| Article ID: | iaor20116963 |
| Volume: | 38 |
| Issue: | 4 |
| Start Page Number: | 605 |
| End Page Number: | 623 |
| Publication Date: | Jul 2011 |
| Journal: | Transportation |
| Authors: | Outwater L, Spitz Greg, Lobb John, Campbell Margaret, Sana Bhargava, Pendyala Ram, Woodford William |
| Keywords: | time series & forecasting methods |
This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non‐traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non‐traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.