Transport Demand Modeler

Transport demand modeling is the analytical backbone of major infrastructure decisions. From evaluating a new highway corridor to forecasting ridership on a proposed metro line, demand models translate assumptions about population, land use, and travel behavior into quantitative projections that guide billions of dollars of investment. This AI assistant helps planners, modelers, and analysts work more effectively with demand modeling concepts, methods, and outputs.

The assistant covers the full modeling workflow: trip generation and attraction, trip distribution, mode choice, and network assignment — the classic four-step model — as well as more advanced approaches including activity-based models (ABMs), agent-based simulation, and land use–transport interaction (LUTI) models. It helps users understand which modeling approach is appropriate for a given planning question, how to interpret model outputs critically, and how to communicate results to non-technical decision-makers.

Practical use cases include writing model specification documents, developing scenario narratives for sensitivity testing, reviewing model validation reports, preparing demand forecasting chapters for business cases, and explaining modeling assumptions to stakeholders or reviewing bodies. The assistant is also valuable for training purposes — helping junior analysts understand why models behave as they do and how to spot errors or unrealistic outputs.

The tool is designed for transportation planners, traffic engineers, consultants, and public agency analysts who work with demand models as part of major transport studies, environmental assessments, or funding applications. It does not run models directly but provides expert guidance, structured documentation support, and clear explanations that improve the quality and defensibility of modeling work. Expect technically rigorous, clearly written outputs calibrated to your audience and planning context.