Analysis the Influencing Factors of Urban Traffic Flows by Using New and Emerging Urban Big Data and Deep Learning

Li, Y., Zhao, Q. and Wang, M. (2022) Analysis the Influencing Factors of Urban Traffic Flows by Using New and Emerging Urban Big Data and Deep Learning. In: XXIVth ISPRS Congress, Nice, France, 6-11 June 2022, pp. 537-543. (doi: 10.5194/isprs-archives-XLIII-B4-2022-537-2022)

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Urban traffic analysis has acted an important role in the process of urban development, which can provide insights for urban planning, traffic management and resource allocation. Meanwhile, the advancement of Intelligent Transportation Systems has produced a variety of traffic-related data from sensors and cameras to monitor urban traffic conditions in high spatio-temporal resolution. This research applies spatial regression models combined with computer vision and deep learning to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. We include road characteristics and surrounding environments such as land use/cover, nearby points of interest (POI) and Google Street View images. The results show that the daily average traffic flow on main roads is much higher than smaller roads, and nearby POIs numbers have positive effect on traffic flows. The impact of land cover type is insignificant in the linear regression model, while demonstrates significant contribution to traffic flows in spatial regression models. Although the spatial autocorrelation still exists after the spatial regression, the spatial error model generates a better fit on the dataset. Further analysis will focus on extend the current model with the time parameters and understand what influence the changes of traffic flow in the different spatio-temporal scales.

Item Type:Conference Proceedings
Additional Information:The first author is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China. Dr. Qunshan Zhao has received UK ESRC’s on-going support for the Urban Big Data Centre (UBDC) [ES/L011921/1 and ES/S007105/1].
Glasgow Author(s) Enlighten ID:Wang, Dr Mingshu and Zhao, Dr Qunshan
Authors: Li, Y., Zhao, Q., and Wang, M.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
College of Science and Engineering > School of Geographical and Earth Sciences
Journal Name:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Copyright Holders:Copyright © 2022 The Authors
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190698Urban Big Data Research CentreNick BaileyEconomic and Social Research Council (ESRC)ES/L011921/1S&PS - Urban Big Data
304042UBDC Centre TransitionNick BaileyEconomic and Social Research Council (ESRC)ES/S007105/1S&PS - Administration