Metodología para la asignación de vehículos de una flota a rutas preestablecidas

de Andrés, Alfonso Román · 2014 · OpenAlex-citations

DOI: 10.20868/upm.thesis.33641

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Summary

This doctoral thesis addresses the optimization of vehicle fleet allocation to fixed urban routes to minimize fuel consumption and pollutant emissions. The research is motivated by the observation that fleet distribution is often not based on objective criteria, ignoring the kinematic variability between different routes. By matching specific vehicle technologies to the characteristics of each itinerary, the study aims to reduce the environmental impact of public transport fleets without requiring new vehicle acquisitions or significant infrastructure investments. The proposed methodology consists of a multi-step procedure. First, kinematic characteristics of vehicles are recorded on a representative sample of routes. These routes are then grouped into clusters of similar itineraries using a hierarchical algorithm that optimizes a similarity index derived from hypothesis testing of representative variables. A specific kinematic cycle is constructed for each cluster to simulate operational conditions. To classify the remaining routes without direct measurement, macroscopic variables are identified and used to train a neural network. Finally, an optimization algorithm reallocates the existing fleet to these routes, minimizing an objective function based on emissions while ensuring that other pollutants do not exceed current baseline levels. The methodology was applied to 160 bus lines of the Madrid Municipal Transport Company (EMT), with detailed kinematic data collected from a sample of 25 routes. The optimization process evaluated two primary scenarios: minimizing carbon dioxide (CO2) emissions to address greenhouse gas impacts, and minimizing nitrous oxide (NOx) emissions to mitigate acid rain and tropospheric ozone formation. In both scenarios, additional constraints were imposed to prevent increases in other pollutants beyond the levels observed in the operator’s current fleet organization. The results demonstrate that it is feasible to significantly reduce the majority of pollutant substances through fleet redistribution. The study found that optimizing for either CO2 or NOx reduction allowed for a decrease in overall emissions without causing a compensatory increase in other contaminants. This indicates that matching vehicle technology to route-specific kinematic profiles is an effective strategy for improving the environmental performance of urban bus fleets. The approach offers a low-cost, short-term solution for cities struggling to meet air quality directives, providing a data-driven alternative to subjective fleet management practices.

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