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Smart city data analysis

Published: 01 October 2018 Publication History

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Abstract

Smart City is one of the vital issues in the next coming years as it is estimated that more number of people will be migrating towards city and by 2040 cities is populated by 70% of the world's population. This will give raise to the city management problems like traffic congestion, parking queues, capacity planning and continuous depreciation in energy. Hence there is a need to understand the upcoming issues and find an efficient way to resolve them by resource planning.
The evolution of city to Smart City demand information and communications technology (ICT) to gather data. The data is collected based on city infrastructure and conduct of people in the city. This big data is analyzed to make knowledgeable decisions to give quality of life to the citizens. It is referred as the evolution of intelligent city to the next generation city of information technology. ICT provides real time processing of data to gather useful information. Based on this information visual applications can be developed to have a more farsighted view of the Smart City. Online banking, online shopping and remote video communication are some of the applications of Smart City.
As one of the concepts to improve city structure and its facilities, Smart City is vital in city management and development and uses information and communication technology (ICT) for city evolution and fast growth. These cities are planned to enhance more business and industrial growth thus increasing in economic development and raising living standard of the inhabitants [1]. It offers various facilities to the city like better transportation in the city, more job opportunities, less travelling time, healthy environment, better healthcare, less crime etc.
Smart City is a concept which works with sensors and data analytics to enhance the standard and quality of life in the cities. Smart City deals with the challenges like traffic, parking, capacity planning, energy etc. and resolves them to provide better city facilities to their citizens [2]. ICT is becoming widespread in the cities and is being used as a tool to deal with Smart City application domains. Smart City is using big data to generate intelligent information systems which support decision making capabilities. With effective data sources and data analytics tool we can design high end services for the citizens in urban cities. Big data has changed the way we store data and process the data efficiently. Now this crucial data is not only used to get the historic trends information but also used to predict the future. It is significant in planning the future of the cities. Data is analyzed to develop action plans from the predicted results.
In this paper, various datasets for traffic management and parking garage management have been tested and evaluated by using data analytics techniques in order to find a pattern in a historical information. Based on this research this information is further analyzed by machine learning tools and algorithms to predict the future behavior and help on taking decision. Machine learning and Analytics tools for Data Analytics from Hadoop and Elasticsearch environments as well as Mining algorithms from Python library are used for data prediction.
This paper gives us the details of some domain applications of Smart City listed as below:
• Smart City Traffic Management
• Smart City Parking Management.

References

[1]
Li Hao et al., "The application and implementation research of smart city in China", "System Science and Engineering (ICSSE)", Dalian, Liaoning, 2012, © IEEE
[2]
Irfan Ahmed Halepoto et al., "Multi-criteria assessment of smart city transformation based on SWOT analysis", "Information Technology: Towards New Smart World (NSITNSW)", Riyadh, Saudi Arabia, 2015, © IEEE
[3]
Zaheer Khan et al., "Cloud Based Big Data Analytics for Smart Future Cities", "Utility and Cloud Computing (UCC)", Dresden, 2013, © IEEE
[4]
GSMA, Connecting Living. GSMA Smart Cities Guide: traffic Management. Why mobile operators are key partners for cities seeking to deploy sustainable traffic management solutions, 2016, © GSMA.
[5]
Vangipuram Radhakrishna, Shadi A. Aljawarneh, Puligadda Veereswara Kumar, and Kim-Kwang Raymond Choo. 2018. A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns. Soft Comput. 22, 6 (March 2018), 1903--1919.
[6]
Shadi A. Aljawarneh, Ali Alawneh, and Reem Jaradat. 2017. Cloud security engineering. Future Gener. Comput. Syst. 74, C (September 2017), 385--392.
[7]
Shadi A. Aljawarneh, Radhakrishna Vangipuram, Veereswara Kumar Puligadda, and Janaki Vinjamuri. 2017. G-SPAMINE. Future Gener. Comput. Syst. 74, C (September 2017), 430--443.

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DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
October 2018
274 pages
ISBN:9781450365369
DOI:10.1145/3279996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 October 2018

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Author Tags

  1. big data
  2. capacity planning
  3. city management
  4. data analytics
  5. insights
  6. mining algorithms
  7. parking management
  8. random forest
  9. smart city
  10. traffic congestion
  11. traffic management

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  • (2024)Exploiting Physical Referent Features as Input for Multidimensional Data Selection in Augmented RealityACM Transactions on Computer-Human Interaction10.1145/364861331:4(1-40)Online publication date: 19-Sep-2024
  • (2024)Quantum Smart World Era—A Digital Innovative PerspectiveData Management, Analytics and Innovation10.1007/978-981-97-3242-5_41(625-639)Online publication date: 23-Jul-2024
  • (2023)A Slim Digital Twin for a Smart City and Its ResidentsProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628936(8-15)Online publication date: 7-Dec-2023
  • (2023)Cyber Physical Social Systems for the Blind: A New Way to Connect2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC)10.1109/ICRTAC59277.2023.10480853(120-125)Online publication date: 14-Dec-2023
  • (2023)Ridesharing and Crowdsourcing for Smart Cities: Technologies, Paradigms and Use CasesIEEE Access10.1109/ACCESS.2023.324326411(18038-18081)Online publication date: 2023
  • (2022)Impact of COVID-19 on Cloud Business IntelligenceResearch Anthology on Business Continuity and Navigating Times of Crisis10.4018/978-1-6684-4503-7.ch068(1406-1416)Online publication date: 2022
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  • (2022)Fraudulent Transactions Prediction Using Deep Neural Network2022 International Conference on Engineering & MIS (ICEMIS)10.1109/ICEMIS56295.2022.9914349(1-7)Online publication date: 4-Jul-2022
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