Pathways to Improving Traditional Travel Behavior Models with Travel-based Multitasking and Attitudinal Data - Aliaksandr Malokin

Sustainable Education Building, Room 122
Thursday, January 10, 2019 - 09:00



Historically, regional transportation forecasting models used mostly socio-economic characteristics and relevant travel-related attributes to account for travel patterns. With the increased complexity and lability of transportation systems, these factors could be insufficient for reliable policy and decision-making as the heterogeneity of travel preferences and experiences grows. Hence, the need for incorporating attitudinal data (an aggregate term for lifestyles, preferences, intentions, propensities, etc.), which underlies many travel-related decisions, into regional travel behavior models is especially strong now, and growing.

Accordingly, the main goal of the present dissertation is to contribute to the improvement of regional travel behavior models by investigating the influence of understudied behavioral drivers and increasing the availability of attitudinal data. This goal can be decomposed into two distinctive parts, among other ways unified through the use of a single attitudinally-rich dataset: (1) studying the effects of travel-based multitasking on mode choice and the value of travel time (VOTT), and (2) developing an approach for porting attitudinal data from a small regional dataset to a large national sample.

For the first part of the objective, the empirical analysis is based on a survey of Northern California commuters (N > 2,000) that measures travel multitasking attitudes and behaviors, together with other attitudes, mode perceptions, and standard socioeconomic traits. A revealed preference mode choice model, which accounts for the impact of multitasking attitudes and behavior on the utility of various alternatives, is estimated. Results show that the propensity to engage in productive activities on the commute, operationalized as using a laptop/tablet, significantly influences utility and accounts for a small but non-trivial portion of the current mode shares.

For the second part of the objective, we transfer transportation-related attitudes from the same Northern California dataset to the 2009 National Household Travel Survey by augmenting both datasets with a large number of built-environment attributes and by applying machine-learning methods. The results show that in the source dataset the observed attitudes account for an 8.0% model lift (improvement in goodness of fit), while in the target dataset the predicted attitudes account for a 1.2 -5.4% model lift.

This study presents a valuable combination of novel empirical application, and data augmentation methodology that could be transferred to a variety of contexts. To our knowledge, it is the first study based on a revealed preference model to quantify the contribution of travel multitasking to mode choice. The proposed transfer learning framework targets this data unavailability and offers a way to synthesize promising variables into a practice-ready context.


Dr. Patricia Mokhtarian


Dr. Giovanni Circella
Dr. Laurie Garrow
Dr. Patrick McCarthy (ECON)
Dr. Kari Watkins