scholarly journals An open data-driven approach for travel demand synthesis: an application to São Paulo

2021 ◽  
Vol 8 (1) ◽  
pp. 371-386
Author(s):  
Aurore Sallard ◽  
Miloš Balać ◽  
Sebastian Hörl
Keyword(s):  
Travel Demand ◽  
Open Data ◽  
Sao Paulo ◽  
Data Driven ◽  
São Paulo ◽  
Data Driven Approach

scholarly journals Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 540
Author(s):  
Fabio Amaral ◽  
Wallace Casaca ◽  
Cassio M. Oishi ◽  
José A. Cuminato
Keyword(s):  
Learning Strategies ◽  
Time Series Data ◽  
Sao Paulo ◽  
The State ◽  
Data Driven ◽  
Series Data ◽  
São Paulo ◽  
Time Data ◽  
Unified Framework ◽  
Infected People

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

scholarly journals Disparities in travel times between car and transit: Spatiotemporal patterns in cities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Liao ◽  
Jorge Gil ◽  
Rafael H. M. Pereira ◽  
Sonia Yeh ◽  
Vilhelm Verendel
Keyword(s):  
Travel Time ◽  
Travel Demand ◽  
Time Of Day ◽  
Sao Paulo ◽  
São Paulo ◽  
Real World Data ◽  
Car Use ◽  
Real Time Traffic ◽  
Average Travel Time ◽  
Car Travel

AbstractCities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio $$R$$R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: $$R < 1$$R<1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.

The Comparison between Empirical and Data-driven Computation in Predicting CO and NOx Emissions from Gas-Turbine-Based Power Plant

2021 ◽  
Author(s):  
T. Y. Wicaksono
Keyword(s):  
Gas Turbine ◽  
Monitoring System ◽  
Power Plants ◽  
Combustion Process ◽  
Open Data ◽  
Living Organism ◽  
Empirical Method ◽  
Data Driven ◽  
Gas Emissions ◽  
Data Driven Approach

The demand for the energy has been significantly increased over years led by the growth of global population. By the signing of the Paris Agreement in 2015, countries pledged to reduce the greenhouse gas effect including gas emissions to prevent and mitigate the global warming. The emissions control from power generation has then become a serious concern for countries to achieve their target in reducing gas emissions. Besides, the emitted gas such as Nitrogen oxides (NOx) or Carbon Monoxide (CO) that are resulted from the combustion process of fossil fuels in power plants is harmful pollutants to the living organism. The presence of those gas emissions can be predicted using Predictive Emissions Monitoring System (PEMS) or Continues Emissions Monitoring System (CEMS) methods. Continuous Emissions Monitoring System is a system that was designed to monitor the effluent gas streams resulted from the combustion processes. However, this empirical method still has several constraints in predicting the gas emissions where in some cases, it produces significant errors that caused by some uncontrollable aspects such as ambient temperature, pressure and humidity that can lead to miscalculation of operational risks and costs. Solving this problem, we conduct a PEMS with data-driven approach. In this study, we used the 2011-2015 open data from gas-turbine-based power plants in Turkey to train and test several supervised methods as a practical application to predict gas concentration. Predictive Emissions Monitoring System (PEMS) offers more advantages than Continuous Emissions Monitoring System (CEMS) especially in economic aspects. The system will monitor and predict the actual emissions from gas-turbine-based power plants operation. The results of this study indicate that the data-driven approach produces a good RMSE value. By having the gas emissions predicted, a mitigation plan can be set and the operational costs in the following years can be optimized by the company

scholarly journals Gender and Numbers: Using Data from International Women’s Day Coverage on the Sites of Three Major Brazilian Newspapers

2019 ◽  
Vol 15 (1) ◽  
pp. 74-101
Author(s):  
Ana Carolina Araújo
Keyword(s):  
Information And Communication Technologies ◽  
Open Data ◽  
Communication Technologies ◽  
Sao Paulo ◽  
São Paulo ◽  
Use Of Data ◽  
Information And Communication ◽  
Data Journalism ◽  
Using Data ◽  
Visualization Techniques

