# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 5 Experiments  
### Datasets  
We conduct our experiments on two datasets. Mobile Signaling Data (MSD): A private dataset that contains two parts collected by China Mobile Communications Corporation; the first part is the raw mobile signaling data that contains the coarse moving patterns of mobile phone users in Beijing. The positioning accuracy is quite low, with an average of more than 300 m; the other part is OTT (over-the-top) data, which can be generated when mobile phone users use the Internet. OTT data contain precise locations of the users, but they are sparse compared with signaling data. OTT data were used as labels in this paper. Each user has more than 200 signaling records per day, while only 15 OTT records per day for each user. We chose the period from 4 March 2023 to 10 March 2023 and chose the mobile phone users in Beijing. The signaling data contains the information of the station ID, station location, reaching timestamp, and leaving timestamp. Geolife [42], which is collected in the Geolife project by 182 users from April 2007 to August 2012 by Microsoft Research Asia. Each record has the information of user ID, GPS location, and timestamps. Since most of the records are in Beijing, we use only the trajectories that are in Beijing, There are about 16,000 long trajectories in Beijing in the Geolife dataset. In this work, we mainly focus on the area within the Fifth Ring Road of Beijing, since over 70 percent of the trajectories in Beijing are located within the Fifth Ring Road of Beijing. Here, we present the dataset info in Table 2.  
**Preprocessing**: To obtain the coarse trajectory of the data and map it to a precise location, in the mobile signaling dataset, we use the station ID and its location of the station as the coarse location information and use the OTT record that matches the time range and the radius range of the signaling data, we reckon the detection range of base stations is 500 m. For each OTT record, the matched signaling record should match the time in the range of signaling record's time range. To generate the trajectory sequence in the model, as long as we match the OTT record and the signaling record, we use the present signaling record's last \(m\) records and the next \(m\) records to generate the whole coarse trajectory sequence, and we use the OTT record's location as the coarse trajectory's precise location of the whole coarse trajectory sequence, and this part is the sample's label. For the Geolife dataset, we partition the geographical space into grid cells of equal size and use the middle of the grid as the coarse position, which can also be regarded as virtual base stations. Each grid cell has a size of 500 m in width and height. All the points in the same grid are regarded as the same location, and the trajectory sequence will be constructed with the change of the grid; to construct a single record in \(p_{u,i}^{m}=(v_{u,i}^{n},lat_{u,i}^{n},lat_{u,i}^{n},t_{u,i}^{n}(in),t_{u,i}^{ n}(out))\cdot t_{u,i}^{n}(in)\) is the first timestamp of the first GPS point in the grid, while \(t_{u,i}^{n}(out)\) is the first timestamp of the first GPS point in the next grid. For the last record, \(t_{u,i}^{n}(out)\) is the timestamp of the last GPS point, and using this way, we construct \"virtual\" signaling records. We follow the same way of dealing with the MSD dataset and use the GPS points' coordinates in the matched grid as labels. For both datasets, following the definitions in the preliminaries, we filter out those sequences that have the following features, (1) \(\exists i,t_{u,i}^{n}(in)=t_{u,i}^{n}(out)\) (2) \(\exists i,t_{u,i}^{n}(out)\  
The next two baselines are machine learning methods. * LinearRegression: This is a traditional machine learning method. We use all the coordinates and the timestamps in the raw coarse trajectory and this method to adaptively reconstruct the precise position. * XGBoost: This statistical machine learning method did well in many areas. We use the same features as in the baseline of LinearRegression to reconstruct the precise position.