| Citation: | FENG X Y，CHEN Z L，JI N，et al. Short-term traffic state prediction under planned special events[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（10）：2721-2730 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0758
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Accurate short-term traffic state prediction is an important basis for effective traffic management and control. The planned special events (PSEs) generate abnormal traffic demand around the venue in a short time. However, due to the limited number of the special events and the difficulty in data sample collection, the prediction accuracy is hard to guarantee.Therefore, the short-term traffic evolution characteristics under PSEs are analyzed by measured data. On this basis, a short-term traffic state prediction model is established by using the framework of improved K-nearest neighbor (KNN) algorithm. Therefore, the evolution characteristics of short-term traffic under PSEs are analyzed through real event data, and a short-term traffic state KNN (PSE-KNN) prediction model was proposed. Moreover, through real-time super parameter optimization method based on Deep reinforcement learning, we constructed into an adaptive PSE-KNN (APSE-KNN) model. Finally, the effect of the model is verified by taking the concert scene in Beijing as an example. The results show that in the multi-step prediction experiment, compared with the other seven comparative prediction models, the proposed prediction model reduces the mean residual error by 12.43 % and the mean absolute percentage error by 29.90 % on average. These results prove that this model has excellent rapid adjustment ability and is more suitable for short-term traffic state prediction task under PSEs.
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