Effective barge scheduling in the logistic domain requires advanced information on the availability of the port terminals and the maritime traffic in their vicinity. To enable a long-term prediction of vessel arrival times, we investigate how to use the publicly available automatic identification system (AIS) data to identify maritime patterns and transform them into a directed graph that can be used to estimate the potential trajectories and destination points. To tackle this problem, we use a genetic algorithm (GA) to cluster vessel position data. Then, we show how to enhance the process to allow fast computation of incremental data coming from the sensors, including the importance of adding a quad tree structure for data preprocessing. Focusing on a real case implementation, characterized by partially incomplete and noisy AIS data, we show how the algorithm can handle routes intersecting the regions with missing data and the repercussions this has on the route graph. Finally, postprocessing is explained that handles graph pruning and filtering. We validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces followed by the simulation using synthetic data to highlight the strengths and weaknesses of this approach.

Andrej Dobrkovic

Maria-Eugenia Iacob

Jos van Hillegersberg

International Journal of Data Science and Analytics

数据科学已被确立为重要的新兴科学领域和范式，推动统计学、计算科学和智能科学等学科的研究发展，以及科学、工程、公共部门、商业、社会科学和生活方式等领域的实践变革.该领域包括人工智能、数据分析、机器学习、模式识别、自然语言理解和大数据操作等更大的领域。它还应对相关的新科学挑战，从数据捕获、创建、存储、检索、共享、分析、优化和可视化，到跨异构和相互依赖的复杂资源的综合分析，以更好地做出决策、协作并最终实现价值CREATION.THE INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS (JDSA) 汇集了数据科学与分析的思想领袖、研究人员、行业从业者和潜在用户，共同开发该领域，讨论新趋势和机遇，交流想法和实践，并促进跨学科和跨领域的合作。该期刊由三个流组成： 常规，交流有关数据科学和分析的原始和可重复的理论和实验发现；应用程序，将重要的数据科学应用报告到现实生活中；和趋势，报告专家意见以及对数据科学和分析相关领域和主题的全面调查和评论。相关主题包括数据科学和分析的趋势、科学基础、技术和应用的所有方面，主要关注点关于：数据科学和分析的统计和数学基础；对复杂数据、人类、领域、网络、组织、社会、行为和系统特征、复杂性和智能的理解和分析；创建和提取、处理、表示和建模、学习复杂数据、行为、知识和智能的发现、融合和整合、呈现和可视化；数据分析、模式识别、知识发现、机器学习、深度分析和深度学习，以及各种数据（包括事务、文本、图像、视频、图形和网络）、行为和系统；主动、实时、个性化ZED、可操作和自动化的分析、学习、计算、优化、演示和推荐；大数据架构、基础设施、计算、匹配、索引、查询处理、映射、搜索、检索、互操作性、交换和推荐；大数据的内存、分布式、并行、可扩展和高性能计算、分析和优化；数据科学研究、创新和应用的回顾、调查、趋势、前景和机会；科学、商业、政府、文化、行为、社会和经济、健康和医疗、人类、自然和人工中的数据科学应用、智能设备和服务（包括在线/WEB、云、物联网、移动和社交媒体）域；道德、质量、隐私、安全和安保、信任以及数据科学和分析的风险