Over the last decade, data journalism has evolved alongside information and communication technologies, spreading more and more into professional journalism. The purpose of this article is to understand if and how traditional press has reacted to the new technological tools available for reporting and the increasing availability of open data, whether public or private. To investigate these issues, articles published on the websites of three major Brazilian newspapers – Folha de S. Paulo, O Globo and O Estado de São Paulo – were analyzed for their coverage of International Women's Day in 2017. The results indicate that there is still a lack of data in most of the material, that the use of digital visualization techniques is still in its early stages and that there is no raw data supply in news production. On the other hand, the use of data and statistics without reference or source checking is quite noticeable and frequently occurs in the corpus.Na última década, o jornalismo de dados evoluiu ao lado das tecnologias de informação e comunicação, espraiando-se cada vez mais pelo ambiente do jornalismo profissional. A proposta deste artigo é compreender se e como o jornalismo impresso tradicional tem reagido às novas ferramentas tecnológicas disponíveis para a prática da reportagem e à crescente disponibilidade de dados abertos, sejam eles de origem pública ou privada. Para investigar estas questões, foram utilizadas como material de análise reportagens publicadas nos sites de três grandes jornais brasileiros – Folha de S. Paulo, O Globo e O Estado de São Paulo – durante a cobertura do Dia Internacional da Mulher em 2017. Os resultados indicam que a presença dos dados ainda ocorre na menor parte do material analisado, que o uso das técnicas digitais de visualização é incipiente e a oferta de dados brutos no processo de produção da notícia não existe. Por outro lado, foi possível perceber que o uso de dados e estatísticas sem indicação ou checagem de fontes é bastante frequente no corpus estudado.En la última década, el periodismo de datos creció al lado de las tecnologías de información y comunicación, tomando el campo del periodismo profesional. La propuesta de este documento es comprender si y cómo el periodismo impreso tradicional ha reaccionado a las nuevas herramientas tecnológicas disponibles para la práctica del reportaje y la creciente disponibilidad de datos abiertos, ya sean de origen público o privado. Para esta investigación se analizarán reportajes publicados en los sitios de tres grandes periódicos brasileños – Folha de S. Paulo, O Globo y O Estado de São Paulo – durante la cobertura del Día Internacional de la Mujer en 2017. Los resultados indicaron bajo uso de datos, uso incipiente de visualizaciones digitales y ninguna oferta de datos brutos. El uso de datos y estadísticas sin referencias o chequeo de las fuentes fueron frecuentes.

scholarly journals A data-driven analysis of the potential of public transport for German commuters using accessibility indicators

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tudor Mocanu ◽  
Jigeeshu Joshi ◽  
Christian Winkler
Keyword(s):  
Public Transport ◽  
Transport Model ◽  
Negative Impact ◽  
Open Data ◽  
Data Driven ◽  
Travel Times ◽  
Time Data ◽  
Mode Shift ◽  
Data Driven Approach ◽  
Household Travel

Abstract Background A significant mode shift will be required in order to meet the ambitious greenhouse gas emissions reduction targets in Germany and elsewhere. Such a mode shift can only be achieved by a combination of drastic push and pull measures. Getting commuters to switch modes might be particularly difficult and have a negative impact on their access to employment and welfare. Methodology We investigate the potential for a mode shift from car to public transport for German commuters using a data-driven approach based mainly on open data sources that avoids complex transport model runs. Different datasets on the home and workplace location of all employees in Germany are consolidated to create an origin-destination commuter matrix at traffic analysis zone level. The commuter matrix is merged with travel time data for car and public transport to calculate a spatially disaggregated and mode-specific measure of accessibility. The comparison of accessibility by car and public transport is used to derive the potential for a mode shift and identify potential challenges and barriers. Results Public transport accessibility to workplaces is poorer across the country compared to access by car. On average, public transport travel times are almost three times higher than the corresponding car travel times. The differences in accessibility are largely independent of the region type. Results are validated by an independent dataset from a household travel survey. Based on these results, the potential for a mode shift appears to be very low.

scholarly journals A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together

2021 ◽  
Vol 10 (9) ◽  
pp. 599
Author(s):  
Giuliano Cornacchia ◽  
Luca Pappalardo
Keyword(s):  
Human Mobility ◽  
Open Data ◽  
Generative Models ◽  
Data Driven ◽  
Social Aspect ◽  
Social Perspectives ◽  
Traffic Forecasting ◽  
Data Driven Approach ◽  
Temporal Dimensions ◽  
The Impact

Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.

scholarly journals Forecasting Covid-19 dynamics in Brazil: a data driven approach

2020 ◽  
Author(s):  
Igor Gadelha Pereira ◽  
Joris M Guerin ◽  
Andouglas Goncalves Silva ◽  
Cosimo Distante ◽  
Gabriel Santos Garcia ◽  
...  
Keyword(s):  
Curve Fitting ◽  
Sao Paulo ◽  
Data Driven ◽  
Advanced Stage ◽  
Main Approach ◽  
Preliminary Results ◽  
World Regions ◽  
The World ◽  
Future Data ◽  
Data Driven Approach

