Vehicular Networks to Intelligent Transportation Systems

2018 ◽  
pp. 297-315 ◽  
Author(s):  
Felipe Cunha ◽  
Guilherme Maia ◽  
Heitor S. Ramos ◽  
Bruno Perreira ◽  
Clayson Celes ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Intelligent Transportation ◽  
Transportation Systems

scholarly journals Instantly Decodable RaptorQ Intersessions in Vehicular Adhoc Networks

2020 ◽  
Vol 21 (2) ◽  
pp. 134-148
Author(s):  
Nandhini Vineeth ◽  
H. S. Guruprasad
Keyword(s):  
Automobile Industry ◽  
Intelligent Transportation Systems ◽  
Vehicular Networks ◽  
Emergency Services ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Unmanned Vehicles ◽  
End To End Delay ◽  
Adhoc Networks ◽  
Vehicular Adhoc Networks

AbstractVehicular networks that deal with sharing of information among vehicles are gaining popularity among the automobile industry as well as the researchers. These networks are prevalent under the umbrella of Intelligent Transportation Systems (ITS) and deal with data that belongs to either the emergency category or the entertaining category. In case of emergency services, it is clear that - earlier the reception of information, lesser the commotion. The objective of this work thus has been the reduction of the end to end delay when video files are exchanged among vehicles during intersessions. The set objective is accomplished through the design and development of the technique “Instantly Decodable RaptorQ Inter-Sessions” (IDRQIS) for Vehicular Adhoc Networks and the results obtained show that this outperforms the existing popular techniques – the Network Coding and RaptorQ when applied independently to the same environment. This technique can also be applied to the upcoming unmanned vehicles.

Software-Defined Vehicular Networks (SDVN) for Intelligent Transportation Systems (ITS)

2021 ◽  
pp. 305-327
Author(s):  
Rinki Sharma
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Intelligent Transportation ◽  
Vehicular Network ◽  
Transportation Systems ◽  
Vehicular Communication ◽  
Research Issues ◽  
Open Research

Vehicular communication is going to play a significant role in the future intelligent transportation systems (ITS). Due to the highly dynamic nature of vehicular networks (VNs) and need for efficient real-time communication, the traditional networking paradigm is not suitable for VNs. Incorporating the SDN technology in VNs provides benefits in network programmability, heterogeneity, connectivity, resource utility, safety and security, routing, and traffic management. However, there are still several challenges and open research issues due to network dynamicity, scalability, heterogeneity, interference, latency, and security that need to be addressed. This chapter presents the importance of vehicular communication in future ITS, the significance of incorporating the SDN paradigm in VNs, taxonomy for the role of SDVN, the software-defined vehicular network (SDVN) architecture, and open research issues in SDVN.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

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