Vehicular Networks to Intelligent Transportation Systems

Maia, Guilherme; Ramos, Heitor S.; Perreira, Bruno; Celes, Clayson; Campolina, Andre B.; Rettore, Paulo H. L.; Guidoni, Daniel L.; Sumika, Fernanda; Villas, Leandro A.; Mini, Raquel A. F.; Loureiro, Antônio A. F.

doi:10.1007/978-981-13-0396-8_15

Cited by 20 publications

(12 citation statements)

References 32 publications

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“…ITS makes use of solutions to control and manage traffic, communication technologies to transmit the data generated by the ECUs to servers on the cloud to be processed and analyzed to solve urban and road traffic issues. [5][6][7][8] One of the goals of ITS is to improve vehicle passengers' safety by using roadside infrastructures to detect possible dangerous environmental and road conditions (ahead of time) to reduce the possibility of accidents or inconveniences. Several challenges in the ITS domain still need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

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Section: Introductionmentioning

confidence: 99%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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“…Due to the rapid increase in population and the number of vehicles, traffic congestion has had a major impact on day-to-day life. Many scholars have done a great deal of research on improving traffic congestion, such as on bus priority strategies [1,2], mass rapid transit [3,4], parking management [5,6], intelligent transportation systems [7,8], etc. As an important component of intelligent transportation systems, the efficient operation of intersections has several advantages for the transportation in a city.…”

Section: Introductionmentioning

confidence: 99%

A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

et al. 2019

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Setting up an exclusive left-turn lane and corresponding signal phase for intersection traffic safety and efficiency will decrease the capacity of the intersection when there are less or no left-turn movements. This is especially true during rush hours because of the ineffective use of left-turn lane space and signal phase duration. With the advantages of vehicle-to-infrastructure (V2I) communication, a novel intersection signal control model is proposed which sets up variable lane direction arrow marking and turns the left-turn lane into a controllable shared lane for left-turn and through movements. The new intersection signal control model and its control strategy are presented and simulated using field data. After comparison with two other intersection control models and control strategies, the new model is validated to improve the intersection capacity in rush hours. Besides, variable lane lines and the corresponding control method are designed and combined with the left-turn waiting area to overcome the shortcomings of the proposed intersection signal control model and control strategy.

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“…Vehicular ad hoc networks (VANETs) are a special mobile ad hoc network for implementing various road traffic applications [1]. It can improve the future development of the transportation system via wireless communication technologies, such as advanced information processing, wireless communication, and device-to-device communication [2,3].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of Functional Device for Vehicular Cloud Computing

Sha

Wang

et al. 2019

Electronics

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Relay technology application becomes prevalent nowadays, as it can effectively extend the communication distance, especially for vehicular networks with a limited communication range. Combined with vehicular cloud (VC), transmission efficiency can be improved by offloading partial data. Hence, designing a vehicle relay algorithm and implementation embedded vehicle device is critical. In this paper, VC is considered to deal with the complexity computation in our proposed system model. Without a loss of generality, an end-to-end vehicle communication with one assisted vehicle is analyzed firstly on a transmission link based on VC. Here, the signal-to-noise ratio (SNR) on the receiving end and link outage probability is obtained to enhance the link reliability. The VC computing helps us further simplify computational complexity. Subsequently, an embedded vehicle-enabled device is designed to achieve the optimal path relay selection in realistic vehicular environments. In the functional device framework, we display an optimal path relay selection algorithm according to the link quality. Finally, the performance of the transmission link on the outage probability related with SNR is verified in the simulation results. Meanwhile, the effect of the relay gain is also analyzed. The application of a vehicle-enabled embedded device could improve the performance of vehicular networks.

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Vehicular Networks to Intelligent Transportation Systems

Cited by 20 publications

References 32 publications

Deep learning support for intelligent transportation systems

Deep learning support for intelligent transportation systems

A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

Analysis and Design of Functional Device for Vehicular Cloud Computing

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