文章基本信息

标题：DEEP LEARNING BASED BIG DATA ANALYTICS ON TRAFFIC CONGESTION IN URBAN INTELLIGENT TRANSPORTATION SYSTEM
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作者：E. Kalaivanan ; S. Brindha
期刊名称：International Journal of Early Childhood Special Education
电子版ISSN：1308-5581
出版年度：2022
卷号：14
期号：3
页码：9008-9010
DOI：10.9756/INT-JECSE/V14I3.1043
语种：English
出版社：International Journal of Early Childhood Special Education
摘要：It is possible to think of smart urban transportation management as a multidimensional big data problem. Information collected from diverse, extensive, and heterogeneous data sources as well as the capacity to extract actionable insights from them are critical to the success of this strategy. In addition to data, full-stack information and communication technology (ICT) solutions must be specially implemented to handle the difficulties of smart cities. Various levels of traffic congestion can be found in metropolitan areas. Such issues have a negative impact on travel experiences while also raising the probability of being involved in an accident. In this paper, we look at the urban intelligent transportation system (ITS) that is based on GPS and GIS technology, both of which are analysed with deep learning architectures. This work also looks at how these technologies work together. The simulation is conducted in sumo simulator to test the efficacy of the model in urban ITS scenario and the results show that the proposed model is better in predicting the traffic flow than other methods.
关键词：Deep Learning;Analytics;Big Data;Traffic Congestion;Intelligent Transportation System