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Route Recommendation based on Dynamic User Preference on Road Networks
http://doi.org/10.5626/JOK.2019.46.1.77
The current location based services provide maps and nearby information, or provide a route to a specific destination. A route recommendation system recommends the best route that suits the evaluation criteria for each user. The existing personalized path recommendation system recommends the route under the assumption that the user’s preference is constant regardless of the change of the time zone. However, there is a problem in that it does not reflect requirements that important factors to users can be different for each time zone, such as importance of moving distance in morning time and importance of risk in late time. In this paper, we propose a Dijkstra algorithm considering time attributes to overcome this limitation. In addition, we suggest an efficient algorithm that can search the path reflecting the change of the weight of the preference factor according to the time zone using the G-tree index structure that effectively expresses the road network.
Robust Particle Filter Based Route Inference for Intelligent Personal Assistants on Smartphones
Much research has been conducted on location-based intelligent personal assistants that can understand a user"s intention by learning the user"s route model and then inferring the user"s destinations and routes using data of GPS and other sensors in a smartphone. The intelligence of the location-based personal assistant is contingent on the accuracy and efficiency of the real-time predictions of the user"s intended destinations and routes by processing movement information based on uncertain sensor data. We propose a robust particle filter based on Dynamic Bayesian Network model to infer the user"s routes. The proposed robust particle filter includes a particle generator to supplement the incorrect and incomplete sensor information, an efficient switching function and an weight function to reduce the computation complexity as well as a resampler to enhance the accuracy of the particles. The proposed method improves the accuracy and efficiency of determining a user"s routes and destinations.
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