Research Article

Real-Time Prediction of Lane-Based Queue Lengths for Signalized Intersections

Table 3

MAE, MAPE, and RMSE of the predictions of traffic volume proportion in each lane.

Prediction methodsErrorsLane 1Lane 2Lane 3Average

Kalman filterMAE (vehs)All lanes (the proposed method)2.432.372.252.35
Single lane (the previous method)3.363.282.743.13
Single exponential smoothingSingle lane3.152.982.462.86
Quadratic exponential smoothingSingle lane2.782.772.312.62
Third-order moving averageSingle lane2.852.892.352.70
Kalman filterMAPE (%)All lanes (the proposed method)17.756.526.7110.33
Single lane (the previous method)24.969.188.0714.07
Single exponential smoothingSingle lane23.908.377.1313.11
Quadratic exponential smoothingSingle lane20.767.676.7911.74
Third-order moving averageSingle lane21.078.117.0712.08
Kalman filterRMSE (vehs)All lanes (the proposed method)3.443.322.703.15
Single lane (the previous method)4.384.343.314.01
Single exponential smoothingSingle lane4.613.823.033.82
Quadratic exponential smoothingSingle lane4.043.522.763.44
Third-order moving averageSingle lane3.823.472.763.35