Search

25th International Conference on Urban Transport and the Environment, Urban Transport 2019; Aveiro; Portugal; 25 June 2019 through 27 June 2019; Code 155807 In recent years, the growth of fossil fuel use and greenhouse gases emissions (GHGs) has been promoted by the population increase and development of the industry sector. Due to the increasing attention towards the effects of climate changes on quality of life, recent researches on pollutant formation processes have been developed in different sectors, especially in transportation. The last emission standards on pollutants impose limits on the dimensions and on the particle number of the particulate matter emissions, because of the highly dangerous effect on human health. To fight high concentrations of particulate matter (PM) emissions, a wide number of studies are addressed towards the definition of the most important parameters in effective production of particulate matter, especially in spark ignition engines. Physical processes such as mixture formation, engine operating parameters and fuel chemical properties strongly affect the soot formation in gasoline engines. The heat transfer process between the piston hot surface and the fuel gasoline during the post-injection phase is a key aspect of soot emissions for an engine. This paper is devoted to analyzing the fundamental parameters that are responsible for pollutant formation in the transport sector and the actual experimental and numerical techniques used to predict the environmental impact of engines.

The collapse of infrastructures is very often a complicating factor for the early emergency actuations after a disaster. A proper plan to better cover the needs of the affected people within the disaster area while maintaining life-saving relief operations is mandatory hence. In this paper, we use a drone for flying over a set of difficult-to-access locations for imaging issues to get information to build a risk assessment as the earliest stage of the emergency operations. While the drone provides the flexibility required to visit subsequently a sort of isolated locations, it needs a commando vehicle in ground for (i) monitoring the deployment of operations and (ii) being a recharging station where the drone gets fresh batteries. This work proposes a decision-making process to plan the mission, which is composed by the ground vehicle stopping points and the sequence of locations visited for each drone route. We propose a Genetic Algorithm (GA) which has proven to be helpful in finding good solutions in short computing times. We provide experimental analysis on the factors effecting the performance of the output solutions, around an illustrative test instance. Results show the applicability of these techniques for providing proper solutions to the studied problem.

This paper deals with the problem of designing and analyzing rotating schedules with an algebraic computational approach. Specifically, we determine a set of Boolean polynomials whose zeros can be uniquely identified with the set of rotating schedules related to a given workload matrix subject to standard constraints. These polynomials constitute zero-dimensional radical ideals, whose reduced Gröbner bases can be computed to count and even enumerate the set of rotating schedules that satisfy the desired set of constraints. Thereby, it enables to analyze the influence of each constraint in the same. Junta de Andalucía P09-TEP-5022