VLDB 2026 Research / reviewers in the wild / expert
Hayato Fukatsu
dblp:326/3004
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 2 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A GA-Based Safe Route Recommendation Method Based on Driver CharacteristicsabstractRoute recommendation services have been widely used due to the spread of mobile devices such as smartphones. However, conventional route recommendation services often recommend difficult and unsafe routes which require skilled driving techniques because conventional services aim to recommend the shortest route considering the travel distance and time. In a previous study, we proposed the method of route recommendation that minimizes the accident rate by estimating the accident rate for each road segment according to the characteristics of the driver. The Dijkstra algorithm used for route selection could only consider one link weight, so we obtained some routes that were unnecessarily increasing the route length. Therefore, in this study, we applied a genetic algorithm to route selection and set a threshold for distance in the constraints. In the simulation, it was confirmed that it can recommend safe routes that reduce the accident rate satisfying the constraints by changing the thresholds by parameters and evaluating the results. Hayato Fukatsu, Tomoya Kawakami, Yoshimi Kawamoto |
COMPSAC | 1 |
| 2022 | A Safe Route Recommendation Method Based on Driver Characteristics from Telematics DataabstractRoute recommendation services have been widely used due to the spread of mobile devices such as smartphones. However, conventional route recommendation services often rec-ommend difficult and unsafe routes which require skilled driving techniques because conventional services aim to recommend the shortest route considering the travel distance and time. Therefore, in this paper, we propose a safe route recommendation method. The proposed method estimates the accident rate for each road section based on driver characteristics from telematics data and recommends routes that minimize the estimated accident rate. A binary classification model is generated by machine learning from the acquired data, and the objective variable is the presence or absence of accidents. The features of the driver are input to the generated model and the accident rate is estimated for the case where that driver travels that road section. Simulation evaluation confirmed that the proposed method can recommend routes with lower accident rates than the distance-minimizing route recommendation method. Hayato Fukatsu, Tomoya Kawakami |
COMPSAC | 1 |