UNDERSTANDING TRAVEL BEHAVIOR: A DEEP NEURAL NETWORK AND SHAP APPROACH TO MODE CHOICE DETERMINANTS
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F24%3A00378842" target="_blank" >RIV/68407700:21260/24:00378842 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.14311/NNW.2024.34.012" target="_blank" >https://doi.org/10.14311/NNW.2024.34.012</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2024.34.012" target="_blank" >10.14311/NNW.2024.34.012</a>
Alternative languages
Result language
angličtina
Original language name
UNDERSTANDING TRAVEL BEHAVIOR: A DEEP NEURAL NETWORK AND SHAP APPROACH TO MODE CHOICE DETERMINANTS
Original language description
Understanding individual travel behavior is crucial for developing effective travel demand management strategies and informed transportation policies. This study investigates the factors influencing individuals’ mode choices by analyzing data from a comprehensive travel survey. We employ a deep neural network model to explore the relationships between survey variables and respondents’ transportation mode preferences, focusing on both observable and latent factors. The SHAP method is applied to interpret the model’s outputs, providing global and local explanations that offer detailed insights into the contribution of each variable to mode choice decisions. By identifying the key determinants of mode selection and uncovering the complex interactions between these factors, this research provides valuable insights for designing targeted policies that can better address transportation needs and influence sustainable travel behavior.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Network World
ISSN
1210-0552
e-ISSN
2336-4335
Volume of the periodical
34
Issue of the periodical within the volume
4
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
23
Pages from-to
219-241
UT code for WoS article
001387819600001
EID of the result in the Scopus database
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