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The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) is introduced to to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) is developed to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model’s reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model.
- AIS data
- river sinuosity
- trajectory clustering
- trajectory prediction
1. Introduction引言
In contrast to land transport, water transport lacks well-defined fixed routes, granting ships a greater degree of freedom in their movement. Consequently, the management of water transport is inherently more intricate. Ship collisions are an unfortunate occurrence during waterborne navigation, often stemming from factors like equipment malfunctions and human errors. Thus, real-time monitoring of the ship’s course becomes indispensable for timely detection of anomalous behavior and the reduction of the risk of ship accidents. To support the safety of ship navigation, the water traffic management authorities employ a suite of modern information equipment, including the 与陆路运输相比，水路运输缺乏明确的固定路线，使船舶在行动中具有更大的自由度。因此，水运管理本身就更加复杂。船舶碰撞是水上航行过程中不幸发生的事件，通常源于设备故障和人为错误等因素。因此，对船舶航向的实时监控对于及时发现异常行为和降低船舶事故风险变得不可或缺。为了支持船舶航行安全，水上交通管理当局采用了一套现代信息设备，包括自动识别系统（Automatic Identification System (AIS) and Global Positioning System (GPS) IS）和全球定位系统（GPS）[1].。
AIS allows real-time monitoring of vessels within the base station’s coverage area by receiving and processing information transmitted by shipboard 通过接收和处理船上AIS equipment, leveraging unique AIS equipment codes for automatic ship identification. While AIS data discloses the location of the most recent AIS message sent, it carries inherent transmission delays. Additionally, there’s a time lag associated with data transmission, parsing, loading, and display, hindering real-time ship position representation. Moreover, equipment failures and signal interference can lead to the loss of AIS data. These challenges in the domain of AIS data complicate the task of ensuring ship safety.设备传输的信息，利用独特的AIS设备代码进行自动船舶识别，从而可以实时监控基站覆盖区域内的船舶。虽然 AIS 数据披露了最近发送的 AIS 消息的位置，但它带有固有的传输延迟。此外，数据传输、解析、加载和显示存在时间滞后，阻碍了船舶的实时位置表示。此外，设备故障和信号干扰会导致AIS数据丢失。AIS数据领域的这些挑战使确保船舶安全的任务变得复杂。
To address these issues, we present a real-time ship trajectory prediction model (RSTPM) designed to enable real-time ship trajectory monitoring and prompt detection of irregular behavior. This model offers valuable applications in ensuring the safety of ship navigation.
2. Related Work
根据国内外基于According to the relevant research based on AIS data at home and abroad, we summarize both the advantages and disadvantages of various ship trajectory prediction methods, as indicated in Table数据的相关研究，研究人员总结了各种船舶轨迹预测方法的优缺点，如表1所示。
表 1
.
.轨迹预测方法的分类和优缺点的简要说明。
| 类别 | 方法类别 | 优势 | 弊 |
|---|---|---|---|
| 模拟方法 | 指数平滑模型 （ESM） [2] | 可以使用少量数据进行预测 | 只能进行短期预测 |
| 曲率速度法 [3] | 模型简单，实时性好 | 只能进行短期预测 | |
| 统计方法 | 卡尔曼滤波 [4] | 线性、无偏、高精度 | 依赖于原始数据，无法随时间推移进行预测 |
| 自回归移动平均模型 （ARIMA） [5] | 模型简单，应用广泛 | 需要大量数据且精度低 | |
| 隐马尔可夫模型 （HMM） [6] | 过程的良好状态预测 | 鲁棒性差，参数设置复杂 | |
| 高斯混合模型 （GMM） [7] | 短距离预测精度高 | 容易受到数据复杂性和低效性的影响 | |
| 贝叶斯网络 [8] | 高效且易于培训 | 容易受到先验概率和输入变量的影响 | |
| 机器学习 | K 最近邻 （KNN） [9] | 易于实施，无需参数估计 | 当样本量不平衡时，准确性会受到影响 |
| 支持向量机（SVM） [10,11] | 适用于线性和非线性问题 | 仅适用于二分法问题 | |
| 人工神经网络 （ANN） [12] | 高精度和噪声误差容限 | 需要大量的初始参数和较长的训练时间 | |
| 极限学习机 （ELM） [13] | 隐藏层无需迭代，快速学习 | 可能导致过拟合问题 | |
| 反向传播 （BP） [14,15] | 能够自行学习和概括 | 可能会陷入局部极端，导致训练失败 | |
| 深度学习 | 长短期记忆 （LSTM） [16,17,18,19] | 循环神经网络（RNN）中长期依赖性的不足得到有效改善。 | 内部结构比较复杂，计算起来很耗时 |
| 格鲁 [20] | 模型简单，训练速度比LSTM快 | 不能完全解决梯度消失问题 | |
