Joao F. M. Sarubbi

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In this work we tackle the bus stop selection step for the School Bus Routing Problem (SBRP). Our goal is to minimize the number of bus stops in order to assign all students to a bus stop respecting a home-to-bus-stop walking distance constraint. Our strategy creates a large number of possible bus stops points in a road network and uses a pseudo-random(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)
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)
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)
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)
This work presents a novel algorithm for the deployment of roadside units based on partial mobility information. Instead of relying on the individual vehicles trajectories, our proposal relies on the migration ratios between urban regions in order to infer the better locations for the deployment of the roadside units. Our goal is to identify those α(More)