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Record W1804120270

Social Networks and Trip Behavior in Insular Areas- A Latent Class Model Application

2015· article· en· W1804120270 on OpenAlexaboutno aff
Ioanna Kourounioti, Amalia Polydoropoulou, Athena Tsirimpa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTravel behaviorTRIPS architectureLatent class modelFriendshipClass (philosophy)Car ownershipPsychologyPerceptionSocial psychologyAdvertisingGeographyPublic transportComputer scienceBusinessEngineeringTransport engineering
DOInot available

Abstract

fetched live from OpenAlex

Abstract The emergence of Social Networks (SN) has modified the way individuals interact with each other and the world and has affected the perception of social relationships such as friendship, information sharing and leisure activities (Kamargianni and Polydoropoulou (a), 2014). In recent years an increasing body of researchers has attempted to explore how SN influence individuals’ personalities and psychologies. However, there is a limited literature on how SN usage affects daily travel behavior. In addition, the relationship between ICT and travel patterns has received a substantial amount of attention, but without focusing on leisure or social travel although it is considered as fastest-growing segment of travel (Van de Berg et al., 2011; Mokhatarian et al., 2006). It is highly probable that the effect of ICT on social travel differs from its effect on travel for other purposes, such as work or shopping. The aim of this paper is to investigate and quantify the influence of various social networking (SN) usage styles on individuals’ travel behavior, focusing primarily on social/leisure travel. For this purpose a latent class model is under development that consists of two parts: 1. The class membership model, which links the latent SN usage styles to socio-demographic variables; and 2. the class-specific choice model, which is a Poisson regression and show the number of trips made for social purposes of each SN usage style and socio-economic variables. Latent Class Models (LCM) have been used in many transportation studies in order to quantify transportation behavior (Kamargianni and Polydoropoulou (a), 2014; Ettema, 2010; Walker and Li, 2007; Tawfik and Rakha; 2013; Anowar et al., 2013). According to LCM theory the population can be segmented into a finite number of groups, or classes, that their members share common characteristics and are dissimilar from those in other groups. The methodology is tested with data from a household activity survey conducted in the island of Chios, Greece in 2014, within the context of GreTIA research project (Green Transportation in Island Areas). Chios is the fifth largest Greek island with a relatively high quality of life, as it is the fourth Greek county in terms of savings (€16,570) and has the third highest car ownership per capita in the country (Hellenic Statistical Authority, 2011). The sample includes 400 individuals that completed two daily activity dairies and provided information on their socio-economic characteristics, the level of SN usage, their gadget ownership and their Internet connection. After the identification of the different SN classes, the number of social trips that the members of each class conduct will be identified. The innovation of this research covers several topics. First of all, although the effect of ICT on travel behavior has been widely studied the last decade, there are only few surveys that investigate the effect of SN on travel behavior. Secondly, it is interesting to explore the SN usage of remote insular areas inhabitants, since their activity patterns are quite different from urban regions. Furthermore, the findings of this study are expected to assist transport policy makers when making decisions for rural and/or island areas with similar characteristics. References Anowar, S., S. Yasmin, N. Eluru, and L.F. Miranda-Moreno. Analyzing Car Ownership in Two Quebec Metropolitan Regions: Comparison of Latent Ordered and Unordered Response Models. Presented at 92nd Annual Meeting of the Transportation Research Board, Washington D.C., 2013. Ettema, D. The Impact of Telecommuting on Residential Relocation and Residential Preferences. A Latent Class Modeling Approach. Journal of Transport and Land Use, Vol. 3(1), 2010, pp. 7-24. Kamargiannai, M. and A., Polydoropoulou, (2014) (a). Generation’s Y Travel Behavior and Perceptions Towards Walkability Constraints among Three Distinct Geographical Areas. Paper presented at the 93 rd Annual Transportation Research Board, Washington DC, USA. Kamargianni, M. and A. Polydoropoulou, (2014) (b). Social Networking Effect on Net Generation’s Trip Making Behavior. Findings from a Latent Class Model. Paper presented at the 93 rd Annual Transportation Research Board, Washington DC, USA. Mokhtarian, P.L., I. Salomon, and S.L. Handy. The Impacts of ICT on Leisure Activities and  Travel: A Conceptual Exploration. Transportation, Vol. 33, 2006, pp. 263-289. Tawfik, A., and H. Rakha. Latent Class Choice Model of Heterogeneous Drivers' Route Choice Behavior Based on Real-World Experiment. Presented at 92nd Annual Meeting of the Transport Research Board, Washington D.C., 2013.  Van de Berg, P., T. Arentze, and H. Timmermans. A Latent Class Accelerated Hazard Model of Social Activity Duration. Presented at 90th Annual Meeting of the Transportation Research Board, Washington D.C., 2011. Walker, J., and J. Li. Latent Lifestyle Preferences and Household Location Decisions. Journal of Geographical Systems, Vol. 9(1), 2007, pp. 77-101.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.329
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2015
Admission routes1
Has abstractyes

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