Abstract
Understanding human activities and urban mobility patterns is key to solving many urban issues such as congestion and emissions. With the abundant data sets available at different levels of fidelity, one of the main challenges is the sparsity and heterogeneity of data sources. The integration of such data sources is essential to better inform system design and community-level strategies. In this paper, we incorporate a variety of data sources including land use, vehicle emissions and building footprint to comprehensively visualize and analyze traffic patterns in the Chicago Loop area. We first implement and compare three different nearest-neighbor-search algorithms to determine building occupancy assignment, and then perform a spatial-temporal correlation analysis of vehicle emissions focusing on factors such as land use, public transit and demographic. Lastly, we discuss the traffic characteristics from data analysis, such as traffic congestion formation and rush hours etc.
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Acknowledgement
This material is based upon work supported by the National Science Foundation under Grant No. CMMI-1727785 (Hu), CMMI-1853913 (Wang), and USDOT Dwight D. Eisenhower Fellowship program under Grant No. 693JJ31945012 (Wang).
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Qu, A., Wang, Y., Hu, Y., Wang, Y., Baroud, H. (2020). A Data-Integration Analysis on Road Emissions and Traffic Patterns. 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_34
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