Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navig...

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Published inIEEE open journal of vehicular technology Vol. 5; pp. 397 - 427
Main Authors Khalil, Ruhul Amin, Safelnasr, Ziad, Yemane, Naod, Kedir, Mebruk, Shafiqurrahman, Atawulrahman, SAEED, NASIR
Format Journal Article
LanguageEnglish
Published IEEE 2024
Subjects
Autonomous vehicles
Deep learning
explainable AI
Intelligent transportation systems
Large language models
Surveys
Traffic control
traffic flow prediction
Transportation
Vehicle dynamics
Online AccessGet full text
ISSN2644-1330
2644-1330
DOI10.1109/OJVT.2024.3369691

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Abstract Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects, including transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.
AbstractList Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects, including transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.
Author Kedir, Mebruk
Safelnasr, Ziad
Shafiqurrahman, Atawulrahman
SAEED, NASIR
Khalil, Ruhul Amin
Yemane, Naod
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SSID ssj0002511137
Score 2.4844654
Snippet Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at...
SourceID doaj
crossref
ieee
SourceType Open Website
Enrichment Source
Index Database
Publisher
StartPage 397
SubjectTerms Autonomous vehicles
Deep learning
explainable AI
Intelligent transportation systems
Large language models
Surveys
Traffic control
traffic flow prediction
Transportation
Vehicle dynamics
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Title Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges
URI https://ieeexplore.ieee.org/document/10444919
https://doaj.org/article/7592733143b749af816c96b64d276c4c
Volume 5
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