This paper has a twofold contribution. The first is a data driven approach for predicting the Covid-19 pandemic dynamics, based on data from more advanced countries. The second is to report and discuss the results obtained with this approach for Brazilian states, as of May 4th, 2020. We start by presenting preliminary results obtained by training an LSTM-SAE network, which are somewhat disappointing. Then, our main approach consists in an initial clustering of the world regions for which data is available and where the pandemic is at an advanced stage, based on a set of manually engineered features representing a country's response to the early spread of the pandemic. A Modified Auto-Encoder network is then trained from these clusters and learns to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks. Finally, curve fitting is carried out on the predictions in order to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated between the 25th of April and the 19th of May 2020. Predicted numbers reach a total of 240 thousand infected Brazilians, distributed among the different states, with Sao Paulo leading with almost 65 thousand estimated, confirmed cases. The estimated end of the pandemics (with 97 % of cases reaching an outcome) starts as of May 28th for some states and rests through August 14th, 2020.

scholarly journals O USO DE CORPUS ESPECIALIZADO NO ENSINO PÚBLICO DE LÍNGUA INGLESA

2017 ◽  
Vol 28 (28) ◽  
Author(s):  
Maria Carolina Zuppardi
Keyword(s):  
Sao Paulo ◽  
Data Driven ◽  
São Paulo

O objetivo deste artigo é apresentar a Linguística de Corpus como abordagem pedagógica adicional no processo ensino-aprendizagem dos estudantes da rede estadual de ensino de São Paulo. Para isso, foi coletado um corpus especializado voltado para o ensino de língua inglesa e contendo os gêneros contemplados na Proposta Curricular de São Paulo para Língua Estrangeira Moderna (LEM) no 4º bimestre do 6º ano do Ensino Fundamental – ciclo II. As informações extraídas do corpus foram usadas como subsídio para a criação de uma sequência didática baseada nas abordagens data-driven learning (Aprendizado Movido por Dados) e multimídia/multigênero. O objetivo da atividade é proporcionar a interação entre estudantes e os gêneros que permeiam as atividades sociais previstas na Proposta Curricular.

What open data tells us - Reconstructing 55 years of global land use/cover change

2020 ◽  
Author(s):  
Karina Winkler ◽  
Richard Fuchs ◽  
Martin Herold ◽  
Mark Rounsevell
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Land Surface ◽  
Open Data ◽  
Data Driven ◽  
System Modelling ◽  
Observation Data ◽  
Land Change ◽  
Global Land ◽  
Data Driven Approach

&lt;p&gt;People have increasingly been shaping the surface of our planet. Land use/cover change &amp;#8211; the most visible human footprint on Earth &amp;#8211; is one of the main contributors to greenhouse gas emissions and biodiversity loss and, hence, is a key topic for current sustainability debates and climate change mitigation. To understand these land surface dynamics and its impacts, accurate reconstructions of global land use/cover change are necessary. Although more and more observational data sets are publicly available (e.g. from remote sensing), current land change assessments are still incomplete and either lack temporal consistency, spatial explicitness or thematic detail. Here, we show a consistent reconstruction of global land use/cover change from 1960-2015, using an open data-driven approach that combines national land use statistics with earth observation data of multiple sources and scales. Our land change reconstruction model HILDA+ (Historic Land Dynamics Assessment) accounts for data-derived gross changes within six main land use/cover classes at 1 km spatial resolution: Urban areas, cropland, pastures and rangeland, forest, (semi-)natural grass- or shrubland, other land. As a result, we present yearly land use/cover maps at 1 km spatial resolution, magnitudes and hot spot areas of change. Globally, around 20 % of the land surface &amp;#8211; almost three times the size of Brazil - has undergone change within the last 55 years. Further, gross change is about seven times as high as yearly net change extent for forest, cropland and pasture dynamics. We prove that land change studies accounting for net change only can lead to severe underestimations of change extent and frequency. With this purely data-driven approach, we address current research needs of the earth system modelling community by providing new layers of land use/cover change with unprecedented level of detail. Learning from the recent past, understanding how management and land cover dynamics interactively affect the climate is essential for implementing measures of mitigation and sustainable land use policies. In this context, a solid information base can support informed decision-making.&lt;/p&gt;

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