Generation Gaps in Activity and Travel Behavior
thesisposted on 22.07.2017 by Behzad Karimi Varzardoliya
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
The United States is experiencing a rapid increase in seniors (i.e. 65 years old or higher) population. According to Census Bureau estimates, seniors’ population is expected to increase by 104.2% from 2000 to 2030, which translates into 72.1 million people age 65 and older in 2030. The main reason for this considerable increase in seniors’ population is the entrance of baby boomers into elderly age since the beginning of 2011. Baby Boomers are the generation who were born between 1946 and 1965 and represent the peak of births rate in the U.S. since 1930. This rapid and sudden increase in seniors’ population has become a serious concern in the United States because of the potential social and economic effects that an increasing elderly population can have on socioeconomic systems. Elderly people have their own specific needs that must be addressed in the coming years. Review of literature showed that the seniors’ activity-travel modeling lacks appropriate tools, to deal with the complex nature of activity-travel behavior. Current studies, mostly, employed conventional analytical tools to study differences between seniors’ and non-seniors’ travel behavior. As a result, the role of actual effective factors in observed travel behavior is mostly overlooked in the current studies. This study outlines innovative econometric tool box that will address some of the technical and conceptual hurdles that have challenged past travel behavior modeling efforts. The tool box developed in this dissertation includes: 1) Mixed copula-based discrete-continuous joint model; 2) Random Regret Minimization versus Random utility Maximization for travel mode choice; 3) Nested logit model for modeling stop-go behavior of drivers in dilemma zone of a signalized intersection; and 4) Latent segmentation AFT-based model for shopping activity participation. All these models demonstrate the use of advanced behavioral-based modeling approaches for forecasting travel behavior of seniors at disaggregate individual level.