Recent literature review on neural networks in delay prediction
| Literature | Method | Input data | Characteristics |
|---|---|---|---|
| Huang, Spanninger, and Corman (2022) | CLF-Net (3DCNN, LSTM, FCNN) | Spatio-temporal features, timetable features (time-series), infrastructure (non-time-series) | It is the first time that static, temporal, and spatio-temporal data are simultaneously considered in a hybrid model |
| Li et al. (2022) | LLCF (CNN, two LSTM, FCNN) | Considers the arrival routes of predicted trains and route conflicts with forward trains | The detailed train arrival/departure routes are considered from a microscopic view in the proposed arrival delay prediction model |
| Heglund et al. (2020) | STGCN | A sequence of node features that are the arrival delay of trains passing through links | Consider the connections between elements in the rail network |
| Ding et al. (2021) | MTGNN | The actual delay and infrastructure data of trains at previous stations | Combines graph learning, graph convolution, and temporal convolution modules to predict train arrival delays under different causes |
| Zhang et al. (2021) | TSTGCN (SAtt, TAtt, GCN) | Recent time series, daily time series, weekly time series | Predict the total number of delayed trains in each railway station |
| Xu et al. (2022) | DB-STGCN (STGCN, DBN) | Timetable-related variables, delay pattern variables, infrastructure-related variables, and weather-related variables | Consider train delay patterns and dynamic interactions between train events, and study the dynamic causality of train delay propagation |
| Literature | Method | Input data | Characteristics |
|---|---|---|---|
| CLF-Net (3DCNN, LSTM, FCNN) | Spatio-temporal features, timetable features (time-series), infrastructure (non-time-series) | It is the first time that static, temporal, and spatio-temporal data are simultaneously considered in a hybrid model | |
| LLCF (CNN, two LSTM, FCNN) | Considers the arrival routes of predicted trains and route conflicts with forward trains | The detailed train arrival/departure routes are considered from a microscopic view in the proposed arrival delay prediction model | |
| STGCN | A sequence of node features that are the arrival delay of trains passing through links | Consider the connections between elements in the rail network | |
| MTGNN | The actual delay and infrastructure data of trains at previous stations | Combines graph learning, graph convolution, and temporal convolution modules to predict train arrival delays under different causes | |
| TSTGCN (SAtt, TAtt, GCN) | Recent time series, daily time series, weekly time series | Predict the total number of delayed trains in each railway station | |
| DB-STGCN (STGCN, DBN) | Timetable-related variables, delay pattern variables, infrastructure-related variables, and weather-related variables | Consider train delay patterns and dynamic interactions between train events, and study the dynamic causality of train delay propagation |
Source(s): Author's own work
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