# Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework

arXiv: https://arxiv.org/abs/1612.01022 · HTML: https://mlsanity.com/paper/1612.01022/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2016.

## Built on by (most cited first, 23 of 23)

- [Residual Correction in Real-Time Traffic Forecasting](https://mlsanity.com/paper/2209.05406/) (2022)
- [AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting](https://mlsanity.com/paper/2101.12465/) (2021)
- [Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow](https://mlsanity.com/paper/2107.13226/) (2021)
- [A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2205.15218/) (2022)
- [Earthquake Nowcasting with Deep Learning](https://mlsanity.com/paper/2201.01869/) (2021)
- [Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2103.06126/) (2021)
- [TrafFormer: A Transformer Model for Predicting Long-term Traffic](https://mlsanity.com/paper/2302.12388/) (2023)
- [GraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction](https://mlsanity.com/paper/2502.19823/) (2025)
- [TempoKGAT: A Novel Graph Attention Network Approach for Temporal Graph Analysis](https://mlsanity.com/paper/2408.16391/) (2024)
- [FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction](https://mlsanity.com/paper/2412.17373/) (2024)
- [Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting](https://mlsanity.com/paper/2511.08888/) (2025)
- [GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series](https://mlsanity.com/paper/2110.14331/) (2021)
- [Spatial-Temporal Attention Fusion Network for short-term passenger flow prediction on holidays in urban rail transit systems](https://mlsanity.com/paper/2203.00007/) (2022)
- [Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation](https://mlsanity.com/paper/2210.07246/) (2022)
- [Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach](https://mlsanity.com/paper/2310.12353/) (2023)
- [Short-term prediction of construction waste transport activities using AI-Truck](https://mlsanity.com/paper/2312.04609/) (2023)
- [Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach](https://mlsanity.com/paper/2312.05576/) (2023)
- [A Multi-Graph Convolutional Neural Network Model for Short-Term Prediction of Turning Movements at Signalized Intersections](https://mlsanity.com/paper/2406.00619/) (2024)
- [Temporal Attention Evolutional Graph Convolutional Network for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2505.00302/) (2025)
- [The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics](https://mlsanity.com/paper/2505.08822/) (2025)
- [Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting](https://mlsanity.com/paper/2508.00884/) (2025)
- [Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction](https://mlsanity.com/paper/2602.22274/) (2026)
- [EMAGN: Efficient Multi-Attention Graph Network via Learned Clustering for Scalable Traffic Forecasting](https://mlsanity.com/paper/2607.13241/) (2026)

## Abstract (arXiv)

Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can successfully characterize the temporal correlation, thus exhibits superior capability for time series tasks. Traffic flow data have plentiful characteristics on both time and space domain. However, applications of CNN and LSTM approaches on traffic flow are limited. In this paper, we propose a novel deep architecture combined CNN and LSTM to forecast future traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow. Given those meaningful features, the feature-level fusion is performed to achieve short-term forecasting. The proposed CLTFP is compared with other popular forecasting methods on an open datasets. Experimental results indicate that the CLTFP has considerable advantages in traffic flow forecasting. in additional, the proposed CLTFP is analyzed from the view of Granger Causality, and several interesting properties of CLTFP are discovered and discussed .