Abstract
This paper tackles Challenge 4, ‘Computational Urban Data Analytics’, of the 2020 Smoky Mountains Conference Data Challenge. Specifically, we design and implement an analysis and visualization framework to study traffic emissions across time and space in a urban setting. We use our framework to qualitatively and quantitatively analyze the influence of urban layout on traffic flows in the Chicago Loop area. Our findings allow us to investigate the relationships between traffic congestion, building distributions, and vehicle emissions. Insights from our framework can provide communities with decision-making tools for urban design and smart cities.
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References
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Acknowledgment
Supported by IBM Shared University (SUR) Award and NSF awards IIS 1841758 and CCF 1841758.
The authors wish to thank Silvina CaÃno-Lores, Travis Johnston, and Michela Taufer for their mentorship during this project.
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Hathaway, C., Mobo, S. (2020). A Framework for Linking Urban Traffic and Vehicle Emissions in Smart Cities. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_33
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DOI: https://doi.org/10.1007/978-3-030-63393-6_33
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