ARMA Model-Based Prediction of the Number of Vessels Navigating the Istanbul Strait Unassisted by Maritime Pilots

The Istanbul Strait is one of the busiest and riskiest trade routes, with the annual traffic of 50,000 ships. Such high traffic density is managed by the enforcement of a passage regimen by the Vessel Traffic Service (VTS) and maritime pilots of the Directorate General of Coastal Safety of the Republic of Turkey. VTS operations and maritime pilot actions are assumed to complement each other. Accordingly, a vessel unaccompanied by a maritime pilot is expected to interact with the VTS to a greater extent than a vessel assisted by a maritime pilot. Thus, estimating the number of ships that pass through the Istanbul Strait, especially those that do not use maritime pilot assistance, will be an effective tool for the Istanbul Strait traffic scheme management, as it will allow the authorities to balance and integrate VTS and maritime pilot operations. The predictive model based on Autoregressive Moving Average (ARMA) described in this paper has been developed to estimate the number of ships that navigate through the Istanbul Strait without pilot assistance. The best ARMA model was identified through the use of historical data on 100-150 meter and 150-200-meter-long ships that passed through the Istanbul Strait unaccompanied by pilots in 2012-2019. The ARMA model obtained has also been validated through the comparison of real and estimated data

ARMA Model-Based Prediction of the Number of Vessels Navigating the Istanbul Strait Unassisted by Maritime Pilots

The Istanbul Strait is one of the busiest and riskiest trade routes, with the annual traffic of 50,000 ships. Such high traffic density is managed by the enforcement of a passage regimen by the Vessel Traffic Service (VTS) and maritime pilots of the Directorate General of Coastal Safety of the Republic of Turkey. VTS operations and maritime pilot actions are assumed to complement each other. Accordingly, a vessel unaccompanied by a maritime pilot is expected to interact with the VTS to a greater extent than a vessel assisted by a maritime pilot. Thus, estimating the number of ships that pass through the Istanbul Strait, especially those that do not use maritime pilot assistance, will be an effective tool for the Istanbul Strait traffic scheme management, as it will allow the authorities to balance and integrate VTS and maritime pilot operations. The predictive model based on Autoregressive Moving Average (ARMA) described in this paper has been developed to estimate the number of ships that navigate through the Istanbul Strait without pilot assistance. The best ARMA model was identified through the use of historical data on 100-150 meter and 150-200-meter-long ships that passed through the Istanbul Strait unaccompanied by pilots in 2012-2019. The ARMA model obtained has also been validated through the comparison of real and estimated data

Big Data y áreas de oportunidad para la proyección del Sistema Inteligente de Transporte en Bogotá, Colombia

Today, the large cities of Colombia – especially Bogotá, due to the growth of its population (9.3 million with the arrival of immigrants) – demand the projection of intelligent public and private transport systems, as an achievement of the mobility policy of the Bogota Humana administration. Hence, this question arises: What are the challenges and areas of opportunity of adapting Big Data to project an Intelligent Transportation System for all citizens in Bogotá? Based on this question, our aim is to determine the contributions that Big Data offers as a collection center for the projection of an intelligent system for the city. Our research was proposed with a qualitative approach and a descriptive study. The review of some studies developed using Big Data techniques and content data analysis of their organized structure by the District Mobility Secretariat in Bogotá was included. The results allow guiding the contributions of Big Data after analyzing the structure of indicators offered by the data set. From these, we found gaps and voids that are concerning for the Intelligent Transportation System that is expected in the future for Bogotá.Hoy en día, en las grandes ciudades de Colombia, en especial en Bogotá, y debido al crecimiento de su población (9,3 millones con la llegada de inmigrantes), se exige una demanda de aporte a la proyección de sistemas inteligentes de transporte públicos y privados como un logro de la política de movilidad de la administración de la Bogotá Humana. De ahí surge el interrogante: ¿cuál es el desafío y las áreas de oportunidad de adaptar un Big Data en la proyección de un Sistema Inteligente de Transporte para todos los ciudadanos en Bogotá? A partir de esta pregunta, se propone determinar los aportes que el Big Data ofrece como centro de acopio en la proyección de un sistema inteligente para la ciudad. La indagación se plantea desde un enfoque cualitativo y un estudio descriptivo. Se incluye la revisión de algunos estudios realizados mediante las técnicas del Big Data y del análisis de datos de contenido de la estructura organizada de estos por la Secretaría Distrital de Movilidad en Bogotá. Los resultados permiten orientar los aportes del Big Data después de analizar la estructura de indicadores que ofrecen estos el conjunto de datos. A partir de estos, se encuentran brechas y vacíos preocupantes para el Sistema Inteligente de Transporte que se espera en el futuro para Bogotá

