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Figure 1 | EURASIP Journal on Wireless Communications and Networking

Figure 1

From: Real-time urban traffic amount prediction models for dynamic route guidance systems

Figure 1

Prediction errors versus prediction interval τ . (a) MAE (b) SMAPE. Model-1 has the best prediction accuracy and reduces MAE and SMAPE by up to 52% and 41% respectively compared to the Shift Model in the best case (τ=10 s). Model-2 also reduces MAE and SMAPE by up to 30% (τ=10 s) and SMAPE by 28% (τ=300 s) compared to the Shift Model in the best cases. Reducing τ may improve prediction accuracy, but short τ is not always achievable as it is bounded by the latency of traffic data collection and data processing at the control center.

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