The Contribution of Open Big Data Sources and Analytics Tools to Sustainable Urban Mobility

The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders.

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Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

10.2196/preprints.24504 ◽

2020 ◽

Author(s):

Bankole Olatosi ◽

Jiajia Zhang ◽

Sharon Weissman ◽

Zhenlong Li ◽

Jianjun Hu ◽

...

Keyword(s):

Big Data ◽

South Carolina ◽

Data Science ◽

Age Groups ◽

The Elderly ◽

The United States ◽

Data Sources ◽

Patient Registries ◽

Multiple Partner ◽

Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

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Methodology of Big Data Integration from A Priori Unknown Heterogeneous Data Sources

Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence - CSAI '18 ◽

10.1145/3297156.3297249 ◽

2018 ◽

Author(s):

Alexey Samoylov ◽

Nikolay Sergeev ◽

Margarita Kucherova ◽

Boris Denisov

Keyword(s):

Big Data ◽

Data Integration ◽

A Priori ◽

Heterogeneous Data ◽

Data Sources ◽

Heterogeneous Data Sources

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Challenges of Sustainable Urban Mobility Integration

22nd International Conference on Human-Computer Interaction with Mobile Devices and Services ◽

10.1145/3406324.3426767 ◽

2020 ◽

Author(s):

Dirk Ahlers

Keyword(s):

Urban Mobility ◽

Sustainable Urban Mobility

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Use of the Sustainable Mobility Efficiency Index (SMEI) for Enhancing the Sustainable Urban Mobility in Greek Cities

Sustainability ◽

10.3390/su13041709 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1709

Author(s):

Maria Morfoulaki ◽

Jason Papathanasiou

Keyword(s):

Multicriteria Analysis ◽

Efficiency Index ◽

Urban Mobility ◽

Specific Procedure ◽

Sustainable Mobility ◽

European Cities ◽

Step Procedure ◽

Directorate General ◽

Alternative Measures ◽

Sustainable Urban Mobility

Since 2013, the European cities have been encouraged to develop local Sustainable Urban Mobility Plans (SUMPs) according to the specific procedure that was launched by the Directorate-General for Mobility and Transport (DG Move) and updated in 2019. One of the most critical steps in this 12-step procedure is the assessment—with specific criteria—of all the alternative measures and infrastructure, which will be optimally combined, in order to better satisfy the problems and the achieve the vision of each area. The aim of the current work is to present the development and implementation of a methodological framework based on the use of multicriteria analysis. The framework targets the capturing of opinions of the relevant local experts in order to evaluate alternative sustainable mobility measures, and also prioritize them using the Sustainable Mobility Efficiency Index (SMEI).

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Key Challenges in the Status Analysis for the Sustainable Urban Mobility Plan in Podgorica, Montenegro

Sustainability ◽

10.3390/su13031037 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1037

Author(s):

Radoje Vujadinović ◽

Jelena Šaković Jovanović ◽

Aljaž Plevnik ◽

Luka Mladenovič ◽

Tom Rye

Keyword(s):

Traffic Accidents ◽

Road Traffic ◽

Motor Vehicles ◽

Urban Mobility ◽

Passenger Cars ◽

Study Program ◽

Wide Range ◽

The Status ◽

Sustainable Urban Mobility ◽

Status Analysis

The paper presents the results of the application of a practical approach for collecting data, which provides a simple, cost efficient, and easily reproducible method that was applied to obtain the necessary data for the status analysis of the Sustainable Urban Mobility Plan (SUMP) for Podgorica, the capital of Montenegro. Important data for the estimation of the existing condition of the traffic system were collected through desk research from the appropriate institutions and organizations. Several surveys and focus group interviews were conducted, in which about 5000 residents of Podgorica participated. In addition to answering questions, residents made numerous suggestions, confirming the correctness of a participatory approach in the new traffic planning paradigm that provides the SUMP with crucial advantages. A manual cordon count of traffic on five bridges for the traffic of the motor vehicles, as well as on two pedestrian-only bridges, was performed by students from the study program Road Traffic, and there are plans to repeat this in the coming years in order to enable more reliable monitoring and evaluation of the obtained data. Contemporary quality management tools such as BYPAD and ParkPAD were also used to assess the status of cycling and parking policy, respectively. It is especially important to emphasize that Podgorica is the first city in the West Balkans, and the fourth city in Europe, in which the ParkPAD tool was applied. A wide range of negative phenomena and trends was identified, like a rapid increase in the number of registered vehicles, an increase in the motorization rate and the number of traffic accidents, increased non-compliance with traffic rules, excessive use of passenger cars and auto-taxi vehicles, insufficient use of unattractive public transport, walking and cycling, etc. Based on the data collected, key challenges in status analysis in Podgorica were identified, which the SUMP should try to overcome.

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Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

Journal of Pension Economics and Finance ◽

10.1017/s1474747221000044 ◽

2021 ◽

pp. 1-5

Author(s):

Marco Angrisani ◽

Anya Samek ◽

Arie Kapteyn

Keyword(s):

Big Data ◽

Academic Research ◽

Cost Effective ◽

Research Data ◽

Data Sources ◽

Special Issue ◽

Administrative Records ◽

The Past ◽

Survey Questionnaires ◽

Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

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Mapping the United Nations Fundamental Principles of Official Statistics against new and big data sources

Statistical Journal of the IAOS ◽

10.3233/sji-210789 ◽

2021 ◽

Vol 37 (1) ◽

pp. 161-169

Author(s):

Dominik Rozkrut ◽

Olga Świerkot-Strużewska ◽

Gemma Van Halderen

Keyword(s):

Big Data ◽

Public Information ◽

Fundamental Principle ◽

Data Sources ◽

Official Statistics ◽

Development Agenda ◽

Data Gaps ◽

Data Source ◽

Exciting Time ◽

Statistical Systems

Never has there been a more exciting time to be an official statistician. The data revolution is responding to the demands of the CoVID-19 pandemic and a complex sustainable development agenda to improve how data is produced and used, to close data gaps to prevent discrimination, to build capacity and data literacy, to modernize data collection systems and to liberate data to promote transparency and accountability. But can all data be liberated in the production and communication of official statistics? This paper explores the UN Fundamental Principles of Official Statistics in the context of eight new and big data sources. The paper concludes each data source can be used for the production of official statistics in adherence with the Fundamental Principles and argues these data sources should be used if National Statistical Systems are to adhere to the first Fundamental Principle of compiling and making available official statistics that honor citizen’s entitlement to public information.

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