Update README.md

README.md

@@ -108,26 +108,29 @@ Blow shows the performance of different methods in Shanghai.
 
 Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.
 
+
 | Method       | HR@3         | KRC          | LSD         | ED          |
 |--------------|--------------|--------------|-------------|-------------|
-| TimeGreedy   | 57.65        | 31.81        | 5.54        | 2.15        |
-| DistanceGreedy | 60.77        | 39.81        | 5.54        | 2.15        |
-| OR-Tools     | 66.21        | 47.60        | 4.40        | 1.81        |
-| LightGBM     | 73.76        | 55.71        | 3.01        | 1.84        |
-| FDNET        | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 |
-| DeepRoute    | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 |
-| Graph2Route  | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 |
+| TimeGreedy   | 59.81        | 39.93        | 5.20        | 2.24        |
+| DistanceGreedy | 61.07        | 42.84        | 5.35        | 1.94        |
+| OR-Tools     | 62.50        | 44.81        | 4.69        | 1.88        |
+| LightGBM     | 70.63        | 54.48        | 3.27        | 1.92        |
+| FDNET        | 69.05 ± 0.47 | 52.72 ± 1.98 | 4.08 ± 0.29 | 1.86 ± 0.03 |
+| DeepRoute    | 71.66 ± 0.11 | 56.20 ± 0.27 | 3.26 ± 0.08 | 1.86 ± 0.01 |
+| Graph2Route  | 71.69 ± 0.12 | 56.53 ± 0.12 | 3.12 ± 0.01 | 1.86 ± 0.01 |
+| DRL4Route    | 72.18 ± 0.18 | 57.20 ± 0.20 | 3.06 ± 0.02 | 1.84 ± 0.01 |
 
 
 ## 4.2 Estimated Time of Arrival Prediction
 
-| Method | MAE          | RMSE         | ACC@30      |
+| Method | MAE          | RMSE         | ACC@20      |
 | ------ |--------------|--------------|-------------|
-| LightGBM | 17.48        | 20.39        | 0.85        |
-| SPEED | 23.75        | 27.86        | 0.73        |
-| KNN | 21.28        | 25.36        | 0.75        |
-| MLP | 21.54 ± 2.20 | 25.05 ± 2.46 | 0.79 ± 0.04 |
-| FDNET | 18.47 ± 0.25 | 21.44 ± 0.28 | 0.84 ± 0.01 |
+| LightGBM | 17.48        | 20.39        | 0.68        |
+| SPEED | 23.75        | 27.86        | 0.58        |
+| KNN | 21.28        | 25.36        | 0.60        |
+| MLP | 18.58 ± 0.37 | 21.54 ± 0.34 | 0.66 ± 0.02 |
+| FDNET | 18.47 ± 0.31 | 21.44 ± 0.34 | 0.67 ± 0.02 |
+| RANKETPA | 17.18 ± 0.06 | 20.18 ± 0.08 | 0.70 ± 0.01 |
 
 
 ## 4.3 Spatio-temporal Graph Forecasting