Public Value of Intelligent Transportation System

Blanes, Ramona; Paton, Robert A.; Docherty, Iain

doi:10.1109/hicss.2015.170

Cited by 3 publications

(2 citation statements)

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“…This labeling process is used to compare and evaluate the prediction class during training. However, we have to remember that during runtime in the VDTN environment this labeling process must not be done, because we are only interested in identifying the samples with the best fitness (that is, the samples with the highest f value), which are directly obtained after the forward pass by the sigmoid function (see Equations (3) and 4).…”

Section: Host Groupsmentioning

confidence: 99%

“…As urban environments have exponential grow, smart cities (SC) is the technological paradigm that aims at providing the ultimate solution to the urban development in every aspect in wide areas such as social management, security and safety, health and medical care, smart living, tourism, and transportation, with the aid of sensors and electronics, communication networks, and information technologies [1,2]. Among the essential needs and key components of a smart city are intelligent transportation systems, which seek to provide a solution to transportation-related problems, such as pollution, traffic congestions, and accident reduction [3,4]. In this sense, vehicular networks play a key role by providing a communication framework for moving vehicles, road infrastructure, and pedestrians [5].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

2019

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The exponential growth of cities has brought important challenges such as waste management, pollution and overpopulation, and the administration of transportation. To mitigate these problems, the idea of the smart city was born, seeking to provide robust solutions integrating sensors and electronics, information technologies, and communication networks. More particularly, to face transportation challenges, intelligent transportation systems are a vital component in this quest, helped by vehicular communication networks, which offer a communication framework for vehicles, road infrastructure, and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between nodes that may be moving at very high speeds very difficult to achieve, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this paper, we address this problem using artificial intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path. Furthermore, we propose a deep learning–based router (DLR+), a router with a prioritized type of message scheduler and a routing algorithm based on deep learning. Simulations done to assess the router performance show important gains in terms of network overhead and hop count, while maintaining an acceptable packet delivery ratio and delivery delays, with respect to other popular routing protocols in vehicular networks.

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Section: Host Groupsmentioning

confidence: 99%