| 甘 [21] | 可以产生更清晰、更逼真的样品 | 不适合处理离散数据，例如文本 | |
| 卷积神经网络 （CNN） [22] | 可以自动执行特征提取 | 训练结果很容易收敛到局部最小值 | |
| 深度神经网络 （DNN） [23] | 极佳的非线性拟合能力 | 训练难度大，需要大量数据 | |
| 其他 | 混合动力车型 [24,25,26,27] | 结合多种机型的优点 | 可能导致计算成本增加 |
2.1. Ship Trajectory Prediction Based on Simulation Methods
2. 基于仿真方法的船舶轨迹预测
Simulation methods involve creating physical models to simulate real ship behavior. This method is rarely used alone in ship trajectory prediction仿真方法包括创建物理模型来模拟真实的船舶行为。这种方法很少单独用于船舶轨迹预测; it is generally combined with other methods to form a hybrid method for prediction.它通常与其他方法相结合，形成混合的预测方法。
The ESM is used to predict the location, course, and speed of the ship; meanwhile, the actual collision scene of the ship is analyzed. This method has been shown to achieve the prediction of ships’ behavior 用于预测船舶的位置、航向和速度;同时，分析了船舶的实际碰撞场景。该方法已被证明可以实现船舶行为的预测[2]. 。Mazzarella proposed a Bayesian algorithm based on particle filters that uses KNN to match the current trajectory sequence of the ship, enabling the prediction of ship trajectories when traffic route data are available 提出了一种基于粒子滤波器的贝叶斯算法，该算法使用KNN来匹配船舶的当前轨迹序列，从而能够在交通路线数据可用时预测船舶轨迹[3].。
2.2. Ship Trajectory Prediction Based on Statistical Methods
3. 基于统计方法的船舶轨迹预测
The statistical-based approach assumes that the historical trajectory of a ship and the predicted trajectory have a certain similarity, and the prediction is achieved by fitting the ship trajectory.基于统计的方法假设船舶的历史轨迹与预测的轨迹具有一定的相似性，并通过拟合船舶轨迹来实现预测。
Ju et al. 等[4] proposed a multi-layer architecture interactive-aware Kalman neural network to solve the problem of mutual interaction in the transportation system. 提出了一种多层架构交互感知卡尔曼神经网络，以解决交通系统中的相互作用问题。利用差分A differential ARIMA model was used to predict ship trajectories from ship AIS data, which is applicable to the detection of ship collision avoidance RIMA模型对船舶AIS数据进行船舶轨迹预测，适用于船舶避碰检测[5]. The trajectory sequence is transformed into column vectors through wavelet transform, which are then used as the input for 。通过小波变换将轨迹序列转换为列向量，然后将其用作HMM. This is an algorithm (的输入。这是一种算法（HMM-WA) that increases the accuracy of ship trajectory prediction ），可提高船舶轨迹预测的准确性[6]. 。Rong et al. 等[8] presented a model based on Gaussian processes and uncertain acceleration, designed to achieve real-time monitoring of ships during navigation.提出了一个基于高斯过程和不确定加速度的模型，旨在实现对船舶航行过程中的实时监控。
2.3. Ship Trajectory Prediction Based on Machine Learning
4. 基于机器学习的船舶轨迹预测
Unsupervised learning mainly focuses on the clustering and dimensionality reduction of data, while supervised learning has a broader range of applications. For example, 无监督学习主要侧重于数据的聚类和降维，而监督学习的应用范围更广。例如，KNN and SVM can predict ship trajectories by learning the motion characteristics of ship trajectories.和 SVM 可以通过学习船舶轨迹的运动特性来预测船舶轨迹。
Duca et al. 等[9] proposed a model for trajectory prediction based on a 提出了一种基于KNN classifier, considering five characteristics of ships: longitude, latitude, heading, speed, and type. The model’s prediction accuracy was verified. Liu et al. 分类器的轨迹预测模型，考虑了船舶的5个特征：经度、纬度、航向、速度和类型。验证了模型的预测精度。Liu等[10] developed an online multioutput model based on a selection mechanism. The model can achieve high prediction accuracy with small samples. Additionally, an 开发了一种基于选择机制的在线多输出模型。该模型可以在小样本下实现较高的预测精度。此外，还提出了一种基于SVR-based trajectory prediction model was proposed, but the sample data and parameters required for the model cannot be changed during model training 的轨迹预测模型，但在模型训练过程中不能改变模型所需的样本数据和参数[11]. 。Gan et al. 等[12] used the clustered ship trajectory and other known factors, such as ship speed, to establish an 利用聚类船舶轨迹和其他已知因素（如船舶速度）建立了预测船舶轨迹的ANN model for predicting the ship’s trajectory.模型。
