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Intelligent Transportation Systems (ITS) demand sophisticated vehicular networks integrating entities from the transportation sector. The ability to plan and manage such networks represents a key challenge for moving ITS solutions from laboratories into the streets. In this work, we propose Gamma Deployment as a metric for evaluating the distribution of(More)
In this article, we describe a strategy for planning the roadside infrastructure for vehicular networks based on the global behavior of drivers. Instead of relying on the trajectories of all vehicles, our proposal relies on the migration ratios of vehicles between urban regions in order to infer the better locations for deploying the roadside units. By(More)
Gamma Deployment is a metric for evaluating the distribution of roadside units in vehicular networks in terms of two parameters: a) the inter-contact time between vehicles and the infrastructure; and, b) the share of vehicles that must respect the inter-contact time guarantees. We envision the use of the Gamma Deployment metric when the network designer(More)
This paper presents a Cut-and-Branch algorithm for the Multicommodity Traveling Salesman Problem (MTSP), a useful variant of the Traveling Salesman Problem (TSP). The MTSP presents a more general cost structure, allowing for solutions that consider the quality of service to the customers, delivery priorities and delivery risk, among other possible(More)
Demand Responsive Transport (DRT) systems emerge as an alternative to deal with the problem of variable demand, or even unpredictable, occurring in conventional urban transport systems. It can be seen in some practical situations such as public transport in rural areas, wherein in some situations, there is no way to predict demand. This paper addresses the(More)
The minimum latency problem, also known as traveling repairman problem, the Deliveryman problem and the traveling salesman problem with cumulative costs is a variant of the Traveling Salesman Problem in which a repairman is required to visit customers located on each node of a graph in such a way that the overall waiting times of these customers is(More)
This work presents a mixed load algorithm to solve the School Bus Routing Problem applied to the rural area of a Brazilian city. We use a complete set of real georeferenced data containing 716 students, 23 schools, and the road network. Our goal is to minimize the total traveled distance of a heterogeneous fleet. We compare our strategy to a single load(More)