Assessing a risk-avoidance navigation system based on localized torrential rain data

NICT Big Data Analytics Laboratory, National Institute of Information and Communications Technology, Nukuikitamachi 4-2-1, Koganei, Tokyo 184-8795, Japan

^a Corresponding author: sadanori.ito@nict.go.jp

Abstract

Localized torrential rainfall events and related traffic problems are increasing in Japan, suggesting the need for a navigation-alert system to help drivers avoid such risks. Based on ongoing developments of weather radar systems for early detection of localized torrential rain and a cross-data collaboration platform for traffic optimization, in this study we tested the application of a route-guidance system that can help drivers avoid heavy rainfall. Participants were given equivalent levels of pre-training un the early detection of rainfall and the relationship between rainfall and accidents, then allowed to test a driving simulator set up with four alert methods, three route options, and four levels of possible risk avoidance. Using this system, the heavy rain avoidance rate was 85.63%, suggesting that such a system would be socially acceptable and useful, though further research is needed to refine the specific approach.