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| Abstract: |
| Short-term traffic flow prediction plays a crucial role in planning of intelligent transportation systems. Nowadays, there is a large amount of traffic flow data generated from the monitoring devices of urban road networks, which contains road network traffic information with high application value. In this study, an improved spatio-temporal attention transformer model (ISTA-Transformer Model) is proposed to provide a more accurate method for predicting multi-step short-term traffic flow based on monitoring data. By embedding a temporal attention layer and a spatial attention layer in the model, the model learns the relationship between traffic flows at different time intervals and different geographic locations, and realizes more accurate multi-step short-time flow prediction. Finally, we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao, China. In the four time steps of prediction, the MAPE (Mean Absolute Percentage Error) values of ISTA-Transformer's prediction results are 0.22, 0.29, 0.37, and 0.38, respectively, and its prediction accuracy is usually better than that of six baseline models (Transformer, GRU, CNN, LSTM, Seq2Seq and LightGBM), which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi-step prediction. |
| Key words: Urban Road Network, Traffic Flow Prediction, Spatio-temporal Feature, ISTA-Transformer Model |
| DOI：10.11916/j.issn.1005-9113.24075 |
| Clc Number:U121 |
| Fund: |
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| Descriptions in Chinese: |
| 短期交通流预测在智能交通系统规划中具有重要作用。目前，城市路网卡口监测设备收集了大量的交通数据，这些数据中包含着具有较高应用价值的路网交通信息。本文提出一种改进的时空注意力Transformer模型（ISTA-Transformer model），为基于卡口数据的多步短期交通流预测提供一种更准确的方法。该模型通过在模型中嵌入时间注意层和空间注意层，学习不同时间间隔和不同地理位置的交通流之间的关系，实现更准确的短时流量预测。实验部分，采用中国青岛620个监测点15天的监测数据验证了模型的优越性。在预测的4个时间步长中，ISTA-Transformer预测结果的MAPE值分别为0.22、0.29、0.37和0.38，其预测精度通常优于6个基线模型（Transformer、GRU、CNN、LSTM、Seq2Seq和LightGBM），这表明本文模型在多步预测中始终具有较好的时间步长解释预测结果的能力。 |
