Modelling multiple occurrences of activities during a day: an extension of the MDCEV model

Abstract

The increased interest in time use among transport researchers has led to a search for flexible but tractable models of time use, such as Bhat's Multiple Discrete Continuous Extreme Value (MDCEV) model. MDCEV formulations typically model aggregate time allocation into different activity types during a given period, such as the amount of time spent working and shopping in a day. While these applications provide valuable insights into activity participation, they ignore disaggregate activity-episodes, that is the fact that people might split their total time spent working in multiple separate blocks, with breaks or other activities in between. Insights into this splitting into episodes are necessary for predicting trips and understanding time use satiation. We propose a modified MDCEV model where an activity-episode, rather than an activity type, is the basic choice alternative, using a modified utility function to capture the reduced likelihood of individuals performing a very large number of episodes of the same activity. Results from two large revealed preference datasets exhibit equivalent forecast accuracy between the traditional and proposed approach at an aggregate level, but the latter also provides insights on the number and duration of activity-episodes with significant accuracy.

Keywords: MDCEV; Time use; activity modelling; discrete-continuous; episodes.

Figure 1.

Example of alternative coding for traditional and episode approach.

Figure 2.

Average duration of episodes when engaged in the Leeds (left) and PSR (right) datasets, in hours. Episodes are ordered in a clockwise fashion.

Figure 3.

Frequency of episode engagement (%) and size of base utility penalty ( $\sum _{p=1}^{p=3}{\pi }_{\psi kp}(i-1{)}^p$ where k, i index activity and episode, respectively) in the Leeds dataset.

Figure 4.

Frequency of episode engagement (%) and size of base utility penalty ( $\sum _{p=1}^{p=3}{\pi }_{\psi kp}(i-1{)}^p$ where k, i index activity and episode, respectively) in the PSR dataset.