282 research outputs found

    Data driven spatio-temporal analysis of e-cargo bike network in Lisbon and its expansion: The Yoob case study

    Get PDF
    The adoption of more environmentally friendly and sustainable fleets for last-mile parcel delivery within large urban centers has been on the rise. Cargo bikes have been the most common alternative. The implementation of this type of fleet has proven to bring benefits, but has evidenced some limitations. The infrastructure network, which supports urban logistics, had to adapt to respond to the requirements of this new type of fleet. The implementation of micro-hubs and nano-hubs was the solution. Our study has two main objectives. The first objective is to perform a spatiotemporal characterization of fleet behavior, by conducting a case study where we explored the data from YOOB (a last mile delivery logistics start-up that operates in the Lisbon area and outskirts) e-cargo bike fleet. And the second is to identify potential expansion locations to the establishment of new hubs. The work procedures followed the CRIPS-DM methodology and the collected data was based on a 4-month period (January to April 2022). By adopting data science and machine learning techniques, five types of performances of YOOB fleet were identified, with variations in distances traveled, times, volumes transported and speeds. In the perspective of expanding YOOB's e-cargo bike network, three new locations in Lisbon were signaled for potential new hub installation.A adoção de frotas mais ecológicas e sustentáveis para a distribuição das encomendas na última milha dentro dos grandes centros urbanos tem vindo a crescer. As bicicletas de carga têm sido a alternativa mais comum. A implementação deste tipo de frotas, demonstrou trazer benefícios, mas evidenciou algumas limitações. A rede de infraestruturas, que serve de suporte á logística urbana, teve de se adaptar para poder responder às necessidades deste novo tipo de frotas. A implementação de microhubs e nano-hubs foram a alternativa. O nosso estudo tem dois objetivos principais. O primeiro objetivo é o de fazer uma caracterização espácio temporal dos comportamentos da frota, através de um estudo de caso onde efetuámos a exploração dos dados da frota de e-cargo bike da YOOB (start-up logística de entregas na última milha que atua na área de Lisboa e na periferia). E o segundo consiste em identificar potenciais locais de expansão para a instalação de novos hubs no mesmo estudo de caso. Nos processos de trabalho foi seguida a metodologia CRISP-DM e os dados recolhidos foram referentes a um período de 4 meses (Janeiro a Abril de 2022). Com recurso a técnicas de ciência dos dados e aprendizagem automática, foram identificados cinco tipos de desempenhos da frota da YOOB, com variações em distâncias percorridas, tempos efetuados, volumes transportados e velocidades praticadas. Numa perspetiva de expansão da rede de e-cargo bike da YOOB, forma identificados três novos locais na cidade de Lisboa para a instalação potencial de novos hubs

    A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

    Get PDF
    The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluation

    Geographic knowledge discovery from sparse GPS-data : Revealing spatio-temporal patterns of Amazonian river transports

