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Yihang Lu

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EAAI Journal 2024 Journal Article

IODRNN - Incremental output decomposition for a valid traffic flow prediction with GNSS data

Yihang Lu
Xianwei meng
Liqun Peng
Shucai Xu
Enhong Chen

Traffic flow prediction, a crucial application of intelligent transportation systems (ITS), has become an increasingly prevalent research topic. However, existing models that achieved high prediction accuracy on selected metrics may suffer from time delay in prediction curves which has been rarely explored. These models may produce seemingly accurate but invalid predictions by merely tracking and replicating previous true values. To address this anomaly, we propose a highly interpretable prediction mechanism, the Incremental Output Decomposition Recurrent Neural Network (IODRNN). We also introduce a new metric called Shift Divergence Difference (SDD) to assess the degree of latency of the overall sequence and evaluate the effectiveness of IODRNN in reducing the delay phenomenon. Our experimental results using real-world GNSS traffic data show that IODRNN has the smallest degree of latency and improves MAE and RMSE by 16. 8% and 17. 4% on average, respectively, over most contrast models. Our study presents an effective approach to evaluate prediction latency, ensuring validity and robustness in traffic prediction.

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IJCAI Conference 2021 Conference Paper

Discrete Multiple Kernel k-means

Rong Wang
Jitao Lu
Yihang Lu
Feiping Nie
Xuelong Li

The multiple kernel k-means (MKKM) and its variants utilize complementary information from different kernels, achieving better performance than kernel k-means (KKM). However, the optimization procedures of previous works all comprise two stages, learning the continuous relaxed label matrix and obtaining the discrete one by extra discretization procedures. Such a two-stage strategy gives rise to a mismatched problem and severe information loss. To address this problem, we elaborate a novel Discrete Multiple Kernel k-means (DMKKM) model solved by an optimization algorithm that directly obtains the cluster indicator matrix without subsequent discretization procedures. Moreover, DMKKM can strictly measure the correlations among kernels, which is capable of enhancing kernel fusion by reducing redundancy and improving diversity. What’s more, DMKKM is parameter-free avoiding intractable hyperparameter tuning, which makes it feasible in practical applications. Extensive experiments illustrated the effectiveness and superiority of the proposed model.

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