Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary data

Abstract

An understanding of activity choices and duration is a key requirement for better policy making, in transport and beyond. Previous studies have failed to make the important link with individuals' social context. In this paper, the Multiple DiscreteContinuous Nested Extreme Value (MDCNEV) model is applied to the choice of activity type and duration over the course of two days, using data from the Chilean city of Concepción. In common with other studies, heterogeneity across decision makers is accommodated in the model by analysing the impact of different sociodemographic, mobility and residential location variables on both the activity choice and the time allocation decision. In addition, different social network and social capital measures are found to be significantly correlated with the choice and duration of different activities, and we show how these relationships seem to differ from the effects of socio-demographic variables. Finally, we perform a forecasting exercise using the MDCNEV model, highlighting the differences in substitution patterns from a standard MDCEV model.

We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning

“…A key area of research activity has looked at the role of social networks in shaping travel decisions (Calastri et al, 2017b;Carrasco et al, 2008b;Lin and Wang, 2014;van den Berg et al, 2013). This requires a snapshot of the composition and influence of a respondent's network of social contacts.…”

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

“…In the last decade, the multiple discrete continuous (MDC) structure pioneered by Hanemann ( 1984 ) has evolved into an elegant framework to model activity participation and time allocation decisions subject to a budget constraint (Bhat 2008 ; Bhat, Castro, and Khan 2013 ; Liu, Susilo, and Karlström 2017 ; Wang and Li 2011 ). However, the state-of-the-art MDC models focus on predicting the aggregate duration for an activity type rather than accommodating the time allocation at the episode level (Bhat and Misra 1999 ; Calastri et al 2017 ; Enam et al 2018 ). Hence, the time allocation information obtained from the state-of-the-art MDC models can at best act as a constraint (Bhat et al 2004 ), but will seldom be (immediately) useful for the representation of downstream travel choices such as number of trips, mode, destination and route, which rely on episode-level activity participation and time allocation decisions (Auld and Mohammadian 2009 ).…”

Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai

“…Compared with home range buffers [16], individualized models are more effective for studying the association between space (e.g., green space) and health [12]. Previously, activity space was investigated by using self-report activity log diaries [17] and retrospective self-reported data [18]. However, this approach may lead to a low response rate and recall bias, especially among older adults [19].…”