Identifying Route Preferences over Origin-Destination Using Cellular Network Data
Zhichao Guo, Tongyu Zhu
Available Online February 2018.
- https://doi.org/10.2991/csece-18.2018.9How to use a DOI?
- cellular network data; spatio-temporal data; trajectory data mining; origin-destination
- Current research on studying people's routing behavior focus on how to provide the minimum cost routes while ignore users' preference. Therefore, to analyze user's routing preference, identifying routes they actually choose is a crucial task. Cellular network data contains sufficient spatio-temporal information, which is widely used for trajectory analysis nowadays. However, it is a big challenge to extract precise trajectory from the cellular network data due to its low positioning accuracy. Compared to Density-Based Spatial Clustering of Applications with Noise algorithm, we present a Spatio-Temporal Density Clustering algorithm considering the timing sequence of the points to promote the precision of the trajectories, which aims to filter users' most probable routes with the map matching algorithm. Our approach could find out those most probable routes and the probability of each route. Finally, we experimented with real data. The results show that our approach is efficient for both extracting the probable trajectories and identifying multi-routes that users would prefer to route.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zhichao Guo AU - Tongyu Zhu PY - 2018/02 DA - 2018/02 TI - Identifying Route Preferences over Origin-Destination Using Cellular Network Data BT - 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/csece-18.2018.9 DO - https://doi.org/10.2991/csece-18.2018.9 ID - Guo2018/02 ER -