首页|Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions
Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions
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NETL
NSTL
Assoc Computing Machinery
Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial phases. The research community is working tirelessly to improve the reliability of ITS by designing new protocols, standards, and connectivity paradigms. In the recent past, several surveys have been conducted that focus on Big Data Intelligence for ITS, yet none of them have comprehensively addressed the fundamental challenges hindering the widespread adoption of autonomous vehicles on the roads. Our survey aims to help readers better understand the technological advancements by delving deep into Big Data architecture, focusing on data acquisition, data storage, and data visualization. We reviewed sensory and non-sensory platforms for data acquisition, data storage repositories for archival and retrieval of large datasets, and data visualization for presenting the processed data in an interactive and comprehensible format. To this end, we discussed the current research progress by comprehensively covering the literature and highlighting challenges that urgently require the attention of the research community. Based on the concluding remarks, we argued that these challenges hinder the widespread presence of autonomous vehicles on the roads. Understanding these challenges is important for a more informed discussion on the future of self-driven technology. Moreover, we acknowledge that these challenges not only affect individual layers but also impact the functionality of subsequent layers. Finally, we outline our future work that explores how resolving these challenges could enable the realization of innovations such as smart charging systems on the roads and data centers on wheels.
Intelligent transportation systemsbig datasensorsstoragevisualizationdata centers on the wheelssmart computers on the roads
MIAN AHMAD JAN、MUHAMMAD ADIL、BOUZIANE BRIK、SAAD HAROUS、SOHAIL ABBAS
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Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
Department of Computer Science and Engineering, University at Buffalo, Buffalo, United States
NETL
NSTL
Assoc Computing Machinery
