5.0 CONCLUSIONS

This report presents the research undertaken to develop a long-distance multimodal passenger travel modal choice model. This research started with a literature and practice review that served as a precursor to the development of quantitative mathematical methods to analyze how long-distance passenger travelers make their modal choices. Findings from this review helped identify mathematical techniques and models that have been used on mode choice modeling over the last several years. In addition the review assisted with identifying data sources used for long-distance modeling and factors that were found to influence long-distance passenger travel mode choice.

The report presents a detailed discussion of the mathematical models and inputs to the models used to estimate mode choice for long-distance passenger travel. The report examines the effects that the traveler (in terms of their socioeconomic, demographic, and behavioral attributes), the trip (in terms of distance, purpose, length, and traveling party size), the availability of transportation infrastructure, and land-use characteristics has on the selection of travel mode for long-distance travel as measured by a generalized multinomial logit model. Major findings from this research are as follows:

• Summary statistic and model results provide evidence that mode choice varies by trip purpose and that separate models are warranted;
• There were a much greater number of factors found to significantly influence mode choice observed across trip purpose types for personal vehicle and air travel outcomes than bus and train outcomes. This is due, in part, to the low frequency of bus and train trips in the NHTS;
• Characteristics of the survey respondents who were taking the trips tended to be more significant predictors of travel mode choice than the characteristics of the trips themselves. This indicates that people’s travel mode choices may be driven largely by fixed attributes that revolve around residence and demographics rather than consideration of the dynamic costs and benefits of different modes of travel;
• The results suggest that respondents’ demand for different modes of travel may be relatively decoupled from cost considerations such as the price of airfares or gasoline and that the preference set may be fairly inelastic in the short run – that is, not responsive to changes in price;
• Available transportation infrastructure only appeared to be influential for business travel;
• Respondent’s demographic and behavioral variables were the most consistently significant predictors of travel choice for business and pleasure travel;
• One of the most consistently significant variables in predicting mode choice was route distance of a trip from origin to destination. The probability of choosing to travel in a personal vehicle decreases exponentially with travel distance while the probability of choosing air travel increases exponentially with travel distance; and
• The multinomial logit models developed to predict long-distance passenger travel mode choice predict personal vehicle and air travel well. This is encouraging given that 97 percent of all long distance travel was conducted via these two methods according to the 2001 NHTS. However, the models do not predict bus and train passenger travel very well. When bus and train were actually used, the models most frequently predicted personal vehicle. This is largely due to the fact that bus and train travel comprise such a small proportion of overall observations even after accounting for survey weights.

More data will be needed to effectively predict bus and train long-distance passenger travel. Traditional national long-distance travel surveys have not been able to capture this data. Thus, modifications to the sample design and data collection techniques for future national household long-distance transportation surveys would be warranted to address these concerns. Concepts such as transitioning from a household-based frame to frame that focuses on locations where long-distance travelers are located (e.g., airports, train and bus stations), using social media to connect to long-distance travelers, and leveraging technology such as GPS to help identify when long-distance trips occur are possible ways to increase the amount of long-distance data and to better target bus and train travel modes. As part of this data collecting effort, the research team would recommend a targeted study to identify measureable factors that could differentiate inclination for long-distance passenger travelers to use bus and train relative to private vehicle.