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International Journal of Intelligent Transportation Systems Research
Erschienen in:

26.04.2019

Driver Behavior Response to Information Presentation Based on the Emergency Evacuation Procedure of the Great East Japan Earthquake

verfasst von: Shigeyuki Yamabe, Fumihiko Hasegawa, Takahiro Suzuki, Koichiro Kamata, Kensuke Hatakeyama, Osamu Ito

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 3/2019

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Abstract

After the Great East Japan Earthquake (GEJE), many people who tried to evacuate by vehicles were swallowed by tsunami. To clarify the driver’s acceptability of information at a disaster from the driver’s behavior, we measured driver’s behavioral response in response to the presence or absence of emergency evacuation information at the earthquake using driving simulator capable of reproducing earthquakes. Even if we receive evacuation information, there are drivers that do not follow the information, and it has been confirmed that we keep traffic rules despite emergency situations. On the other hand, it was also confirmed that drivers who accept everything on faith of the information and cannot judge their own. This suggested the danger of information presentation methods in a disaster.
Vorheriger Artikel Real-Time Driver Drowsiness Detection Using Wavelet Transform and Ensemble Logistic Regression
Nächster Artikel CPS-Based Reliability Enhancement Mechanism for Vehicular Emergency Warning System

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Metadaten
Titel
Driver Behavior Response to Information Presentation Based on the Emergency Evacuation Procedure of the Great East Japan Earthquake
verfasst von
Shigeyuki Yamabe
Fumihiko Hasegawa
Takahiro Suzuki
Koichiro Kamata
Kensuke Hatakeyama
Osamu Ito
Publikationsdatum
26.04.2019
Verlag
Springer US
Erschienen in
International Journal of Intelligent Transportation Systems Research / Ausgabe 3/2019
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-019-00179-0

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CPS-Based Reliability Enhancement Mechanism for Vehicular Emergency Warning System

A New Lane-Changing Model with Consideration of Driving Style

Real-Time Driver Drowsiness Detection Using Wavelet Transform and Ensemble Logistic Regression

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