Since the advent of deep learning, it has demonstrated excellent performance in many tasks, including the prediction of ship trajectories. The advantage of 自深度学习出现以来，它在许多任务中都表现出出色的性能，包括船舶轨迹的预测。LSTM over BP neural networks lies in its ability to process and analyze time series and sequence data. The gate structure in LSTM’s internal network enables it to mine trends and correlations in sequence data, resulting in a better prediction effect when applied to time series data, such as traffic and location. Moreover, LSTM’s prediction accuracy is better than that of BP neural networks 相对于 BP 神经网络的优势在于它能够处理和分析时间序列和序列数据。LSTM内部网络中的门结构使其能够挖掘序列数据中的趋势和相关性，从而在应用于时间序列数据（如流量和位置）时产生更好的预测效果。此外，LSTM的预测精度优于BP神经网络[16,17,18,28], making it applicable to long-term prediction. ，使其适用于长期预测。Gao et al. 等[29] studied a bi-directional 研究了一种双向LSTM (（Bi-LSTM) network, aiming to enhance the memory ability of historical data and the correlation between future time series data. Liu et al. ）网络，旨在增强历史数据的记忆能力和未来时间序列数据之间的相关性。Liu等[19] integrated convolutional transformations into a 将卷积变换集成到基于注意力机制的Bi-LSTM based on an attention mechanism in order to achieve long-term prediction. In another study, ship trajectory sequence features extracted by CNN are input into the LSTM model for prediction 中，以实现长期预测。在另一项研究中，CNN提取的船舶轨迹序列特征被输入到LSTM模型中进行预测[22]. In。除了 addition to LSTM, GRU, CNN, and GAN, there are also DNN-based frameworks for predicting the trajectory of merchant ships (such as tankers and container ships). However, the DNN module is prone to overfitting and may not achieve high accuracy during training LSTM、GRU、CNN 和 GAN 之外，还有基于 DNN 的框架用于预测商船（如油轮和集装箱船）的轨迹。然而，DNN模块容易出现过拟合，在训练过程中可能无法达到高精度[23].。
2.4. Ship Trajectory Prediction Based on the Hybrid Method
5. 基于混合方法的舰船轨迹预测
The hybrid method focuses on combining the advantages of various methods to enhance the efficiency of trajectory prediction tasks. 混合方法侧重于结合各种方法的优点，以提高轨迹预测任务的效率。Murray & Perera [24] proposed an algorithm that combines 提出了一种结合GMM, KNN, and bilinear automatic coding to build the prediction model. 、KNN和双线性自动编码的算法来构建预测模型。Schöller et al. 等[26] first used kernel density estimation to convert historical 首先使用核密度估计将历史AIS data into probabilistic heat maps and then used a convolutional autoencoder for further coding. They constructed a model based on GAN and LSTM to achieve ship trajectory prediction. Additionally, Suo et al. 数据转换为概率热图，然后使用卷积自动编码器进行进一步编码。他们构建了基于GAN和LSTM的模型，实现了船舶轨迹预测。此外，Suo等[27] constructed a hybrid model based on 构建了基于DBSCAN and GRU to achieve real-time prediction.和GRU的混合模型，实现了实时预测。
Moreover, the clustering methods of ship trajectory are roughly divided into distance-based 此外，船舶轨迹的聚类方法大致分为基于距离的[30,31,32,33], density-based 、基于密度的[34,35,36], and statistical-based 和基于统计的[37,38] methods. Most studies have achieved efficient clustering of ship trajectories by combining the advantages of various clustering methods 方法。大多数研究通过结合各种聚类方法的优点实现了船舶轨迹的高效聚类[30,31,35,36,37,39]. In this study, a new approach to cluster ship trajectory, called the combination of 。该文提出了一种新的聚类船舶轨迹方法，即DBSCAN and DTW (CDDTW), is proposed, which combines the optimized 与DTW相结合（CDDTW），该方法结合了优化的DBSCAN algorithm (based on density) and the improved DTW algorithm (based on distance) to cluster ship historical trajectories. The proposed method also extracts the regional navigation characteristics of ship trajectory based on the clustering results. An RSTPM based on an LSTM is constructed.算法（基于密度）和改进的DTW算法（基于距离）对船舶历史轨迹进行聚类。该方法还基于聚类结果提取了船舶航迹的区域导航特征。构建了基于LSTM的RSTPM。
