Tide Gauges: From Single Hazard to Multi-Hazard Warning Systems

Texting while driving has been shown to impair driving performance with the greatest probability of leading to an accident. This is a great concern with young and inexperienced drivers, who are reported to be the most prolific users of texting while driving and are disproportionately involved in car crashes as compared to their experienced and older counterparts. Hazard Warning Systems (HWSs) have been researched to reduce distracted driving and improve driving performance. The first purpose of this study is to showcase a game-based, multi-player, online simulated training (GMOST) application with an integrated HWS. The second is to examine whether such an HWS integrated into the GMOST improves young and inexperienced drivers’ hazard perception skills, as measured by hazard reaction time (HRT) and horizontal road scanning (HS). A total of 22 high school students from a private school participated in this study. To determine the effects of HWS, a 2 × 2 ANOVA and a 2 × 2 MANOVA were run. The results of this study suggest that the GMOST with integrated HWS leads to earlier detection and reaction to hazards as well as wider HS by novice drivers. Therefore, this study reports that HWSs improve novice distracted drivers’ hazard perception skills. Accordingly, a wide-spread use of the GMOST-like training applications by novice drivers would be a proactive approach to lower accident rates caused by texting while driving.

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Multilingual Analysis of Twitter News in Support of Mass Emergency Events

International Journal of Information Systems for Crisis Response and Management ◽

10.4018/jiscrm.2013010105 ◽

2013 ◽

Vol 5 (1) ◽

pp. 77-85 ◽

Cited By ~ 4

Author(s):

Ulrich Bügel ◽

Andrea Zielinski

Keyword(s):

Crisis Management ◽

Relevant Information ◽

Early Warning Systems ◽

Actual Situation ◽

Civil Protection ◽

Warning Systems ◽

Tide Gauges ◽

Emergency Events ◽

Event Based ◽

English As Lingua Franca

Social media are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this article the authors study the problems of analyzing multilingual twitter feeds for emergency events. Specifically, they consider tsunami and earthquakes as one possible originating cause of tsunami. Twitter messages provide testified information and help to obtain a better picture of the actual situation. Generally, local civil protection authorities and the population are likely to respond in their native language. Therefore, the present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania. The authors investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks.

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Bridging Real-Time Precise Point Positioning in Natural Hazard Warning Systems during Outages of MADOCA Corrections

Proceedings of the ION 2017 Pacific PNT Meeting ◽

10.33012/2017.15075 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ahmed El-Mowafy ◽

Manoj Deo

Keyword(s):

Real Time ◽

Natural Hazard ◽

Precise Point Positioning ◽

Warning Systems ◽

Point Positioning ◽

Hazard Warning

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Predicting orbit and clock corrections during their outage in real-time positioning using GPS, GLONASS and QZSS for natural hazard warning systems

Journal of Applied Geodesy ◽

10.1515/jag-2018-0043 ◽

2019 ◽

Vol 13 (2) ◽

pp. 69-79

Author(s):

Ahmed El-Mowafy

Keyword(s):

Real Time ◽

Natural Hazard ◽

Time Window ◽

Model Parameters ◽

Prediction Errors ◽

Warning Systems ◽

International Gnss Service ◽

Positioning Method ◽

Clock Analysis ◽

Hazard Warning

Abstract Real-time Precise Point Positioning (RT PPP) is a primary positioning method used in natural hazard warning systems (NHWS) such as monitoring tsunami and earthquakes. The method relays on precise orbit and clock corrections to eliminate satellite-related errors and its performance can be significantly improved by using measurements from multi-GNSS constellations compared with using only one system, such as GPS. The Japanese Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) provides these corrections for GPS, GLONASS and QZSS satellites enabling a multi-GNSS RT-PPP. However, the accuracy of RT PPP will suffer a major decline in case of presence of an outage in receiving these corrections, for instance due to a temporary failure of the user modem. For that reason, a method is proposed to maintain RT PPP when such a break takes place. For short outages less than 30 minutes we predict MADOCA orbits using a Holt-Winters’ auto-regressive model, and for longer outages up to 1 hr, the most recent International GNSS Service (IGS) ultra-rapid orbits can be used, but only for GPS. In addition, the clock corrections are predicted as a time series using a linear model with sinusoidal terms. The best regression period to estimate the required model parameters is discussed based on analysis of the autocorrelation of the corrections. The prediction model parameters are estimated using a sliding time window. Evaluation of the proposed method showed that positioning accuracy of 15 cm was maintained during the prediction period, which is twice better than using IGS ultra-rapid predicted products. For NHSW, the displacement errors due to prediction errors were generally within ±6 cm with one min interval and ±10 cm with five min interval.

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