The book "Short-term Railway Passenger Demand Forecasting" by Tsung-Hsien Tsai offers a comprehensive analysis and innovative approaches to forecasting passenger demand in rail transport. Predicting passenger arrivals is crucial for daily operations and revenue management. By utilizing Artificial Neural Networks (ANN), it demonstrates how these technologies can be effectively applied to solve time series forecasting problems. The book introduces novel ANN models based on the "divide-and-conquer" concept and provides insights into the development of structures such as the Multiple Temporal Units Neural Network, the Parallel Ensemble Neural Network, and the Input Recurrent Neural Network. Additionally, various relevant questions are tested to showcase the forecasting capabilities of the individual models and their combinations. This specialized book is aimed at researchers and practitioners in the fields of time series forecasting, ANN, revenue management, and rail transport.