  In the past few years, several systems have been proposed to deal with issues
related to the vehicular traffic management. Usually, their solutions include
the integration of computational technologies such as vehicular networks,
central servers, and roadside units. Most systems use a hybrid approach, which
means they still need a central entity (central server or roadside unit) and
Internet connection to find out an en-route event as well as alternative routes
for vehicles. It is easy to understand the need for a central entity because
selecting the most appropriate vehicle to perform aforementioned procedures is
a difficult task. In addition to that, as far as we know, there are very few
systems that apply the altruistic approach (not selfish behavior) to routing
decisions. Because of that, the issue addressed in this work is how to perform
the vehicular traffic management, when an en-route event is detected, in a
distributed, scalable, and cost-effective fashion. To deal with these issues,
we proposed a distributed vehicle traffic management system, named as dEASY
(distributed vEhicle trAffic management SYstem). The dEASY system was designed
and implemented on a three-layer architecture, namely environment sensing and
vehicle ranking, knowledge generation and distribution, and knowledge
consumption. Each layer of the dEASY architecture is responsible for dealing
with the main issues that were not addressed in related works or could be
improved. Simulation results have shown that, compared with other systems from
the literature, our proposed system has lower network overhead due to applied
vehicle selection and broadcast suppression mechanisms. In average, dEASY also
outperformed all other competitors in what regards to the travel time and time
lost metrics. Through the analysis of results, it is possible to conclude that
our infrastructure-less system is scalable and cost-effective.
