A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

Zhu, Weilong; Zhu, Chunsheng; Lin, Zhiyun

doi:10.1109/jiot.2023.3255200

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…Because of the unpredictable flow of traffic from both directions, resources are being wasted because the signals are not uniform. Microelectronic devices with built-in artificial intelligence are referred to as "smart" because of their "smart" capabilities [12]. The development of increasingly sophisticated computational methods paved the way for the emergence of intelligence technology.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations

Anitha,

Sharma,

Nassa

et al. 2024

JMC

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Intelligent traffic monitoring is a prominent topic of investigation due the emergence of advancements like the Internet interconnected Things and intelligent computers. Combining these technologies will make it easier to methods to aid in making better choices and accelerating urban growth. Intelligent sensing has come to the forefront in recent years due to its capacity to make calculated decisions on its own to address difficult issues. Automatic vehicles and smart gadgets are equipped with sensors that are part of an IoT-based system in order to recognize, gather, and transmit data. Artificial intelligence (AI)-based techniques allow machines to acquire knowledge and keep tabs on their surroundings through continuous sensing. Improvements in variable traffic control strategies for overcrowded cities have numerous positive outcomes, one of which is increased road safety. Since the sensors on which conventional dynamic controllers relied had their own shortcomings, we might use vision sensors (like cameras) to avoid these issues. Image and video-based computing has a lot of potential for measuring traffic volumes. A new traffic management system named Enhanced Transportation Technologies (ETT) is implemented to relieve congestion at the busy intersection after the old one was deemed to be inadequate. The term "intelligent transportation system" (ITS) refers to a group of transportation systems to keep drivers and passengers safe on the road and to facilitate autonomous mobility by optimizing control systems. To further improve urban planning, crowd behavior, and traffic forecasting, dependable AI models have been developed to work in tandem with ITS. Compared to controllers using conventional sensors, the proposed model has been shown through extensive simulations to reduce waiting time and increase movement speed on average.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations

Anitha,

Sharma,

Nassa

et al. 2024

JMC

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“…As the urban population is increasing, there are several challenges faced by large metropolitan cities. Some of these challenges include vehicle route guidance to avoid traffic jams [1,2], the effective utilization of the health care system [3,4], and efficient waste management [5,6]. The advancement in technology has provided several techniques and tools that can assist in solving these challenges, thus making way for smarter and cleaner cities [7,8].…”

Section: Introductionmentioning

confidence: 99%

Efficient IoT-Assisted Waste Collection for Urban Smart Cities

Khan,

Ali,

Alharbi

et al. 2024

Sensors

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Waste management is one of the many major challenges faced by all urban cities around the world. With the increase in population, the current mechanisms for waste collection and disposal are under strain. The waste management problem is a global challenge that requires a collaborative effort from different stakeholders. Moreover, there is a need to develop technology-based solutions besides engaging the communities and establishing novel policies. While there are several challenges in waste management, the collection of waste using the current infrastructure is among the top challenges. Waste management suffers from issues such as a limited number of collection trucks, different types of household and industrial waste, and a low number of dumping points. The focus of this paper is on utilizing the available waste collection transportation capacity to efficiently dispose of the waste in a time-efficient manner while maximizing toxic waste disposal. A novel knapsack-based technique is proposed that fills the collection trucks with waste bins from different geographic locations by taking into account the amount of waste and toxicity in the bins using IoT sensors. Using the Knapsack technique, the collection trucks are loaded with waste bins up to their carrying capacity while maximizing their toxicity. The proposed model was implemented in MATLAB, and detailed simulation results show that the proposed technique outperforms other waste collection approaches. In particular, the amount of high-priority toxic waste collection was improved up to 47% using the proposed technique. Furthermore, the number of waste collection visits is reduced in the proposed scheme as compared to the conventional method, resulting in the recovery of the equipment cost in less than a year.

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IoT-Enabled Real-Time Traffic Monitoring and Control Management for Intelligent Transportation Systems

Dui,

Zhang,

Liu

et al. 2024

IEEE Internet Things J.

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A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

Cited by 3 publications

References 36 publications

Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations

Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations

Efficient IoT-Assisted Waste Collection for Urban Smart Cities

IoT-Enabled Real-Time Traffic Monitoring and Control Management for Intelligent Transportation Systems

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