# Urban Regional Function Guided Traffic Flow Prediction

arXiv: https://arxiv.org/abs/2303.09789 · HTML: https://mlsanity.com/paper/2303.09789/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 57 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering](https://mlsanity.com/paper/2207.12647/) (2022)
- [Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks](https://mlsanity.com/paper/1903.07789/) (2019)
- [Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition](https://mlsanity.com/paper/2009.00210/) (2020)
- [TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning](https://mlsanity.com/paper/2112.03587/) (2021)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition](https://mlsanity.com/paper/1911.10751/) (2019)
- [Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition](https://mlsanity.com/paper/1809.00421/) (2018)
- [Global Temporal Representation based CNNs for Infrared Action Recognition](https://mlsanity.com/paper/1909.08287/) (2019)
- [TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2011.14638/) (2020)
- [Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition](https://mlsanity.com/paper/1909.08297/) (2019)
- [A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism](https://mlsanity.com/paper/2205.04762/) (2022)

## Built on by (most cited first, 12 of 12)

- [SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training](https://mlsanity.com/paper/2307.08476/) (2023)
- [STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction](https://mlsanity.com/paper/2307.00495/) (2023)
- [CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language](https://mlsanity.com/paper/2505.23837/) (2025)
- [DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback](https://mlsanity.com/paper/2306.07553/) (2023)
- [CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning](https://mlsanity.com/paper/2306.17462/) (2023)
- [MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments](https://mlsanity.com/paper/2402.00290/) (2024)
- [ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Prediction](https://mlsanity.com/paper/2404.15734/) (2024)
- [VCD: Visual Causality Discovery for Cross-Modal Question Reasoning](https://mlsanity.com/paper/2304.08083/) (2023)
- [A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units](https://mlsanity.com/paper/2403.07022/) (2024)
- [Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting](https://mlsanity.com/paper/2408.16191/) (2024)
- [Robust Spatiotemporal Forecasting Using Adaptive Deep-Unfolded Variational Mode Decomposition](https://mlsanity.com/paper/2509.00703/) (2025)
- [MoE-TransMov: A Transformer-based Model for Next POI Prediction in Familiar & Unfamiliar Movements](https://mlsanity.com/paper/2512.17985/) (2025)

## Abstract (arXiv)

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on adding additional topological structures, ignoring the influence of functional attributes on regional traffic patterns. Different from the existing works, we propose a novel module named POI-MetaBlock, which utilizes the functionality of each region (represented by Point of Interest distribution) as metadata to further mine different traffic characteristics in areas with different functions. Specifically, the proposed POI-MetaBlock employs a self-attention architecture and incorporates POI and time information to generate dynamic attention parameters for each region, which enables the model to fit different traffic patterns of various areas at different times. Furthermore, our lightweight POI-MetaBlock can be easily integrated into conventional traffic flow prediction models. Extensive experiments demonstrate that our module significantly improves the performance of traffic flow prediction and outperforms state-of-the-art methods that use metadata.