Exploring Trip Chaining Behavior in Activity-Transport Systems: Trip Chain Classification, Peak-period Travel Implications, and Ride-hailing's Role

A trip chain is a series of consecutive trips to multiple destinations. By influencing activity and travel decisions, trip chaining can directly impact roadway congestion, vehicle miles traveled by mode, transit ridership, energy consumption, and emissions of harmful pollutants. In this context, my dissertation uses the 2017 National Household Travel Survey (NHTS) and 2018-2019 Household Travel Survey from Four Metropolitan Planning Organizations (MPOs) to (i) identify distinct trip chain types, (ii) quantify the effect of trip chaining propensity on peak and off-peak person-miles traveled (PMT), and (iii) explore how trip chain makers use emerging transportation modes (i.e., ride-hail). To perform these three analyses, I employ several statistical modeling techniques, including Latent Class Analysis (LCA), multi-level Poisson regression, structural equation modeling, and logistic regression. The main findings suggest the existence of distinct trip chain types and a significant association between ride-hailing and trip chaining attributes. I also find that chaining subsistence, maintenance and discretionary activities increases peak PMT for both workers and non-workers, with possible substitution effects on the off-peak PMT.