| Paper | Model Name | Year | Model | Evaluation | Dataset | Code |
|---|---|---|---|---|---|---|
| Chen et al. Context-aware Deep Model for Joint Mobility and Time Prediction | DeepJMT | 2020 | GRU, FC, Encoder | ACC@k | bit.ly/Foursquare-Data | |
| Yang et al. Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States | Flashback | 2020 | Attention, RNN | ACC@k | bit.ly/GowallaData | bit.ly/Flashback-1 |
| Ebel et al. Destination Prediction Based on Partial Trajectory Data | - | 2020 | RNN | Distance | bit.ly/TaxiPorto, bit.ly/TaxiSF | |
| Rossi et al. Modelling Taxi Drivers’ Behaviour for the Next Destination Prediction | - | 2019 | Attention, LSTM | Distance | bit.ly/TaxiPorto, bit.ly/TaxiSF, bit.ly/TaxiNYC-2 | |
| Gao et al. Predicting human mobility via variational attention | VANext | 2019 | CNN, GRU | ACC@k | bit.ly/GowallaData | |
| Kong et al. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction | HST-LSTM | 2018 | LSTM | ACC | - | bit.ly/HST-LSTM |
| Lv et al. T-CONV: A convolutional neural network for multi-scale taxi trajectory prediction | T-VONC | 2018 | CNN | Distance | bit.ly/TaxiPorto | bit.ly/T-CONV |
| Feng et al. Deepmove: Predicting human mobility with attentional recurrent networks | DeepMove | 2018 | Attention, RNN | ACC | bit.ly/DeepMove | bit.ly/DeepMove |
| Yao et al. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction | SERM | 2017 | LSTM | ACC@k | - | bit.ly/SERM-Repo |
| Liu et al. Predicting the next location: A recurrent model with spatial and temporal contexts | ST-RNN | 2016 | RNN | Rec@k, F1@k, MAPE, AUC | bit.ly/GowallaData, bit.ly/GTD | bit.ly/STRNN |
| De Brébisson et al. Artificial neural networks applied to taxi destination prediction | - | 2015 | FC | Distance | bit.ly/TaxiPorto | bit.ly/next-loc-1 |