent congestion. We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by Connected Vehicles (CV) technology. We show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of various events that CV realistically encountered on segments along their trajectory.
  arXiv  Detail & Related papers  (2020-07-10T16:00:44Z)
• FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention [88.33372574562824]
  We propose a novel framework based on feed-forward network (FFN) for ETA, FFN with Multi-factor self-Attention (FMA-ETA) The novel Multi-factor self-attention mechanism is proposed to deal with different category features and aggregate the information purposefully. Experiments show FMA-ETA is competitive with state-of-the-art methods in terms of the prediction accuracy with significantly better inference speed.
  arXiv  Detail & Related papers  (2020-06-07T08:10:47Z)
• Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing [0.27528170226206433]
  DistPre is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. D DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.
  arXiv  Detail & Related papers  (2020-05-10T21:24:23Z)
• Short-Term Traffic Flow Prediction Using Variational LSTM Networks [3.2364716800671873]
  This research is to suggest a forecasting model for traffic flow by using deep learning techniques based on historical data. The historical data collected from the Caltrans Performance Measurement Systems (PeMS) for six months in 2019. The proposed prediction model is a Variational Long Short-Term Memory in brief VLSTM-E.
  arXiv  Detail & Related papers  (2020-02-18T23:22:31Z)
• AdaEnsemble Learning Approach for Metro Passenger Flow Forecasting [4.415977307120616]
  We present a novel adaptive ensemble (AdaEnsemble) learning approach to accurately forecast the volume of metro passenger flows. It combines the complementary advantages of variational mode decomposition (VMD), seasonal autoregressive integrated moving averaging (SARIMA), multilayer perceptron network (MLP) and long short-term memory (LSTM) network. Our proposed AdaEnsemble learning approach not only has the best forecasting performance compared with the state-of-the-art models but also appears to be the most promising and robust based on the historical passenger flow data in Shenzhen subway system.
  arXiv  Detail & Related papers  (2020-02-18T14:18:53Z)
• Stream-Flow Forecasting of Small Rivers Based on LSTM [3.921808417990452]
  This paper tries to provide a new method to do the forecast using the Long-Short Term Memory (LSTM) deep learning model. We collected the stream flow data from one hydrologic station in Tunxi, China, and precipitation data from 11 rainfall stations around to forecast the stream flow data. We evaluated the prediction results using three criteria: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2)
  arXiv  Detail & Related papers  (2020-01-16T07:14:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.