A Review on ITS (Intelligent Transportation Systems) Technology

Ravi, Shashi; Mamdikar, Mohan Rao

doi:10.1109/icaaic53929.2022.9792638

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…This training happens within a simulation environment that mimics real-world traffic dynamics. Finally, the RL agent with XAI integration comes into play [33]. An appropriate RL algorithm like Deep Q-Networks is chosen.…”

Section: Methodsmentioning

confidence: 99%

Reinforcement Learning Approach for Traffic Congestion Prediction Empowered with Explainable Artificial Intelligence (XAI)

Waqas,

Abbas,

Farooq

et al. 2024

Preprint

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Traffic congestion in urban areas is an insistent challenge, increasing pollution levels and economic inefficiencies while reducing quality of life. Traditional traffic management systems, largely static and based on rules of thumb, struggle to dynamically adapt to ever-changing to solve the urban traffic problems. This work introduces a novel predictive framework using Reinforcement Learning enhanced with Explainable Artificial Intelligence techniques to boost both the interpretability and effectiveness of the model. A key hurdle involves securely transmitting data between the machine learning model and cloud servers, facilitated via Blockchain based smart contracts. The proposed RL approach augmented with RL-XAI represents the solution to strengthen security, privacy, and accuracy when detecting traffic jams. By using BC and RL and then evaluating outcomes through XAI, this approach achieves significantly more precise predictions and a reduced missing data rate relative to conventional Machine Learning methods and enhances security, reliability, and overall accuracy by a remarkable 5% compared to another approach.

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Section: Methodsmentioning

confidence: 99%

Reinforcement Learning Approach for Traffic Congestion Prediction Empowered with Explainable Artificial Intelligence (XAI)

Waqas,

Abbas,

Farooq

et al. 2024

Preprint

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“…The nal part employs a connected group to foretell the rates of travel of site users. [10] [17] The paper discusses the architecture of the Internet of Things (IoT) for Intelligent Transportation Systems (ITS) in a Smart City. The authors have not mentioned any speci c methodology used in the paper.…”

Section: Literature Surveymentioning

confidence: 99%

Velocious: A Resilient IoT Architecture for 6G based Intelligent Transportation System with Expeditious Movement Mechanism

Amutha

2024

Preprint

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The Internet of Things [IoT] has provided fascinating solutions in various fields. As the significant usages of IoT in smart cities incorporate almost all the domains such as healthcare, Vehicle to Vehicle (V2V), Energy sector, home automation, pollution monitoring, garbage collection, gardening, environment, road and rail transportation, education, public and private sector organization, etc., the need for developing an intelligent IoT architecture for every domain is a paramount criterion. In this research, we are interested in designing and developing, an IoT architecture, Velocious, which will provide a fast and speedy transportation system without delay and with a greater obstacle avoidance mechanism. The integration of sensors and actuators with a cognitive level of thinking based on circumstances leads to decisions with reasoning using the explainable Artificial Intelligence (AI) concept. The procedure of data acquisition, data transferring, and data interpretation requires a federated learning concept with context awareness where the location, time, and situation act as the prime context in decision-making. It can be purported that the development of a smart city relies on different sectors, and consequently the development of an Intelligent Transport System (ITS), highly relies on expansion. Development, penetration, and growth of IoT technologies with seamless integration with other domain architectures too. To lead a smart and safe travel with fuel consumption, obstacle avoidance, and traffic management decisions are mandated with explainable Artificial Intelligence (AI) which could be built in Velocious. This research provides Velocious to be applicable to incorporate integrating all the necessary information required for setting up decisions and making rules. These types of rules can also be viewed as a rule set with proper explanations in decision-making in critical circumstances. Meanwhile, the developed Velocious IoT architecture can also be integrated seamlessly with other IoT architectures for smart Energy, smart health care, etc., for the development of smart cities.

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“…Within ITS, various components, such as parking guidance systems, vehicle-to-vehicle communication (V2V), and vehicle-to-infrastructure (V2I), contribute to improved connectivity. Moreover, wireless technologies like wireless sensor networks, global positioning systems (GPS), and the Internet of Things (IoT) are leveraged to promote smoother traffic flow and reduce congestion, making ITS a pivotal tool in addressing environmental concerns in addition to its traffic management role [34]- [40]. Unique communication approaches, such as "Dedicated Short-Range Communications" (DSRC) and "Continuous Air Interface Long and Medium Range" (CAILM), employ radio, cellular, and infrared connectivity for effective V2V and V2I communication [41].…”

Section: A Conventional Its Systemmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

Preprint

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A Review on ITS (Intelligent Transportation Systems) Technology

Cited by 13 publications

References 7 publications

Reinforcement Learning Approach for Traffic Congestion Prediction Empowered with Explainable Artificial Intelligence (XAI)

Reinforcement Learning Approach for Traffic Congestion Prediction Empowered with Explainable Artificial Intelligence (XAI)

Velocious: A Resilient IoT Architecture for 6G based Intelligent Transportation System with Expeditious Movement Mechanism

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

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