Predicting traffic flow between bike-sharing system stations: A case study of Chicago - Ali Behroozi; Ali Edrisi (Numerical Methods in Civil Engineering)
Active transportation systems, such as bike-sharing systems, offer several advantages, notably their integration with public transportation networks, pollution reduction, congestion alleviation, and decreased fuel consumption. However, a major challenge for shared bicycle companies is the efficient redistribution of bikes to ensure balanced availability across stations. Predicting station demand at various times is crucial for achieving this balance. To address this, we propose a framework leveraging data from Chicago's shared bicycle system and employing modern machine-learning techniques to forecast station demand throughout the day. In this research, we utilize features such as weather, accessibility level for each station, and historical transactions for the accurate prediction of traffic flow. Specifically, we compare two parallel multilayer perceptron deep learning models, incorporating matrix factorization and gate recurrent unit (GRU) neural networks. Furthermore, this research compares two performance models for predicting traffic flow. This research not only aids in optimizing bicycle distribution but also lays the groundwork for predicting demand in other public transportation systems, such as subways and buses, utilizing smart card technology.