An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management

(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA (Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport analytics application that has been developed on top of the EUBra-BIGSEA platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data analytics platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.This work was supported by the European Commission through the Cooperation Programme under EUBra-BIGSEA Horizon 2020 Grant [Este projeto e resultante da 3a Chamada Coordenada BR-UE em Tecnologias da Informacao e Comunicacao (TIC), anunciada pelo Ministerio de Ciencia, Tecnologia e Inovacao (MCTI)] under Grant 690116.Fiore, S.; Elia, D.; Pires, CE.; Mestre, DG.; Cappiello, C.; Vitali, M.; Andrade, N.... (2019). An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management. IEEE Access. 7:117652-117677. https://doi.org/10.1109/ACCESS.2019.2936941S117652117677

Inferindo traços de viagem de ônibus no nível do passageiro dados de programação, posicionamento e emissão de bilhetes: métodos e aplicações.

Como resultado do recente e rápido crescimento da população urbana, a mobilidade tem emergido como um dos problemas urbanos mais complexos e de rápida evolução no século XXI. Com o advento da Internet das Coisas, gigabytes de dados são gerados diariamente por Sistemas de Transporte Público ao redor do mundo, incluindo registros de GPS e velocidade dos ônibus, além de registros de embarque de passageiros. A despeito desses dados possuírem o potencial de auxiliar na melhoria da mobilidade, a enorme quantidade, dinamicidade e diversidade de dados produzidos por diferentes sistemas com diferentes objetivos e restrições, impõe dificuldades para a integração e análise do mesmo com o fim de ajudar os usuários, operadores e administradores do sistema. Esse estudo aborda esse problema, mais especificamente o de utilizar dados de programação dos ônibus, dados brutos de GPS e dados de cartão de embarque para reconstruir viagens de ônibus a nível de passageiro. São utilizados dados do sistema de ônibus de Curitiba no Brasil para conceber um processo de análise que combine e estenda heurísticas consolidadas encontradas na literatura. Experimentos demonstram a utilidade da solução proposta em dois cenários de aplicações: a) a estimação de uma Matriz de Origem-Destino para usuários de Transporte Público, que foi validada através de uma comparação com uma Pesquisa Origem-Destino realizada recentemente na cidade; e b) uma análise da (in)eficiência da escolha de itinerário do passageiro, realizada contrastando o itinerário escolhido estimado (extraído da reconstrução da viagem) com o conjunto de itinerários disponíveis e viáveis no momento do embarque.As a result of the recent and fast rise in urban population, mobility has emerged as one of the most problematic and fast-evolving urban problems of the 21st century. With the advent of the Internet of Things, gigabytes of data are generated every day by Public Transportation Systems around the world, including bus GPS/speed records, and passenger boarding registries. Although this data has the potential to help improve mobility, the vast amount, dynamicity and diversity of data produced by different systems with different goals and constraints poses difficulties to integrate and analyze it and help the system’s users, operators and administrators. This study addresses this problem, more specifically the one of using bus schedule data, raw GPS and smart card records to reconstruct trips at passenger-level. We use data from the Curitiba bus system in Brazil to devise an analysis pipeline that combines and extends consolidated heuristics found in literature. Experiments demonstrate the utility of the proposed solution in two applications scenarios: a) the estimation of an Origin-Destination Matrix for Public Transport users, which was validated by a comparison to a recent Origin-Destination Survey performed in the city; and b) an analysis of the (in)efficiency of passenger itinerary choice, conducted by contrasting the estimated itinerary choice (extracted from trip reconstruction) to the set of available and feasible itineraries at the time of boarding

BIGSEA: A Big Data analytics platform for public transportation information

[EN] Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe¿Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness. All developments are available under Open Source licenses (http://github.org/eubr-bigsea, https://hub.docker.com/u/eubrabigsea/).The work shown in this article has been funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 690116 (EUBra-BIGSEA) and the Ministério de Ciência, Tecnologia e Inovação (MCTI) from Brazil.Alic, AS.; Almeida, J.; Aloisio, G.; Andrade, N.; Antunes, N.; Ardagna, D.; Badía, R.... (2019). BIGSEA: A Big Data analytics platform for public transportation information. Future Generation Computer Systems. 96:243-269. https://doi.org/10.1016/j.future.2019.02.011S2432699

BIGSEA: A Big Data analytics platform for public transportation information

Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe–Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness. All developments are available under Open Source licenses (http://github.org/eubr-bigsea, https://hub.docker.com/u/eubrabigsea/)

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora

Growing knowledge: an overview of Seed Plant diversity in Brazil

Abstract An updated inventory of Brazilian seed plants is presented and offers important insights into the country's biodiversity. This work started in 2010, with the publication of the Plants and Fungi Catalogue, and has been updated since by more than 430 specialists working online. Brazil is home to 32,086 native Angiosperms and 23 native Gymnosperms, showing an increase of 3% in its species richness in relation to 2010. The Amazon Rainforest is the richest Brazilian biome for Gymnosperms, while the Atlantic Rainforest is the richest one for Angiosperms. There was a considerable increment in the number of species and endemism rates for biomes, except for the Amazon that showed a decrease of 2.5% of recorded endemics. However, well over half of Brazillian seed plant species (57.4%) is endemic to this territory. The proportion of life-forms varies among different biomes: trees are more expressive in the Amazon and Atlantic Rainforest biomes while herbs predominate in the Pampa, and lianas are more expressive in the Amazon, Atlantic Rainforest, and Pantanal. This compilation serves not only to quantify Brazilian biodiversity, but also to highlight areas where there information is lacking and to provide a framework for the challenge faced in conserving Brazil's unique and diverse flora