TY  - EJOU
AU  - Jin, Canghong 
AU  - Chen, Jiapeng 
AU  - Wu, Shuyu 
AU  - Wu, Hao 
AU  - Wang, Shuoping 
AU  - Ying, Jing 

TI  - CALTM: A Context-Aware Long-Term Time-Series Forecasting Model
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 139
IS  - 1
SN  - 1526-1506

AB  - Time series data plays a crucial role in intelligent transportation systems. Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval. Existing approaches, including sequence periodic, regression, and deep learning models, have shown promising results in short-term series forecasting. However, forecasting scenarios specifically focused on holiday traffic flow present unique challenges, such as distinct traffic patterns during vacations and the increased demand for long-term forecastings. Consequently, the effectiveness of existing methods diminishes in such scenarios. Therefore, we propose a novel long-term forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow. Our model comprises three components: the similar scene matching module, responsible for extracting Similar Scene Features; the long-short term representation fusion module, which integrates scenario embeddings; and a simple fully connected layer at the head for making the final forecasting. Experimental results on real datasets demonstrate that our model outperforms other methods, particularly in medium and long-term forecasting scenarios.
KW  - Traffic volume forecasting; scene matching; multi module fusion

DO  - 10.32604/cmes.2023.043230