We lifted 10 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| chenyuqi990215/rntrajrec | canonical | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| Euclidean | Ran | chenyuqi990215/rntrajrec/preprocess/epsilon_trajectory.py code served (permissive licence) · get_code("27322694f4514002") |
| epoch_time | Ran | chenyuqi990215/rntrajrec/multi_main.py code served (permissive licence) · get_code("3e21e6589663b136") |
| exp_prob | Ran | chenyuqi990215/rntrajrec/utils/model_utils.py code served (permissive licence) · get_code("acdddab85d978810") |
| getTrajs | Ran | chenyuqi990215/rntrajrec/multi_train.py code served (permissive licence) · get_code("13091511b1179f9a") |
| get_dict_info_batch | Ran | chenyuqi990215/rntrajrec/model.py code served (permissive licence) · get_code("5cb5c9b299bca3a4") |
| get_reachable_inds | Ran | chenyuqi990215/rntrajrec/utils/model_utils.py code served (permissive licence) · get_code("95e5cedc8a0ff8de") |
| haversine | Ran | chenyuqi990215/rntrajrec/preprocess/epsilon_trajectory.py code served (permissive licence) · get_code("d318a6bb973f586e") |
| mask_log_softmax | Ran | chenyuqi990215/rntrajrec/model.py code served (permissive licence) · get_code("2e740dad9477b735") |
| center_geolocation | Not yet run | chenyuqi990215/rntrajrec/preprocess/epsilon_trajectory.py code served (permissive licence) · get_code("bfb3a2261a872bac") |
| projectToSegment | Not yet run | chenyuqi990215/rntrajrec/preprocess/hmm.py code served (permissive licence) · get_code("61c406a953115e52") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
GPS trajectories are the essential foundations for many trajectory-based applications, such as travel time estimation, traffic prediction and trajectory similarity measurement. Most applications require a large amount of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints.We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories. Currently, most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and lack spatial consistency. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model into a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.13234")
get_code_for_paper("2211.13234")
have("2211.13234")
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