probabilistic modelling of influences on travel decision making
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SCCH is an initiative of SCCH is located in
Probabilistic Modelling of Influences on Travel Decision Making
M. Pichler, L. Steiner, H. Neiß
Dr. Mario Pichler
+43 7236 3343 898
www.scch.at
February 6th
2 © Software Competence Center Hagenberg GmbH
Background: insightTourism
Optimization of tourism investment
decisions based on valid demand analysis
by integrating social media and web data
funded by the Austrian Research Promotion
Agency (FFG) within the COIN program line
Nov. 2013 – Oct. 2015
Project partners (academic & companies)
Software Competence Center Hagenberg GmbH
Seekda GmbH
visit.at - visualisierungs und informationstechnologie
Austrian Academy of Sciences (Inst. IGF)
Utopia Refraktor Ltd & Co KG
Johannes Kepler University Linz, Tourism Management
Hotel Edelweiß & Gurgl Scheiber GmbH
1. Analysis – Identification of Tourism Influence Factors
3 © Software Competence Center Hagenberg GmbH
Data analysis, data mining, literature research, expert interviews
Known and novel influence factors
Family friendliness
Young generation
Funpark
Petrol price
Car
Railway
Weather
Sustainable tourism
region
Event tourism
Night life
Event infrastructure
Nature
Seminar tourism
Playground
2. Model Building and Quantification
4 © Software Competence Center Hagenberg GmbH
Model building and quantification of relationships (data and expert
knowledge)
Quantified model of influence factors and relationships
Family friendliness
Young generation
Playground Funpark Petrol price
Car Railway
Weather
Sustainable tourism
region
Event tourism
Night life
Event infrastructure
Nature
Seminar tourism
0,86 0,84
0,53
0,44
0,76
0,91 0,61
0,46
0,94
0,72
0,47 0,85
0,83
Objective: Tourism Knowledge Model for Scenario Analyses
5 © Software Competence Center Hagenberg GmbH
Scenario analyses (modifying model parameters, different weights of influence factors, updated relationships
different action options of tourism professionals)
Analysis of different scenarios based on probabilistic graphical model
Family friendliness
Young generation
Funpark Petrol price
Car Railway
Weather
Sustainable tourism
region
Event tourism
Night life
Event infrastructure
Nature
Seminar tourism
0,86 0,84
0,53
0,44
0,76
0,91 0,61
0,46
0,94
0,72
0,47 0,85
0,83
Playground
Modelling Approach: Bayesian Networks
6 © Software Competence Center Hagenberg GmbH
• Basic principle
– Bayes (1763)
• Founder
– Pearl (1985)
• Example model
– Korb and
Nicholson (2010)
P(P) P(S)
P(C|P,S)
P(D|C) P(X|C)
Defining conditional probabilities: historical/statistical data or expert knowledge
low high
0.90 0.10
Air Pollution (P)
yes no
0.30 0.70
Smoker (S)
Poll. (P) Smok. (S) yes no
high yes 0.05 0.95
high no 0.02 0.98
low yes 0.03 0.97
low no 0.001 0.999
Cancer (C)
Cancer positive negative
yes 0.90 0.10
no 0.20 0.80
X-Ray (X) Cancer yes no
yes 0.65 0.35
no 0.30 0.70
Dyspnoea (D)
Bayesian Networks: A-priori Probability Distribution
7 © Software Competence Center Hagenberg GmbH 7 © Software Competence Center Hagenberg GmbH
exploitation for different reasoning tasks …
Dia
gn
osis
Reasoning with Bayesian Networks
8 © Software Competence Center Hagenberg GmbH
Causal re
asonin
g
Causal
Dia
gnosis
Expla
inin
g (
causes)
aw
ay
a) b)
c) d)
Tourism Model Generation1 Manual BN Model Composition
9 © Software Competence Center Hagenberg GmbH
Satisfied Unsatisfied
70% 30%
Trust Mistrust
40% 60%
Usage of influence factors
from previous studies
Creation of model structure
Definition of parameters
and quantification
Scenario analyses
...
High Low
Trust, Satisfied 90% 10%
Trust, Unsatisfied 40% 60%
Mistrust, Satisfied 30% 70%
Mistrust, Unsatisfied 5% 95%
Manual BN
generation approach
Nunkoo &
Ramkissoon
(2011)
Lee et al. (2013)
Tourism Model Generation2 Data-driven BN Model Learning
10 © Software Competence Center Hagenberg GmbH
Status Country Language Adults Children Source SourceContext Rooms Amount NonSmoking Mealplan RoomType
reserved Schweiz de 2 2 IBE trivago 1 753 TRUE 1 Zimmer
reserved Österreich de 1 1 IBE AT_KINDERHOTELS 1 348 TRUE 1 Zimmer
reserved Schweiz de 2 1 IBE #NUL! 1 217 TRUE 1 Zimmer
reserved Deutschland de 2 2 IBE #NUL! 1 1841 TRUE 1 Zimmer
reserved Deutschland de 2 2 IBE #NUL! 1 1806 TRUE 1 Zimmer
reserved Deutschland de 2 1 IBE AT_KINDERHOTELS 1 1085 TRUE 1 Zimmer
reserved Deutschland de 2 2 IBE #NUL! 1 1760 TRUE 1 Zimmer
reserved Deutschland de 2 1 IBE #NUL! 1 1246 TRUE 1 Zimmer
reserved Deutschland de 2 1 IBE AT_KINDERHOTELS 1 1246 TRUE 1 Zimmer
2. Scenario
analyses
Probabilistic Structural Equation
Model (PSEM), Conrady & Jouffe (2013)
Latent
factor
variables
1. BN model
learning from
tourism data
sources
References
Bayes, T. (1763): An Essay towards Solving a Problem in the Doctrine of Chances.
Philosophical Transactions, 53:370–418.
Conrady, S. & Jouffe, L. (2013). Tutorial on Driver Analysis and Product Optimization with
BayesiaLab. Available online: http://library.bayesia.com/display/whitepapers/
Driver+Analysis+and+Product+Optimization [last access: 2014/09/06].
Korb, K.B. & Nicholson, A.E. (2010): Bayesian Artificial Intelligence. CRC Press, 2. Ed.
Lee, K.; Lee, H. & Ham, S. (2013): The Effects of Presence Induced by Smartphone
Applications on Tourism: Application to Cultural Heritage Attractions . In Xiang, Z. &
Tussyadiah, I. (Eds.) Information and Communication Technologies in Tourism 2014,
Springer International Publishing, 59-72.
Nunkoo, R. & Ramkissoon, H. (2011): Developing a community support model for tourism.
Annals of Tourism Research, 38:964-988.
Pearl, J. (1985): Bayesian networks: a model of self-activated memory for evidential
reasoning. In: Cognitive Science Society 1985. UC Irvine, S. 329–334.
12 © Software Competence Center Hagenberg GmbH