Probabilistic Modelling of Influences on Travel Decision Making

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

[email protected]

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


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


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


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


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


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


• 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


Causal re

asonin

g

Causal

Dia

gnosis

Expla

inin

g (

causes)

aw

ay

a) b)

c) d)

Tourism Model Generation1 Manual BN Model Composition


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


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 1 IBE AT_KINDERHOTELS 1 1085 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

Kontaktmöglichkeiten

11

twitter.com/insightTourism

Thank you!

Questions?

www.insight-tourism.at/

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.


Education

Probabilistic Modelling of Influences on Travel Decision Making