The International Journal of Data Science and Analytics is dedicated to advancing data science as an important emerging scientific field. This field integrates multiple disciplines, including statistics, computational science, and intelligent science, driving transformative practices across science, engineering, public sectors, business, social sciences, and lifestyle. Data science encompasses a wide range of topics, including artificial intelligence, data analysis, machine learning, pattern recognition, natural language processing, and big data operations. The journal aims to address new scientific challenges related to data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, as well as to conduct comprehensive analyses of complex resources that are heterogeneous and interdependent, in order to make better decisions, foster collaboration, and ultimately achieve value creation. The International Journal of Data Science and Analytics brings together thought leaders, researchers, industry practitioners, and potential users in the field of data science and analytics to explore new trends and opportunities, share ideas and practices, and promote interdisciplinary and cross-sector collaboration. The journal is divided into three sections: the regular section, which communicates original and reproducible theoretical and experimental findings in data science and analytics; the applications section, which reports significant applications of data science in real life; and the trends section, which provides expert opinions and comprehensive surveys and reviews of topics related to data science and analytics. Relevant topics include trends, scientific foundations, technologies, and applications of data science and analytics, with a primary focus on the following areas: the statistical and mathematical foundations of data science and analytics; understanding and analyzing complex data, human behavior, domains, networks, organizations, societies, and system characteristics; discovering, integrating, and modeling complex data, behaviors, knowledge, and intelligence; data analysis, pattern recognition, knowledge discovery, machine learning, deep analysis, and deep learning, as well as various types of data (including transactional, textual, image, video, graphical, and network data), behaviors, and systems; proactive, real-time, personalized, actionable, and automated analysis, learning, computation, optimization, presentation, and recommendations; big data architecture, infrastructure, computation, matching, indexing, query processing, mapping, searching, retrieval, interoperability, exchange, and recommendations; memory, distributed, parallel, scalable, and high-performance computing, analysis, and optimization of big data; reviews, surveys, trends, prospects, and opportunities in data science research, innovation, and applications; applications of data science in science, business, government, culture, behavior, society and economy, health and medicine, humanity, nature, and artificial domains, as well as intelligent devices and services (including online/WEB, cloud, IoT, mobile, and social media); and ethical, quality, privacy, security, trust, and risk issues related to data science and analytics.

国际数据科学与分析期刊（International Journal of Data Science and Analytics）致力于推动数据科学作为一个重要的新兴科学领域的发展。该领域融合了统计学、计算科学和智能科学等多个学科，推动了科学、工程、公共部门、商业、社会科学和生活方式等领域的实践变革。数据科学涵盖了人工智能、数据分析、机器学习、模式识别、自然语言处理和大数据操作等广泛主题。期刊旨在应对数据捕获、创建、存储、检索、共享、分析、优化和可视化等新科学挑战，并进行跨异构和相互依赖的复杂资源的综合分析，以便更好地做出决策、促进协作并实现价值创造。国际数据科学与分析期刊汇集了数据科学与分析领域的思想领袖、研究人员、行业从业者和潜在用户，共同探讨新趋势和机遇，分享想法和实践，并促进跨学科和跨领域的合作。期刊分为三个部分：常规部分，交流数据科学和分析的原始和可重复的理论与实验发现；应用部分，报告数据科学在现实生活中的重要应用；趋势部分，提供专家意见以及对数据科学和分析相关领域和主题的全面调查和评论。相关主题包括数据科学和分析的趋势、科学基础、技术和应用的各个方面，主要关注以下几个方面：数据科学和分析的统计和数学基础；对复杂数据、人类、领域、网络、组织、社会、行为和系统特征的理解与分析；创建、提取、处理、表示和建模复杂数据、行为、知识和智能的发现、融合和整合；数据分析、模式识别、知识发现、机器学习、深度分析和深度学习，以及各种数据（包括事务、文本、图像、视频、图形和网络）、行为和系统；主动、实时、个性化、可操作和自动化的分析、学习、计算、优化、演示和推荐；大数据架构、基础设施、计算、匹配、索引、查询处理、映射、搜索、检索、互操作性、交换和推荐；大数据的内存、分布式、并行、可扩展和高性能计算、分析和优化；数据科学研究、创新和应用的回顾、调查、趋势、前景和机会；科学、商业、政府、文化、行为、社会和经济、健康和医疗、人类、自然和人工中的数据科学应用、智能设备和服务（包括在线/WEB、云、物联网、移动和社交媒体）领域；以及数据科学和分析的伦理、质量、隐私、安全和信任等风险问题。

Maritime pattern extraction and route reconstruction from incomplete AIS data

This work investigates how to use the publicly available AIS data to identify maritime patterns and transform them into a directed graph that can be used to estimate the potential trajectories and destination points to enable a long-term prediction of vessel arrival times.

Maritime pattern extraction and route reconstruction from incomplete AIS data

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2