(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatio...
title={KDD CUP 2017 Highway Tollgates Traffic Flow Prediction Dataset}, url={https://tianchi.aliyun.com/dataset/dataDetail?dataId=60}, author={Tianchi}, year={2018}, } 如果您发表的论文有使用本数据集,请发邮件到tianchi_open_dataset@alibabacloud.com,回复论文链接,我们工作人员会给您寄送天池数据...
device: Tesla K80 dataset: PeMS 5min-interval traffic flow data optimizer: RMSprop(lr=0.001, rho=0.9, epsilon=1e-06) batch_szie: 256 Run command below to run the program: python main.py These are the details for the traffic flow prediction experiment. ...
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow,...
Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one...
Traffic flow predictions can be classified into short-term and medium–long-term predictions according to the cycle length. Short-term traffic flow prediction is a significant subject to ensure the construction of smart transportation and the smooth operation of the transportation system. Zhang, Ch...
To this end, we propose a novel Propagation Delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Specifically, we design a spatial self-attention module to capture the dynamic spatial dependencies. Then, two graph masking matrices are introduced to high...
天池实验室 数据集 公共数据集 正文 KDD CUP 2017 Highway Tollgates Traffic Flow Prediction Dataset 天池小喵萌2018-04-03202317CC-BY-SA-NC 4.0 新建Notebook 内容 Notebook 评论 No Data 0
The experimental traffic flow data are obtained from the Caltrans Performance Measurements Systems (PeMS) traffic dataset (PeMS, 2016), which is one of the most widely used datasets for traffic prediction. Sensors in the PeMS dataset are loop detectors that span the freeway across all major metro...
Traffic flow prediction with big data: a deep learning approach IEEE Trans. Intell. Transp. Syst., 16 (2014), pp. 865-873 Google Scholar Ma et al., 2017 D. Ma, X. Luo, W. Li, S. Jin, W. Guo, D. Wang Traffic demand estimation for lane groups at signal-controlled intersections...