| dc.description.abstract | The Bicycle-Sharing System (BSS) is a key solution to the first and last mile problem in public transportation. Governments worldwide actively promote its development by continuously adding new stations. However, as the number of stations increases, BSS routes and dispatching become more complex, requiring more resources to optimize dispatch strategies and plan the riding environment. This study proposes a trip distribution prediction model based on travel demand prediction to effectively predict trip distribution after the establishment of new stations and to arrange efficient dispatch strategies and investment areas for the construction of the riding environment.
In predicting the number of borrowings and returns of trips in travel demand forecasting, this study explores the issues related to both existing stations with historical ticket data and new stations lacking this data. Given that time series analysis methods show higher accuracy than variable-based estimates, for existing stations, this study proposes the statistical models: the Autoregressive Integrated Moving Average (ARIMA) model, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and machine learning models: the Long Short-Term Memory (LSTM) model and the Gated Recurrent Unit (GRU) model; and performs predictions and comparisons. Results indicate that LSTM has the best prediction accuracy. For new stations, the study utilizes four main factors that influence BSS borrowings and returns: climate, population, land use, and the usage of surrounding stations, and applies the statistical Multiple Linear Regression (MLR) model and the machine learning Extreme Gradient Boosting (XGBoost) model for predictions and comparisons, with XGBoost showing the best prediction accuracy. Therefore, this study adopts LSTM and XGBoost for predicting the trip generation of existing and new stations, respectively.For trip distribution, the study employs a Deep Gravity (DG) model, combining traditional gravity models with deep learning, using the travel demand prediction process as the main concept. The output results of trip generation, combined with distance, serve as inputs for trip distribution prediction. The prediction result has an R² value of 0.7884, accurately capturing the BSS trip distribution patterns.
Finally, through the trip distribution results and the characteristics of new stations, the study finds that if a station primarily serves recreational and tourism purposes, it will predominantly interact with other stations with similar characteristics. Residential areas mainly travel to recreational spots, transportation nodes, and educational institutions. | en_US |