One of the most important issues affecting the daily lives of residents of Miami-Dade County in South Florida is the ever-changing transportation system. With an approximate population of 2.56 million people and an estimated 13.9 million tourists each year, driving in the city can often feel like an unwanted challenge.
Recognizing that individuals are faced with various transportation options and routes, FIU CEE Assistant Professor of Transportation Engineering Xia Jin, AICP, is focusing her research on travel behavior analysis that advances the understanding of individual’s travel choices in interacting with the urban system (transportation infrastructure, land use, housing, and social and environmental sectors). Through advanced travel demand modeling techniques, her research facilitates the decision-making for policy and transportation investment decisions that would lead to a better transportation system to serve communities’ transportation, economic, environmental and social objectives.
In a recently funded project, Jin will examine the user heterogeneity in valuing travel time and travel time reliability within the context of managed lanes. This research will also recommend approaches to incorporate pricing and user heterogeneity into the demand modeling process, which will largely enhance the sensitivity of the model for advanced pricing and policy analysis.
In addition to advancing the knowledge and modeling techniques in the core areas in travel demand forecasting, Jin’s most recent research projects redraws attention to the travel markets that have been largely overlooked. In helping the Florida Department of Transportation (DOT) to plan for the next generation of its Standard Urban Transportation Model Structure (FSUTMS), Jin is investigating innovative approaches and additional data sources to better address the “ancillary” travel markets, namely tourist travel, urban goods movement, external trips, and special generators. One of Jin’s projects is examining the travel patterns across the week days, including mid-week days (Tuesday through Thursday), shoulder days (Monday and Friday), and weekends. Going beyond the commonly adopted assumption in demand modeling that emphasizes the daily repetitiveness in an activity-travel pattern, this research recognizes the weekly rhythm in activity arrangement of a household. The results of this research will help capture the activities and traveling needs that have been neglected by current practices, and provide insights on structuring a demand modeling system that addresses these travel needs.