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Predicting Bike Stations Occupation using Time-Series Models: The Case Study of Lisbon

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Resumo:Urban bike-sharing systems have become a vital component of sustainable mobility strategies in modern cities, helping reduce traffic congestion and environmental impact while improving transportation accessibility. In Lisbon, the GIRA bike-sharing network plays a significant role in meeting short-distance travel needs, yet ensuring consistent bike availability across its many stations remains a complex challenge due to spatio-temporal demand fluctuations influenced by external factors like weather and air quality. Although various forecasting methods have been proposed to tackle this issue, many rely on limited data types or struggle with inconsistent data quality, particularly due to missing values in environmental datasets. To better understand and address this problem, we aimed to train various robust forecasting models capable of predicting hourly bike availability at GIRA stations while assessing the impact of different imputation strategies on prediction performance. We employed deep learning architectures, including Long Short-Term Memory (LSTM) and Transformer models, and trained them using historical bike usage data enriched with meteorological and air quality variables. This thesis presents a comprehensive performance evaluation of these models under multiple imputation approaches, analyzing errors across time periods, stations, and input conditions. Our findings reveal that the Transformer architecture consistently achieved the best performance. Grouped statistical imputation produced more reliable forecasts on non-imputed data, although biases emerged in predicting imputed test rows. Furthermore, temporal patterns in error distributions highlight specific hours of the day with heightened predictive difficulty, typically during morning and evening peak times. In conclusion, deep learning models—especially Transformers—offer promising accuracy in forecasting bike availability in a real-world context, provided that imputation strategies are carefully considered. These results support the integration of predictive analytics into bike-sharing operations to enhance decision-making for redistribution, planning, and service quality optimization.
Autores principais:Moreira, Guilherme Valler
Assunto:Bike-sharing Time series forecasting Transformer Data imputation Urban mobility Demand prediction SDG 11 - Sustainable cities and communities SDG 13 - Climate action
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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