The Tennessee Department of Transportation replaced the quick-response-based long-distance component in its statewide model by integrating the new national long-distance passenger travel demand model in a new statewide model and calibrating it to long-distance trips observed in cell phone origin–destination data. The national long-distance model is a tour-based simulation model developed from FHWA research on long-distance travel behavior and patterns. The tool allows the evaluation of many policy scenarios, including fare or service changes for various modes, such as commercial air, intercity bus, Amtrak rail, and highway travel. The availability of this tool presents an opportunity for state departments of transportation in developing statewide models. Commercial big data from cell phones for long-distance trips also pre-sents an opportunity and a new data source for long-distance travel patterns, which previously have been the subject of limited data collection, in the form of surveys. This project is the first to seize on both of these opportunities by integrating the national long-distance model with the new Tennessee statewide model and by processing big data for use as a calibration target for long-distance travel in a statewide model. The paper demonstrates the feasibility of integrating the national model with statewide models, the ability of the national model to be calibrated to new data sources, the ability to combine multiple big data sources, and the value of big data on long-distance travel, as well as important lessons on its expansion.

This paper presents 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. 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 to allow 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 when human inference would be difficult, if not impossible, because of the complexity of the system and when manual random trial and error would be time- and cost-prohibitive. The approach used a genetic algorithm metaheuristic to implement a composite log likelihood and a pseudo composite log likelihood maximization in the development of a new travel model for the South Bend, Indiana, urban area for the Michiana Area Council of Governments. The process made use of a set of parameters transferred from another region and resulted in new parameters that produced significantly better consistency in the model with both local survey data and counts. Although computationally intense, this exciting new approach showed promise, at least for midsized urban areas.