UC Irvine

An activity-travel chain is a series of consecutive trips to multiple destinations. By influencing activity decisions (e.g., activity location, duration, and start time) and travel decisions (e.g., trip mode, route, and departure time), activity-travel 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 activity-travel chain types, (ii) quantify the effect of activity-travel chaining propensity on peak and off-peak person-miles traveled (PMT), and (iii) explore how activity-travel 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.

In Chapter 3, I identify four distinct types of activity-travel chains. The most representative type involves simple car-based activity-travel chains with short-duration stops, typically for maintenance activities. The classification also reveals one group that exclusively represents non-motorized transport (NMT)- and transit-based activity-travel chains. In addition to identifying distinct activity-travel chains, I also model the propensity of travelers to conduct each type of activity-travel chain. I find that travelers in households with children and older travelers more frequently make car-based activity-travel chains for maintenance activities. Moreover, travelers in single-member households, and travelers who are younger and male more frequently make NMT- and transit-based activity-travel chains for maintenance activities. I expect the identification of these distinct activity-travel chain types, and the models of propensity of travelers to perform each activity-travel chain type, to be useful in agent- and activity-based travel forecasting modeling frameworks.

In Chapter 4, I investigate the structural relationship between activity-travel chaining propensity and motorized person-miles traveled (PMT) during the peak and off-peak periods of the day. Moreover, I differentiate between workers and non-workers. Using structural equation modeling techniques, and mediating factors I find that chaining of maintenance and discretionary activities increases peak motorized PMT for workers and non-workers, providing the strongest evidence in the literature that activity-travel chaining can exacerbate traffic congestion during peak travel periods. Moreover, I find possible substitution of maintenance activities (e.g., shopping, dining, etc.) in peak-hour with same/similar chained activities in off-peak hour.

Finally, in Chapter 5, I analyze activity-travel chain mode choice and show that young persons, frequent transit users, and those having long-duration stops prefer ride-hailing over car. Also, activity-travel chain makers headed to healthcare and social/recreational activities have a particularly high tendency to use ride-hail. Understanding the use of ride-hailing in activity-travel chains should help in formulating policies to better align ride-hailing services with compatible activity-travel patterns and consequently improve accessibility and mobility.