| Title |
Personalized Path Recommendation With Specified Way-Points Based On Trajectory Representations |
| ID_Doc |
41920 |
| Authors |
Wang B.; Guo Y.; Chen Y. |
| Year |
2021 |
| Published |
Proceedings - 2021 17th International Conference on Mobility, Sensing and Networking, MSN 2021 |
| DOI |
http://dx.doi.org/10.1109/MSN53354.2021.00067 |
| Abstract |
With the development of the smart city, personalized path recommendation has already attracted the attention of researchers. However, it has not been considered that some users need to make personalized recommendations of a route for a given pair of OD (Origin-Destination) and some specified consecutive way-points. To our knowledge, this problem is studied for the first time. Essentially, this problem can be taken as inferring a high sampling rate fine trajectory from a low sampling rate rough trajectory composed of OD and way-points. The biggest challenge is that the user may just have some knowledge about the location rather than the precise location of a waypoint because a place may have many GPS points. This paper proposes a PSR (Personalized Selective Route) model based on trajectory learning for a given OD and a number of way-points. It integrates multi-source information to learn more comprehensive personalized preferences and introduces the Seq2Seq model with Multi-Head self-attention mechanism to automatically adjust the weights to capture more accurate temporal and spatial correlations. The experiments on the real traffic trajectory data show PSR model is robust to low sampling rate and noise. Compared with the best baseline, the accuracy of Top-l under the Euclidean distance of PSR is improved by 32.57%, and the accuracy of Top-3 is improved by 63.87%. © 2021 IEEE. |
| Author Keywords |
deep representation; Route recommendation; trajectory mining |
