TBSM: A traffic burst-sensitive model for short-term prediction under special events

Traffic prediction is an important management tool for traffic guidance and control and an effective decision-making tool to help travelers plan routes and avoid congested road sections. However, due to the transient and sudden nature of traffic bursts caused by events and data limitations, mainstream methods do not perform well in short-term traffic prediction for special events (SEs). To address this challenge, we propose a traffic burst-sensitive model (TBSM) for short-term traffic prediction. Specifically, we first define a new state unit with the short-term trend and observed state to represent both the burst case and usual case. Second, a state-and-trend unit similarity degree (SD) measurement method and increment-based prediction model are proposed. The key parameter of this model balances the weight of the short-term trend with the observed state. Finally, we use a deep deterministic policy gradient (DDPG) framework containing long short-term memory (LSTM) networks to realize the self-learning and adjustment of weights to ensure the generality and burst sensitivity of the model. The TBSM is implemented in the district of Beijing Workers' Stadium, where SEs occur frequently. The results demonstrate that the proposed model performs significantly better than other traditional machine learning approaches and deep learning approaches for SEs. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Traffic prediction is crucial for traffic guidance and control, and it serves as a valuable decision-making tool for travelers to plan their routes and avoid congested roads. To tackle the challenge of short-term traffic prediction for special events, a traffic burst-sensitive model (TBSM) is proposed, which outperforms traditional machine learning and deep learning approaches according to experimental results.