2. Forecasting Methods

This chapter mainly introduces various forecasting models and explains them in relation to related research and applications. The first method discussed is the mainstream four-step travel demand model, which conceptualizes travel decision-making. The second method is the direct demand model, where cross-sectional regression analysis estimates travel demand. This is followed by a discussion of post-processing models that account for impacts not explicitly considered in the main models. Next, the abstract mode demand model is introduced, which assumes an abstract representation based on features that affect mode choice, such as cost, travel time, and frequency. The incremental demand model is subsequently utilized to examine the effects of variations in travel time, cost, and other transportation attributes. This analysis typically involves the use of elasticity analysis and pivot-point modeling. The final model discussed is the sketch planning model, used to analyze traffic and land use scenarios at a broad resolution level, providing general magnitude estimates. Additionally, the chapter covers the corridor model and the marginal demand model. The corridor model is characterized by simplicity, considering only the entry and exit points as the origin and destination. The marginal demand model focuses on travel demand, most likely influenced by specific projects and policies.