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International Journal of Intelligent Transportation Systems Research
Published in:

26-04-2019

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

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

Published in: International Journal of Intelligent Transportation Systems Research | Issue 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.
previous article Real-Time Driver Drowsiness Detection Using Wavelet Transform and Ensemble Logistic Regression
next article CPS-Based Reliability Enhancement Mechanism for Vehicular Emergency Warning System

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Metadata
Title
Driver Behavior Response to Information Presentation Based on the Emergency Evacuation Procedure of the Great East Japan Earthquake
Authors
Shigeyuki Yamabe
Fumihiko Hasegawa
Takahiro Suzuki
Koichiro Kamata
Kensuke Hatakeyama
Osamu Ito
Publication date
26-04-2019
Publisher
Springer US
Published in
International Journal of Intelligent Transportation Systems Research / Issue 3/2019
Print ISSN: 1348-8503
Electronic ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-019-00179-0

Other articles of this Issue 3/2019

CPS-Based Reliability Enhancement Mechanism for Vehicular Emergency Warning System

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

Traffic Density Recognition Based on Image Global Texture Feature

Multi-view Low Rank Representation for Multi-Source Traffic Data Completion

Stability Analysis of Following Vehicles on a Highway for Safety of Automated Transportation Systems

A New Lane-Changing Model with Consideration of Driving Style

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