Optimal Matching Methods for Historical Sequences

Abbott, Andrew; Forrest, John K.

doi:10.2307/204500

Cited by 328 publications

(229 citation statements)

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“…Cherchi & Cirillo, 2010;Roorda et al, 2008). The sequence alignment method (SAM), which compares two sequences based on the composition and temporal ordering of the daily activities (Abbott & Forrest, 1986;Wilson, 1998), is also employed to assess the similarities between each of the observed sequences and its predicted counterpart (e.g. Sammour et al, 2012).…”

Section: Problem Statementmentioning

confidence: 99%

Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

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Activity-based micro-simulation transportation models typically predict 24-hour activitytravel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure. Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models. By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.

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Section: Problem Statementmentioning

confidence: 99%

Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…The discussion, however, is by no means exhaustive. Among the techniques we did not discuss, optimal matching (Abbot and Forrest, 1986;Malo and Munoz, 2003) deserves special attention. Optimal matching is, like Markovian models, a state sequence analysis tool.…”

Section: Resultsmentioning

confidence: 99%

Life Course Data In Demography And Social Sciences: Statistical And Data-Mining Approaches

Ritschard

2005

Advances in Life Course Research

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This paper has essentially a methodological purpose. In a first section, we shortly explain why demographers have been relatively reluctant to implement the life course paradigm and methods, while the quantitative focus and the concepts of demographic analysis a priori favored such implementation. A real intellectual crisis has been needed before demographers integrated the necessity to face up the challenge of shifting "from structure to process, from macro to micro, from analysis to synthesis, from certainty to uncertainty" (Willekens, 1999, p. 26). This retrospective look also shows impressive progresses to promote a real interdisciplinarity in population studies, knotting the ties between demography and the social sciences. However, we also note that the success of multivariate causal analyses has been so rapid that some pitfalls are not always avoided. In Section 2, we focus on statistical methods for studying transitions. First, readers mind is refreshed about regression like models, and then we consider the issue of population heterogeneity. We show how it could affect results interpretation, and illustrate the interest of robust estimates and of the notion of shared frailty to deal with it. We also present Markovian modeling. Though less popular than regression event history models, Markovian models are specifically well suited for studying successive transitions between states observed at periodic time. In Sections 3, we promote some tools from the developing field of data mining, with special attention on the mining of frequent sequences and induction trees. These highly flexible heuristic tools can, among others, handle trajectories. Hence, they may prove very useful to face the deficit of knowledge on trajectories we observe between standard demographic analysis and causal research.

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“…Starting from the individual sequences we constructed a typology of work-family trajectories by applying optimal matching (OM) (Abbott and Forest 1986;Abbott and Hrycak 1990;Abbott and Tsay 2000;Abbott 1984;Lesnard 2006; and more specifically for the analysis of work-family trajectories Aassve, Billari, and Piccarreta 2007). This method determines the distance between each pair of sequences for the subjects in the sample which expresses how difficult it is for one sequence to be transformed into another 6 .…”

Section: Methodsmentioning

confidence: 99%