VANET constitutes a huge research area due to its potential in traffic management and road safety. In this paper, we propose a novel, smart, and compact representation of vehicular networks. Starting from the standard graph representation, we extract a signal assigning a congestion factor to each vehicle, so that highly jammed traffic areas can be immediately detected by identifying the highest peaks of the wave. The way the signal is built provides useful information about vehicles distribution throughout the network, producing as result a simple but very meaningful wave characterizing the corresponding VANET. © 2019, Springer Nature Switzerland AG.
A Smart Compact Traffic Network Vision Based on Wave Representation / Balzano, W.; Murano, A.; Sorrentino, L.; Stranieri, S.. - 927:(2019), pp. 870-879. (Intervento presentato al convegno 3rd International Conference on Advanced Information Networking and Applications, AINA 2019) [10.1007/978-3-030-15035-8_85].
A Smart Compact Traffic Network Vision Based on Wave Representation
Balzano, W.;Murano, A.;Sorrentino, L.;Stranieri, S.
2019
Abstract
VANET constitutes a huge research area due to its potential in traffic management and road safety. In this paper, we propose a novel, smart, and compact representation of vehicular networks. Starting from the standard graph representation, we extract a signal assigning a congestion factor to each vehicle, so that highly jammed traffic areas can be immediately detected by identifying the highest peaks of the wave. The way the signal is built provides useful information about vehicles distribution throughout the network, producing as result a simple but very meaningful wave characterizing the corresponding VANET. © 2019, Springer Nature Switzerland AG.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