    Get PDF
    A vast amount of spatio-temporal data has become available with the fast development of information technology and different monitoring systems over the last two decades. Position-aware devices are one of the most dominant sources for collecting movement data. Spatio-temporal information that is derived from the tracking devices enable to build movement patterns from the targets, and to calculate measurable motion parameters such as speed, change of speed or the direction of movement. This study utilized a specific pilot GPS-based monitoring system called Amazonian Riverboat Observation System (AROS) that was built to collect movement data of the local riverboats on the departments of Loreto and Ucayali in Peruvian Amazonia. AROS provides real-time GPS-data with coordinates and timestamp that indicate where and when the collaborating vessels are navigating. As an outcome of this thesis a specific analytical tool called Trajectory Reconstruction and Analysis Tool (TRAT) was developed. TRAT utilizes variety of geographic knowledge discovery methods to extract knowledge from movement data provided by AROS. Also spatio-temporal transportation characteristics in the study area were analyzed based on AROS data from the year 2012 and utilizing TRAT. This thesis focused on studying if there is seasonal and directional variation in transportation characteristics along the Amazonian rivers, and if river morphology affects the navigation. Also connection between water height of the rivers and travel speed of individual journeys was studied. Results of the thesis suggest that navigation along the rivers has seasonal and directional variation, and also the river morphology seems to affect the movement patterns of the vessels. On navigation route that was mostly meandering by river morphology, the downstream navigation was over 40% faster than upstream navigation during high water and intermediate, but during low water there was no difference between navigation directions. Seasonal variation was over 30% faster during high water compared to low water (on downstream direction). On upstream direction the navigation was fastest during low water but seasonal differences were considerably lower compared to downstream navigation. On navigation route that was mostly anastomosing by river morphology, the downstream navigation was approximately 20 % faster during the entire year. Results suggest that there is no seasonal difference in navigation characteristics along the larger and wider rivers, since the travel speeds were quite similar throughout the year. Fitting simple regression model between average travel speed of the journeys and water levels of the river revealed that there seems to be strong connection between travel speed and river height on the route along Ucayali river when travelled downstream (R2=0.73). On other cases that were studied, the results suggest that there is not connection between travel speed characteristics and river height. Comparing the results with earlier studies implied that the results of this thesis seemed to be fairly accurate. However, it is necessary to validate the results by doing cross-validations between data from different years observed with AROS. Transportation is in a key role when trying to find the factors affecting on development of a certain location. Thus transportation as means of accessibility has significant role in variety of contexts such as conversation, land use changes and deforestation. Results of this study could provide more accurate data for studies focusing on previously mentioned topics in the study area. Also utilization of TRAT in other contexts, such as studying global transportation patterns of professional vessels, could be possible by making few modifications to the tool.Informaatioteknologian ja erilaisten seurantajärjestelmien nopea kehitys viimeisten kahden vuosikymmenen aikana on mahdollistanut massiivisten spatio-temporaalisten tietovarantojen keräämisen. Paikannusteknologioilla varustetut laitteet ovat keskeisimpiä datalähteitä spatio-temporaalisen liikkumistiedon keräämiseen, ja tällainen data mahdollistaa erilaisten kohteiden (liikennevälineet, ihmiset jne.) liikkumisrakenteiden tutkimisen sekä erilaisten liikkumisparametrien kuten nopeuden, ja nopeuden sekä kulkusuunnan muutoksen laskemisen. Tässä tutkimuksessa hyödynnetään eristyistä pilotti-seurantajärjestelmää (AROS), joka on kehitetty keräämään jokilaivojen liikkumisdataa Loreton ja Ucayalin seuduilla Perun Amazoniassa. AROS mahdollistaa reaaliaikaisten laivojen sijantitietojen (koordinaatit) sekä aikatiedon (aikaleima) keräämisen. Tässä tutkimuksessa kehitettiin erityinen liikkumistiedonlouhintaan tarkoitettu analyysityökalu (TRAT), joka hyödyntää useita spatiaalisen tiedonlouhinnan menetelmiä informaation louhimiseksi AROS datasta. Tutkimuksessa tutkittiin, onko AROS datan perusteella jokinavigoinnissa nähtävissä vuodenaikaista vaihtelua vuoden 2012 aikana, ja vaikuttaako kulkusuunta sekä jokimorfologia navigointinopeuksiin. Tutkimuksessa tutkittiin myös, onko jokien vedenkorkeuksilla yhteyttä navigointinopeuksiin. Tutkimuksen tulokset osoittivat, että navigointi vaihtelee riippuen vuodenajasta sekä kulkusuunnasta, ja myös viitteitä jokimorfologian vaikutuksesta navigointiin oli paikoittain nähtävissä. Meanderoivilla jokiosuuksilla navigoiminen alavirtaan oli n. 40 % nopeampaa korkeanveden aikaan, mutta matalanveden aikaan eroa nopeuksissa ei ollut juuri nähtävissä. Vuodenaikaisvaihtelu oli selkeintä alavirtaan kuljettaessa, jolloin navigointi korkeanveden aikaan oli n. 30 % nopeampaa verrattuna matalanveden aikaan. Anastomoivilla jokiosuuksilla erot nopeuksissa eri kulkusuuntiin olivat vähäisemmät, ja navigointi oli keskimäärin 20 % nopeampaa alavirtaan (verrattuna ylävirtaan). Vuodenaikaisvaihtelua ei ollut juurikaan nähtävissä. Lineaarien regressiomalli jokikorkeuksien ja yksittäisten osareittien navigointinopeuksien välille osoitti, että yhteys oli selkeä (R2=0.73) osareiteillä, jotka kulkivat Ucayali-jokea alavirtaan. Muissa tutkituissa tapauksissa selkää yhteyttä ei löytynyt. Vertailemalla työn tuloksia aiempiin tutkimuksiin osoitti, että tulokset vaikuttavat olevan linjassa muiden tutkimusten tulosten kanssa. Työn tuloksia tulee jatkossa tosin vielä validoida vertailemalla vuoden 2012 tuloksia muiden vuosien tuloksiin AROS datan perusteella. Liikennejärjestelmät ovat keskeisiä tekijöitä, jotka vaikuttavat alueiden yleiseen kehitykseen. Yksi tapa kuvata liikennerakenteita on tarkastella paikkojen välistä saavutettavuutta, jolla on todettu olevan merkitystä lukuisiin eri yhteyksissä kuten maankäytön muutoksessa, deforestaatiossa sekä luonnonsuojelussa. Tämän tutkimuksen tulokset voivat tarjota tarkempaa dataa ja informaatiota liittyen edellämainittujen aiheiden tutkimiseen Perun Amazoniassa ja mahdolllisesti muillakin Amazonin alueilla. Kehitettyä analyysityökalua (TRAT) on myös mahdollista hyödyntää laajemmissa yhteyksissä, kuten globaalin laivaliikenteen tutkimuksessa, tekemällä pieniä muutoksia työkalun algoritmeihin

    Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships

    Get PDF
    Maritime Autonomous Surface Ships (MASS) are deemed as the future of maritime transport. Although showing attractiveness in terms of the solutions to emerging challenges such as carbon emission and insufficient labor caused by black swan events such as COVID-19, the applications of MASS have revealed problems in practice, among which MASS navigation safety presents a prioritized concern. To ensure safety, rational route planning for MASS is evident as the most critical step to avoiding any relevant collision accidents. This paper aims to develop a holistic framework for the unsupervised route planning of MASS using machine learning methods based on Automatic Identification System (AIS) data, including the coherent steps of new feature measurement, pattern extraction, and route planning algorithms. Historical AIS data from manned ships are trained to extract and generate movement patterns. The route planning for MASS is derived from the movement patterns according to a dynamic optimization method and a feature extraction algorithm. Numerical experiments are constructed on real AIS data to demonstrate the effectiveness of the proposed method in solving the route planning for different types of MASS

    MODELLING AND SYSTEMATIC EVALUATION OF MARITIME TRAFFIC SITUATION IN COMPLEX WATERS

    Get PDF
    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

    Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

    Get PDF
    Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

    Simulating spatial behaviour

    Get PDF

    Byliv og Byrumskvalitet:ny viden, metoder og vidensbehov, København, 24. juni 2009, Proceedings

    Get PDF

    Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters

    Get PDF
    Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships
    corecore