This paper examines the impact of specific travel demand modeling (TDM) disaggregation techniques in the context of small- to medium-sized communities. While larger metropolitan regions have incorporated behavioral disaggregation into the traditional four-step modeling framework, small- to medium-sized communities, now also facing plaguing congestion, typically rely on less sophisticated TDM frameworks. This paper focuses on evaluating specific TDM improvement strategies for predictive power and flexibility with case studies based on the Tyler, Texas network and zone system. Model results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on TDM outputs. Other model improvements shown to impact TDM outputs include adding a logit mode choice model (particularly in networks with higher shares of non- auto trips) and incorporating a congestion feedback loop (from the assignment step back to the trip distribution step). For resource-constrained communities, this paper’s results illuminate which model improvements offer the best prediction and model flexibility for various settings and scenarios, allowing for more thoughtful (and cost-effective) specification decisions.
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