Framework metodológico de modelagem e simulação híbrida com aprendizagem por reforço aplicado à racionalização de pontos de ônibus

Abstract

The rationalization of bus stops represents a major challenge in urban public transportation planning, with direct implications for population accessibility, system operational efficiency, and travel quality. Despite advances in urban computing, there remains a lack of methodological frameworks capable of integrating Agent-Based Simulation (ABS), Discrete-Event Simulation (DES), and Reinforcement Learning (RL) in a structured and synergistic manner to support the rationalization of public transportation infrastructure. To address this conceptual and methodological gap, this dissertation aims to develop a Hybrid Modeling and Simulation (HMS) methodological framework to support the rationalization of urban public transportation bus stop locations by integrating ABS, DES, and RL based on measures of accessibility and system usage. The methodological approach comprised a Systematic Literature Review (SLR) conducted according to the PRISMA protocol, the theoretical formulation of the framework, and its computational implementation in the AnyLogic platform, using a georeferenced case study of the municipality of Passos, Minas Gerais, Brazil, for validation. The computational model integrated a spatially distributed synthetic population, origin–destination (OD) matrices, a georeferenced road network obtained from OpenStreetMap, urban Points of Interest (POIs), and the behavioral and operational processes of the public transportation system. Agent behavioral parameters were calibrated and statistically validated against real boarding and electronic ticketing data. During the rationalization stage, a tabular Q-Learning algorithm employing an ε-greedy policy was implemented, in which the reward function combined variations in the global accessibility indicator with variations in the number of public transportation users. In an experiment configured with 10 episodes and 2,000 training steps per episode, the model identified alternative spatial configurations for the bus stop network, producing an improvement of approximately 8% in the global accessibility indicator and an increase of more than 10% in the number of public transportation users compared with the calibrated and validated baseline scenario. These improvements were achieved exclusively by modifying bus stop locations while keeping routes, itineraries, and all other operational characteristics of the system unchanged. The resulting locations were interpreted as candidate solutions and subsequently subjected to physical and topological feasibility assessments. The results demonstrate the methodological feasibility and practical applicability of the proposed framework as an evidence-based decision-support tool for urban transportation planning. Scientifically, this dissertation contributes by proposing a systematic, replicable, and adaptable methodological framework that expands the possibilities for integrating hybrid simulation, reinforcement learning, and spatial analysis to support the rationalization of urban public transportation.