Spatio-temporal route mining and visualization for busy waterways

Wen, Rong; Yan, Wenjing; Zhang, Allan N.; Chinh, Nguyen Quoc; Akcan, Orkan

doi:10.1109/smc.2016.7844346

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…With development and application of various vessel tracking and positioning sensors in the maritime industry, e.g., Automatic Identification System (AIS) and Vessel Monitoring Systems (VMS), abundant spatial and temporal data sets related to vessel voyage are available for vessel motion data analysis. Recent development of machine learning methods provided intelligent data-driven solutions to logistics and transportation problems, as well as traffic management for land and sea transportation [1,2].…”

Section: Introductionmentioning

confidence: 99%

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Wen

Yan

2019

LOGI – Scientific Journal on Transport and Logistics

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The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

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Section: Introductionmentioning

confidence: 99%

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Wen

Yan

2019

LOGI – Scientific Journal on Transport and Logistics

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“…Toro et al[16] study the mobility patterns of users of Milan's bike sharing systems and using the clustering technique with the K-Means algorithm allowed him to identify which stations have the same usage pattern. In the exploitation of the most frequent paths made in the Singapore Strait Ron, Wen et al[22] applied the clustering technique with the K-nearest neighbors algorithm, to clustering time series of waterways, allowed them to identify the most congested areas spatially and temporally.…”

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confidence: 99%

Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study

Gil

Albuquerque

Dias

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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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.

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