Summary and Discussions Sample Clauses

Summary and Discussions. Both qualitative (professional judgment and similar routes, etc.) and quantitative (elasticity analysis and econometric modeling, etc.) methods were proven useful in transit ridership forecasting, and many agencies employed multiple methods in their analysis. Among the quantitative methods, regional travel demand models remain a powerful tool to estimate transit share based on system characteristics, built environment, and the demographics and other contributing factors. These tools are usually readily available and provide a systematic and holistic view of travel choices. With recent advancements in activity-based modeling and better representation of land use factors at higher resolutions, these models may equip the agencies with better capabilities for transit analysis. However, since these regional models generally are not geared toward transit planning and service analysis, they may not be able to reflect the impacts of changes in the transit network or services on travel behavior to the full extent. Complexity of the regional model, cumbersome procedures, long run times, and lack of flexibility are the other common obstacles a transit agency may face. Consequently, local transit agencies were more likely to develop models at finer scales, such as route-level, stop-level, or segment-level ridership models. These tools would provide more user-friendly features that allow the transit agencies to explore and analyze various strategies and scenarios in transit service planning and operations. In this regard, regression models were the most widespread methodology for ridership estimation. This approach would also allow the analyst to take into account additional factors within the corridor or at the route or stop level that may have significant impacts on the usage of transit. On the other hand, it may also require the collection of additional data. Enhanced modeling techniques have also been proposed which tended to enhance the existing models either through the consideration of additional dimensions (geographically weighted regression models and time-series analysis, etc.) or better handling of the demand and supply (dynamic demand formulations and neural network, etc.). However, applying these methods in practice has not been well established at least in the United States, perhaps due to the complexity of the methods, or the data required for model calibration. There are several existing tools (such as, T-BEST and STOPS) that present great potential for...
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