Activity pattern similarity: a multidimensional sequence alignment method

Joh, Chang‐Hyeon; Arentze, TA Theo; Hofman, F Frank; Timmermans, Hjp Harry

doi:10.1016/s0191-2615(01)00009-1

Cited by 138 publications

(100 citation statements)

References 20 publications

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“…Thus, only a handful of recent studies have considered activity time-use behavior and activity timing behavior in a joint framework (see Bhat, 1998, Yamamoto, et al, 2000, Chu, 2005, Joh et al, 2002, Ashiru et al, 2004, and Ettema et al, 2007. Most of these studies, however, consider activity participation during only certain specific portions of the day (Bhat, 1998;and Yamamoto et al, 2000), or consider only a single activity purpose or a very restricted set of activity purposes (Ettema et al, 2007, Ashiru et al, 2004and Chu, 2005.…”

Section: Activity Time-use and Activity Timing Analysismentioning

confidence: 99%

A multiple discrete–continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays

Pinjari

Bhat

2010

Transportation Research Part B: Methodological

107

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This paper develops a multiple discrete-continuous nested extreme value (MDCNEV) model that relaxes the independently distributed (or uncorrelated) error terms assumption of the multiple discrete-continuous extreme value (MDCEV) model proposed by Bhat (2005 and. The MDCNEV model captures inter-alternative correlations among alternatives in mutually exclusive subsets (or nests) of the choice set, while maintaining the closed-form of probability expressions for any (and all) consumption pattern(s).The MDCNEV model is applied to analyze non-worker out of home discretionary activity time-use and activity timing decisions on weekdays using data from the 2000 San Francisco Bay Area data. This empirical application contributes to the literature on activity timeuse and activity timing analysis by considering daily activity time-use behavior and activity timing preferences in a unified utility maximization-based framework. The model estimation results provide several insights into the determinants of non-workers' activity time-use and timing decisions. The MDCNEV model performs better than the MDCEV model in terms of goodness of fit. However, the nesting parameters are very close to 1, indicating low levels of correlation. Nonetheless, even with such low correlation levels, empirical policy simulations indicate non-negligible differences in policy predictions and substitution patterns exhibited by the two models. Experiments conducted using simulated data also corroborate this result.

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Section: Activity Time-use and Activity Timing Analysismentioning

confidence: 99%

A multiple discrete–continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays

Pinjari

Bhat

2010

Transportation Research Part B: Methodological

107

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“…The issues include the meaning of indels and substitutions in the context of human behavior analysis; the arbitrary assignment of transformation costs for substitutions and indels; required symmetry of the pairwise distance matrix; the lack of proper support of multidimensional analysis; and time distortion by indels. Fortunately, a "second wave" of sequence alignment toward methodological improvements is currently observed in sociology (Aisenbrey and Fasang, 2010) as well as transportation (Joh et al, 2002;Wilson, 2008).…”

Section: Sequence Alignment Methods For Activity-pattern Analysismentioning

confidence: 99%

Continuous Data Integration for Land Use and Transportation Planning and Modeling

Wang

Kim²

2014

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There is an urgent need for improved models that address the interdependencies between land use and transportation, and considerable new work is underway to develop such models in Oregon and elsewhere. These models and planning practices to integrate land use into the process, however, require the integration of massive amounts of land use data that is messy and incomplete. There have been considerable advances in the treatment of such data problems in other domains, drawing on data mining and machine-learning techniques to address issues in various domains. To date, however, little systematic effort has applied these technological advances to the problem domain of land use and transportation data. Experience suggests that as much as 70% of the total effort in developing integrated land use and transportation models is directly or indirectly associated with data development, integration and cleaning, yet there is remarkably little systematic research focused on the development of reusable methods and tools to support this problem domain.In coordination with an ongoing project of similar theme funded by University of California Transportation Center (UCTC), this project will focus on bringing interdisciplinary methods to develop land use datasets for integrated land use and transportation planning and modeling, with special attention on preserving temporal dimension of the data and monitoring data quality through indicators. Utilizing statistics and machinelearning techniques, we will develop reusable tools that will create a harmonized and coherent land use database from various public and private sources. These tools will be made available to the public and can be used by cities, counties, metropolitan planning agencies, state agencies, universities or anyone else needing to develop a usable database for use in integrated planning and modeling. All of the resulting data, with the exception of proprietary or confidential input data where there is no viable alternative, would be public and reusable for planning and research.17.

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“…Joh et al, 2002;Kisilevich et al, 2010;Andrienko et al, 2011;Vrotsou et al, 2011). However, these approaches usually rely on a movingwindow in time to construct trajectories.…”

Section: Trajectory Networkmentioning

confidence: 99%

Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

Straumann

Çöltekin

Andrienko

2014

The Cartographic Journal

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We present a study that explores methodological steps towards (re)constructing collective narratives from the phototaking behaviour of two groups (foreign tourists and inhabitants of Switzerland) by analysing spatial and temporal patterns in user-contributed, georeferenced photographs of Zurich, Switzerland. We reason that the photographers typically capture a scene or a moment because they want to remember or share it, thus these scenes or moments are meaningful to them. Various scholars suggest that the human experience (i.e. this meaningfulness) is what separates a place from the mathematical descriptions of space. While this notion is well known in larger geographic literature, it is under-explored in cartographic research. We respond to this research gap and reconstruct static and dynamic patterns of photo-taking and -sharing behaviour to assist in capturing the implicit meaning in the studied locations. These locations may be meaningful to only a certain group of people in certain moments; therefore, studying group differences in spatial and temporal photo-taking patterns will help building a collective and comparative story about the studied place. In our study, we focus on experiences of foreign versus domestic visitors, and in the process, we examine the potential (and feasibility) of georeferenced photographs for extracting such collective narratives using qualitative and quantitative visual analytical methods.

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Activity pattern similarity: a multidimensional sequence alignment method

Cited by 138 publications

References 20 publications

A multiple discrete–continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays

A multiple discrete–continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays

Continuous Data Integration for Land Use and Transportation Planning and Modeling

Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

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