This presentation shares the successful application of a new method to improve travel demand forecasting models by taking advantage of cheap and readily available traffic count data and using them together with household travel survey data to inform the model’s parameter estimates and improve model validation.
Although traffic counts are frequently used in an ad hoc manner in the validation of travel model components, this paper presents a more rigorous, structured and statistically efficient method for allowing the information contained in traffic counts to influence the selection of model parameters simultaneously with household survey data. This formal process allows traffic counts to inform indirectly but importantly related parameters such as destination choice utility functions through formal statistical inference where human inference would be difficult if not impossible due to the complexity of the system and manual random trial and error would be time and cost prohibitive.
This presentation will review the data, methodology and basic results and insights from the application of this method to the estimation of demand model parameters for a new hybrid tour-based model for the Michiana Area Council of Governments (MACOG).
A genetic algorithm metaheuristic was used to implement composite log-likelihood and pseudo-composite log-likelihood maximization for parameter estimation. The process made use of and began from a set of parameters transferred from another region and resulted in new parameters which result in significantly better consistency of the model with both local survey data and counts. While computationally intense, this paper demonstrates the feasibility and promise of this exciting new approach, at least for mid-sized urban areas.