A spatial-temporal data mining method for the extraction of vessel traffic patterns using AIS data

Current traffic pattern mining methods fail to incorporate the temporal co-occurrence of traffic characteristics. To address this problem, a new spatial-temporal data mining method is developed involving three steps. Firstly, a three-dimensional traffic tensor is constructed utilizing AIS data. The AIS data is discretized and numbered so that each AIS data entry is represented by a one-dimensional array that includes region, time, ship type, and speed numbers. Then the AIS array is mapped to the three-dimensional ship traffic tensor. Second, non-negative tensor factorization (NTF) is used to break down the tensor into multiple sub-tensors (i.e., traffic patterns). The effect of the tensor rank (i.e., the number of traffic patterns) is discussed, and the appropriate value of the tensor rank is determined. Thirdly, the traffic patterns are derived from the three-dimensional traffic tensor. The ship traffic pattern is subsequently analyzed in accordance with the actual circumstances. To demonstrate the feasibility of the method, 9 traffic patterns are obtained from the AIS data of Tianjin port-Caofeidian waters. These patterns reveal the presentation of the spatio-temporal distribution of traffic activities of different ship types, and the distribution of navigation speed of different ship types in space, that are of strategic values for port planning, and maritime safety and sustainability

MODELLING AND SYSTEMATIC EVALUATION OF MARITIME TRAFFIC SITUATION IN COMPLEX WATERS

Maritime Situational Awareness (MSA) plays a vital role in the development of intelligent transportation support systems. The surge in maritime traffic, combined with increasing vessel sizes and speeds, has intensified the complexity and risk of maritime traffic. This escalation presents a considerable challenge to the current systems and tools dedicated to maritime traffic monitoring and management. Meanwhile, the existing literature on advanced MSA methods and techniques is relatively limited, especially when it comes to addressing multi-ship interactions that may involve hybrid traffic of manned ships and emerging autonomous ships in complex and restricted waters in the future. The primary research question revolves around the challenge faced by current collision risk models in incorporating the impact of traffic characteristics in complex waters. This limitation hampers their effectiveness in managing complex maritime traffic situations. In view of this, the research aims to investigate and analyse the traffic characteristics in complex port waters and develop a set of advanced MSA methods and models in a holistic manner, so as to enhance maritime traffic situation perception capabilities and strengthen decision-making on anti-collision risk control. This study starts with probabilistic conflict detection by incorporating the dynamics and uncertainty that may be involved in ship movements. Then, the conflict criticality and spatial distance indicators are used together to partition the regional ship traffic into several compact, scalable, and interpretable clusters from both static and dynamic perspectives. On this basis, a systematic multi-scale collision risk approach is newly proposed to estimate the collision risk of a given traffic scenario from different spatial scales. The novelty of this research lies not only in the development of new modelling techniques on MSA that have never been done by using various advanced techniques (e.g., Monte Carlo simulation, image processing techniques, graph-based clustering techniques, complex network theory, and fuzzy clustering iterative method) but also in the consideration of the impact of traffic characteristics in complex waters, such as multi-dependent conflicts, restricted water topography, and dynamic and uncertain ship motion behaviours. Extensive numerical experiments based on real AIS data in the world's busiest and most complex water area (i.e., Ningbo_Zhoushan Port, China) are carried out to evaluate the models’ performance. The research results show that the proposed models have rational and reliable performance in detecting potential collision danger under an uncertain environment, identifying high-risk traffic clusters, offering a complete comprehension of a traffic situation, and supporting strategic maritime safety management. These developed techniques and models provide useful insights and valuable implications for maritime practitioners on traffic surveillance and management, benefiting the safety and efficiency enhancement of maritime transportation. The research can also be tailored for a wide range of applications given its generalization ability in tackling various traffic scenarios in complex waters. It is believed that this work would make significant contributions in terms of 1) improving traffic safety management from an operational perspective without high financial requirements on infrastructure updating and 2) effectively supporting intelligent maritime surveillance and serving as a theoretical basis of promoting maritime safety management for the complex traffic of mixed manned and autonomous ships