Super Smart Society
Super-Smart society team researches on data analysis on transportation data, human flow, etc. to realize highly efficient society. Research topics are: delay estimation using bus operation data, socoiety-wide infrastructure system with demand-supply concept.
This study aims bus operation data to find/solve issues of current operation. We are developing a visualization system which can show bus location, getting-on-and-off of passengers, etc. Using the system, we can view the reproduction of bus operation and find issues.
Also, We are developing simulation system of when, where, how many passengers get on/off. Simulated bus operation leads us measure whether new under-review operation plan pays or not.
Location-based services are widely used with GPS-enabled mobile devices. However, some parts of data will be abandoned due to insufficient polling rate or low accuracy. The users with low quality lcation data cannot receive the same service with those with higer quality data.
This study is focusing on the estimation of users' feature from low quality data.
In this study we are developing a delivery robot which can collaborate with people. In this transition period towards automated warehouse, risk of collision accidents of automated cart and worker is rising. We need a novel robot control that takes the existence of people into account. We classify robot behavior like leading, following, etc. and evaluate usefulness in a simulated environment.
In the cases of scattered rain, pedestrians try to find an optimal route to avoid walking under the rain and getting wet. Depending on the current available services, pedestrians can know where in the city is raining at the moment, however, it is difficult to know exactly which route would be optimal to avoid getting wet while walking.
In this research, we try to estimate the optimal route for the pedestrians in a rainy weather, by calculating a rainy cost in a time-constraint extension to shortest route algorithm.
Autonomous driving car has many sensors to recognize surrounding envinronment. In this study, the information which is recorded by autonous driving car is used to detect the broken pavement.
For now, there is no service which shows detailed estimated time of arrival of city bus in Aichi. In this study, we are researching the method of providing realtime arrival estimation. Statistical analysis on past bus operation data is important for this study.
We also develop a service which can show estimated time and its reliability for users' confort. This study aims further development of bus transportation by creating values from bus operation data.