Future Networks 2030: Challenges in Intelligent Transportation Systems

Pop, Mădălin-Dorin; Pandey, Jitendra; Ramasamy, Velmani

doi:10.1109/icrito48877.2020.9197951

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…In 2012, a smart parking management system was established, which leverages a network of sensors discretely placed along public streets. These data streams are efficiently transmitted to drivers, leading them to the most appropriate path using GPSbased mobile applications [70][71][72][73][74].…”

Section: Nicementioning

confidence: 99%

A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

Mukhopadhyay,

Kumar,

Gupta

et al. 2024

IJTDI

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This study represents the complex terrain of smart transport applications, focusing on the synergistic potential that emerges from the strategic confluence of Machine Learning (ML) and Internet of Things (IoT) methodologies. This review provides insight into how the dynamic nature and large volume of data created by IoT systems make them an excellent environment for the integration of ML approaches by exploring the interplay between these areas. Notably, a wide range of ML algorithms have been reviewed and suggested in the context of smart transportation, with a focus on critical areas such as route optimization, parking management, and accident detection/prevention. A crucial finding from this investigation is the noticeable gap in ML coverage throughout the range of smart lighting systems and parking applications. This highlights the need to refocus on these topics from an ML standpoint, opening the path for future investigation and innovation. This research tackles important topics including sustainability, cost-effectiveness, safety, and time efficiency, highlighting the fascinating possibilities of fusing IoT, ML, and smart mobility. Proactively preventing accidents, expedited parking reservations, cutting-edge street lighting, and accurate route suggestions are just a few benefits of the integration of these technologies. The study does, however, highlight the need for more research, particularly in unexplored areas like parking applications and smart lighting. By bridging these gaps and improving ML and IoT cooperation, smart transportation will be greatly improved and creative solutions for improved urban mobility will be offered.

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Section: Nicementioning

confidence: 99%

A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

Mukhopadhyay,

Kumar,

Gupta

et al. 2024

IJTDI

View full text Add to dashboard Cite

show abstract

“…Other problems revolve around vehicle-to-vehicle (V2V) communication. New certificate management systems must be created to protect the privacy, safety, and security of infrastructure communication networks [120]. V2V communications can be the subject of transmission interference or other intrusion attempts, such as seizing control of the vehicle or manipulating data transmitted to other mobile hosts, which can lead to accidents.…”

Section: Challengesmentioning

confidence: 99%

Smart Transportation: An Overview of Technologies and Applications

Oladimeji

Gupta

Kose

et al. 2023

Sensors

108

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As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.

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“…This data will be used to optimize traffic flow, predict congestion, and enhance overall transportation efficiency. Machine learning algorithms will be employed to extract meaningful insights from this data84][85] [86].…”

Section: Integration Of Its With Smart Citymentioning

confidence: 99%

Advancements in Intelligent Transportation Systems ITS and Roadside Unit RSU Design

2023

IJANSER

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This conference paper provides a comprehensive review of the latest research trends in Intelligent Transportation Systems (ITS) and Roadside Unit (RSU) design. ITS and RSUs play a pivotal role in modern transportation infrastructure, facilitating safer, more efficient, and sustainable mobility. This paper highlights key developments, emerging technologies, and research directions within these domains. We delve into topics such as vehicle-to-infrastructure (V2I) communication, edge computing for RSUs, AI-driven traffic management, and the integration of 5G and IoT into ITS. By examining recent advancements, this paper aims to contribute to the ongoing discourse on the future of transportation systems.

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Future Networks 2030: Challenges in Intelligent Transportation Systems

Cited by 7 publications

References 32 publications

A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

Smart Transportation: An Overview of Technologies and Applications

Advancements in Intelligent Transportation Systems ITS and Roadside Unit RSU Design

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