Abstract
This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.
Highlights
Transportation and mobility systems have become large-scale and complex in many cities, with broad social impacts and strong implications for the economy and the environment
We first formulate a problem related to mobility and transport, simulate the mobility in the city using a specialized transport simulator, use an evolutionary algorithm to find optimal solutions to the formulated problem evaluating the quality of solutions using the outcome of the simulation, analyze the solutions produced by the evolutionary algorithm and extract knowledge about the system
We have shown that varying mutation from high to low rates per signal combined with a strong elitist selection leads to faster and better convergence
Summary
Transportation and mobility systems have become large-scale and complex in many cities, with broad social impacts and strong implications for the economy and the environment. Urban population is overgrowing around the globe [1] bringing an increase in transportation and mobility demand that improperly satisfied often causes congestion of the system. This adds substantial costs due to delays, increases gas emissions, the risk of accidents and affects health. Efforts to continuously redesign the transportation system and make it sustainable to guarantee the mobility of larger populations and the accessibility of cities are required These efforts include the increase of infrastructure capacity, land-use planning, improving public transportation, and the incorporation of soft-computing methods to implement Intelligent Transportation Systems (ITS).
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