Trade-offs among planned versus performed activity patterns

• Published in: Transportmetrica. (Abingdon, Oxfordshire, UK) Oxfordshire, UK), 2017-07, Vol.5 (3), p.342-363
• Main Authors: Allahviranloo, Mahdieh, Recker, Will, Timmermans, Harry J.P.
• Format: Article
• Language: English
• Subjects: activity trade-off; agenda reliability; Behavior; Categories; Covariance; Demographics; Diaries; Fitness; Genetic algorithms; parallel genetic algorithm; Parameter identification; pattern robustness
• ISSN: 2168-0566; 2168-0582
• DOI: 10.1080/21680566.2016.1241729

This paper focuses on the development of a methodology to identify the latent factors leading to changes in the planned itineraries of travellers that result in their actual activity patterns. Specifically, we propose a way to utilise patterns of activities established by individuals across multiple days to generate possible alternative actions by these individuals when faced with conditions that produce a discrepancy between performed and planned patterns on a particular day. The choice alternatives, which are unobserved, are inferred by rules applied to comprehensive multiday data collected in Belgium, consisting of information regarding planned activity itineraries, performed activity/travel diaries, and demographics of travellers. These data are utilised to analyse and explore the underlying reasons preventing individuals from performing their planned activities on a given day, and to identify the influential parameters that lead individuals to trade their planned patterns with those actually performed. Using multiday data, we generate all possible combinations of categories of activities - mandatory, maintenance, discretionary, and pickup/drop off activities - that can form patterns for individuals. Under the assumption that the performed patterns have the closest utility to the planned patterns, we estimate the latent factors that influence travellers' time use behaviour using a multinomial probit choice structure in which the covariance structure of the choice alternatives is specified in terms of the overlap in activities. We further identify the 'costs' associated with making changes in planned agenda (replacing, inserting, or deleting an activity). These penalty values are estimated using 'Parallel Genetic Algorithm', where the fitness function is the likelihood function estimated under the multinomial choice model structure. The results show that individuals' mobility decisions related to mandatory activities are more robust than those associated with their non-mandatory counterparts.